pierreqi/r_code_50k · Datasets at Hugging Face

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b61d15eb9c927bf95c8f9b9be2af9ce3cd60b901

/thurs_practice.R

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alturkaa/soc133

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refs/heads/master

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2019-08-01T18:54:18

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thurs_practice.R

library(tidyverse) # run just this line (ctrl-Enter) # import the data file and assign it to an object called survey. Use the read_csv function. survey <- read_csv("class_survey.csv") # What's the median population of the counties you all live in? # (no code required) Why is there such a big difference between the mean and the median? # How many of you discuss politics with your closest contacts almost all of the time or most of the time? Use both the table and count functions. # Create a new variable that gives you the percent of people you all talk politics with who have a similar political affiliation. Use mutate. Be sure to overwrite the dataframe. I've started it for you. survey <- survey %>% mutate() # Create a table that shows the percent of people who wrote to Congress by how interested they are in politics. Use group_by, then summarize. # What is the mean political heterogeneity index (2000-2016) (variable name is phi_00_16) broken down by whether someone thinks other people are generally trustworthy? Use group_by and summarize. # What is the mean political heterogeneity index (2000-2016) (variable name is phi_00_16) broken down by whether someone thinks voting is a duty or a choice (variable name is duty_choice)? Use group_by and summarize. # plot a bar graph of political interest # draw a density plot of the average commute of all the counties # plot a two-way chart of political interest and contribution (variable name is contrib) # draw a boxplot of the relationship between duty_choice and phi_00_16 # draw something that plots the relationship between income inequality (gini) and phi_00_16 # Bonus: What is the political heterogeneity index (2000-2016) of counties that Trump won in 2016? What is it for counties that Clinton won? # Bonus: Create a new variable that will tell you whether a county became more heterogenous or homogenous between 1980-1996 and 2000-2016. Be sure to overwrite the original dataframe or, if you prefer, save it as a new name. # How many counties became more homogenous? How many became more heterogenous? # Bonus: draw a boxplot of anything you're interested in, with a title and good labels # Bonus: draw a scatterplot (showing points and a line) of something you're interested in, with a title and good labels

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/man/trait_match.Rd

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rogini98/traitmatch

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refs/heads/master

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trait_match.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trait_match.R \docType{package} \name{trait_match} \alias{trait_match} \alias{trait_match-package} \title{trait_match} \description{ Run models to analyze trait matching in networks. }

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/outcome_measurement/malaria/cod/prep_data.R

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refs/heads/develop

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prep_data.R

# ---------------------------------------------- # function to prep the DRC PNLP data prep_data <- function(dataSheet, sheetname, index){ # column names # ---------------------------------------------- columnNames2016 <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "suspectedMalaria_under5", "suspectedMalaria_5andOlder", "suspectedMalaria_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "mildMalariaTreated_under5", "mildMalariaTreated_5andOlder", "mildMalariaTreated_pregnantWomen", "severeMalariaTreated_under5", "severeMalariaTreated_5andOlder", "severeMalariaTreated_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "ANC_2nd", "ANC_3rd", "ANC_4th", "SP_1st", "SP_2nd","SP_3rd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "VAR_0to11mos", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "ArtLum_received", "ArtLum_used", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "stockOut_ASAQ_inj", "stockOut_RDT", "stockOut_artLum", "smearTest_completedUnder5", "smearTest_completed5andOlder", "smearTest_positiveUnder5", "smearTest_positive5andOlder", "RDT_received", "RDT_completedUnder5", "RDT_completed5andOlder", "RDT_positiveUnder5", "RDT_positive5andOlder", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected", "healthFacilities_total", "healthFacilities_numReported", "healthFacilities_numReportedWithinDeadline", "hzTeam_supervisors_numPlanned", "hzTeam_supervisors_numActual", "hzTeam_employees_numPlanned", "hzTeam_employees_numActual", "awarenessTrainings_numPlanned", "awarenessTrainings_numActual", "SSC_fevers", "SSC_RDT_completed", "SSC_RDT_positive", "SSC_ACT", "SSC_casesReferred", "SSC_casesCrossReferred") columnNames2015 <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "suspectedMalaria_under5", "suspectedMalaria_5andOlder", "suspectedMalaria_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "mildMalariaTreated_under5", "mildMalariaTreated_5andOlder", "mildMalariaTreated_pregnantWomen", "severeMalariaTreated_under5", "severeMalariaTreated_5andOlder", "severeMalariaTreated_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "ANC_2nd", "ANC_3rd", "ANC_4th", "SP_1st", "SP_2nd","SP_3rd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "VAR_0to11mos", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "ArtLum_received", "ArtLum_used", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "stockOut_ASAQ_inj", "stockOut_RDT", "stockOut_artLum", "smearTest_completedUnder5", "smearTest_completed5andOlder", "smearTest_positiveUnder5", "smearTest_positive5andOlder", "RDT_received", "RDT_completedUnder5", "RDT_completed5andOlder", "RDT_positiveUnder5", "RDT_positive5andOlder", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected", "healthFacilities_total", "healthFacilities_numReported", "healthFacilities_numReportedWithinDeadline", "hzTeam_supervisors_numPlanned", "hzTeam_supervisors_numActual", "awarenessTrainings_numPlanned", "awarenessTrainings_numActual", "SSC_fevers", "SSC_RDT_completed", "SSC_RDT_positive", "SSC_ACT", "SSC_casesReferred", "SSC_casesCrossReferred") columnNamesComplete <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "suspectedMalaria_under5", "suspectedMalaria_5andOlder", "suspectedMalaria_pregnantWomen", "presumedMalaria_under5", "presumedMalaria_5andOlder", "presumedMalaria_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "mildMalariaTreated_under5", "mildMalariaTreated_5andOlder", "mildMalariaTreated_pregnantWomen", "severeMalariaTreated_under5", "severeMalariaTreated_5andOlder", "severeMalariaTreated_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "ANC_2nd", "ANC_3rd", "ANC_4th", "SP_1st", "SP_2nd","SP_3rd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "VAR_0to11mos", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "ArtLum_received", "ArtLum_used", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "stockOut_ASAQ_inj", "stockOut_RDT", "stockOut_artLum", "smearTest_completedUnder5", "smearTest_completed5andOlder", "smearTest_positiveUnder5", "smearTest_positive5andOlder", "RDT_received", "RDT_completedUnder5", "RDT_completed5andOlder", "RDT_positiveUnder5", "RDT_positive5andOlder", "peopleTested_under5", "peopleTested_5andOlder", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected", "healthFacilities_total", "healthFacilities_numReported", "healthFacilities_numReportedWithinDeadline", "hzTeam_supervisors_numPlanned", "hzTeam_supervisors_numActual", "hzTeam_employees_numPlanned", "hzTeam_employees_numActual", "awarenessTrainings_numPlanned", "awarenessTrainings_numActual", "SSC_fevers_under5", "SSC_fevers_5andOlder", "SSC_RDT_completedUnder5", "SSC_RDT_completed5andOlder", "SSC_RDT_positiveUnder5", "SSC_RDT_positive5andOlder", "SSC_ACT_under5", "SSC_ACT_5andOlder", "SSC_casesReferred_under5", "SSC_casesReferred_5andOlder", "SSC_casesCrossReferred_under5", "SSC_casesCrossReferred_5andOlder") columnNames2014 <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "suspectedMalaria_under5", "suspectedMalaria_5andOlder", "suspectedMalaria_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "mildMalariaTreated_under5", "mildMalariaTreated_5andOlder", "mildMalariaTreated_pregnantWomen", "severeMalariaTreated_under5", "severeMalariaTreated_5andOlder", "severeMalariaTreated_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "SP_1st", "SP_2nd","SP_3rd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "stockOut_ASAQ_inj", "smearTest_completed", "smearTest_positive", "thinSmearTest", "RDT_received", "RDT_completed", "RDT_positive", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected", "healthFacilities_total", "healthFacilities_numReported", "hzTeam_supervisors_numPlanned", "hzTeam_supervisors_numActual", "awarenessTrainings_numPlanned", "awarenessTrainings_numActual" ) columnNames2011to2013 <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "SP_1st", "SP_2nd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "smearTest_completed", "smearTest_positive", "RDT_received", "RDT_completed", "RDT_positive", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected", "healthFacilities_total", "healthFacilities_numReported" ) columnNames2010 <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "SP_1st", "SP_2nd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "smearTest_completed", "smearTest_positive", "RDT_received", "RDT_completed", "RDT_positive", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected", "healthFacilities_total", "healthFacilities_numReported") columnNames2010KINKOR <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "population", "quarter", "month", "totalCasesAllDiseases_under5", "totalCasesAllDiseases_5andOlder", "totalCasesAllDiseases_pregnantWomen", "newCasesMalariaMild_under5", "newCasesMalariaMild_5andOlder", "newCasesMalariaMild_pregnantWomen", "totalHospAllDiseases_under5", "totalHospAllDiseases_5andOlder", "totalHospAllDiseases_pregnantWomen", "newCasesMalariaSevere_under5", "newCasesMalariaSevere_5andOlder", "newCasesMalariaSevere_pregnantWomen", "totalDeathsAllDiseases_under5", "totalDeathsAllDiseases_5andOlder", "totalDeathsAllDiseases_pregnantWomen", "malariaDeaths_under5", "malariaDeaths_5andOlder", "malariaDeaths_pregnantWomen", "ANC_1st", "SP_1st", "SP_2nd", "ITN_received", "ITN_distAtANC", "ITN_distAtPreschool", "ASAQ_received_2to11mos", "ASAQ_received_1to5yrs", "ASAQ_received_6to13yrs", "ASAQ_received_14yrsAndOlder", "ASAQ_used_2to11mos", "ASAQ_used_1to5yrs", "ASAQ_used_6to13yrs", "ASAQ_used_14yrsAndOlder", "ASAQ_used_total", "stockOut_SP", "stockOut_ASAQ_2to11mos", "stockOut_ASAQ_1to5yrs", "stockOut_ASAQ_6to13yrs", "stockOut_ASAQ_14yrsAndOlder", "stockOut_qui_pill", "stockOut_qui_inj", "smearTest_completed", "smearTest_positive", "RDT_received", "RDT_completed", "RDT_positive", "PMA_ASAQ", "PMA_TPI", "PMA_ITN", "PMA_complete", "reports_received", "reports_expected") # ---------------------------------------------- # fix various issues in spelling/typos or extra columns in the data sheet: if ( PNLP_files$year[index] == 2015 & sheetname == "NK" ) { dataSheet <- dataSheet[ , -c("X__71", "X__72", "X__73") ] } if ( PNLP_files$year[index] == 2010 & sheetname == "BC" ) { dataSheet <- dataSheet[ , -c("X__62") ] } if ( PNLP_files$year[index] == 2010 ) { dataSheet <- dataSheet[ , -c("X__14") ] } if ( PNLP_files$year[index] == 2011 & sheetname != "OR" ) { dataSheet <- dataSheet[ , -c("X__14") ] } if ( PNLP_files$year[index] == 2011 & sheetname == "OR" ) { dataSheet <- dataSheet[ , -c("X__11", "X__15", "X__25", "X__39") ] } if ((PNLP_files$year[index] == 2014 | PNLP_files$year[index] == 2013 | PNLP_files$year[index] == 2012 | PNLP_files$year[index] == 2011) & sheetname == "BC") { #dataSheet <- dataSheet[,names(dataSheet)[-length(names(dataSheet))], with=F] dataSheet <- dataSheet[, -ncol(dataSheet), with=F ] } if ( PNLP_files$year[index] == 2016 & sheetname == "KIN") { dataSheet <- dataSheet[ , -c("X__72", "X__73", "X__74") ] } if (PNLP_files$year[index] == 2010 & sheetname == "BDD") { #dataSheet <- dataSheet[,names(dataSheet)[-length(names(dataSheet))], with=F] dataSheet <- dataSheet[ , !apply( dt , 2 , function(x) all(is.na(x))), with=F ] dataSheet <- dataSheet[, -ncol(dataSheet), with=F ] } if ((PNLP_files$year[index] == 2011) & sheetname == "BDD") { #dataSheet <- dataSheet[,names(dataSheet)[-length(names(dataSheet))], with=F] dataSheet <- dataSheet[ , !apply( dt , 2 , function(x) all(is.na(x))), with=F ] dataSheet <- dataSheet[, -ncol(dataSheet), with=F ] dataSheet <- dataSheet[, -ncol(dataSheet), with=F ] } # set column names, depending on differences in years and/or sheets if ( PNLP_files$year[index] == 2014 ) { columnNames <- columnNames2014 } else if (PNLP_files$year[index] < 2014 & PNLP_files$year[index] != 2010) { columnNames <- columnNames2011to2013 } else if (PNLP_files$year[index] == 2010 & sheetname != "KIN" & sheetname != "KOR" ) { columnNames <- columnNames2010 } else if (PNLP_files$year[index] == 2016) { columnNames <- columnNames2016 } else if (PNLP_files$year[index] == 2015) { columnNames <- columnNames2015 } else if (PNLP_files$year[index] == 2010 & ( sheetname == "KIN" | sheetname == "KOR")) { columnNames <- columnNames2010KINKOR } else { columnNames <- columnNamesComplete } names(dataSheet) <- columnNames if (PNLP_files$year[index] == 2012 & sheetname == "EQ"){ dataSheet <- dataSheet[(totalCasesAllDiseases_under5=="971" & totalCasesAllDiseases_5andOlder=="586" & totalCasesAllDiseases_pregnantWomen=="99"), province:="Equateur"] dataSheet <- dataSheet[(totalCasesAllDiseases_under5=="971" & totalCasesAllDiseases_5andOlder=="586" & totalCasesAllDiseases_pregnantWomen=="99"), dps:="Sud Uban"] dataSheet <- dataSheet[(totalCasesAllDiseases_under5=="971" & totalCasesAllDiseases_5andOlder=="586" & totalCasesAllDiseases_pregnantWomen=="99"), health_zone:="Libenge"] dataSheet <- dataSheet[(totalCasesAllDiseases_under5=="971" & totalCasesAllDiseases_5andOlder=="586" & totalCasesAllDiseases_pregnantWomen=="99"), month:= "Janvier"] dataSheet <- dataSheet[(totalCasesAllDiseases_under5=="977" & totalCasesAllDiseases_5andOlder=="816" & totalCasesAllDiseases_pregnantWomen=="242"), month:= "Janvier"] dataSheet <- dataSheet[(totalCasesAllDiseases_under5=="977" & totalCasesAllDiseases_5andOlder=="816" & totalCasesAllDiseases_pregnantWomen=="242"), health_zone:="Mawuya"] dataSheet <- dataSheet[(health_zone=="Mawuya" & !is.na(population)), health_zone:=NA] dataSheet <- dataSheet[!is.na(health_zone)] } # add a column for the "year" to keep track of this variable as we add dataSheets to this one dataSheet$year <- PNLP_files$year[index] # ---------------------------------------------- # Get rid of rows you don't need- "subset" # delete rows where the month column is NA (totals rows or any trailing rows) dataSheet <- dataSheet[!is.na(month)] dataSheet <- dataSheet[!month==0] # clean "Province" column in BDD datasheet for 2016 and 2015 because # it has some missing/"0" values that should be "BDD" - doesn't work if (sheetname == "BDD"){ dataSheet <- dataSheet[province==0, province := sheetname] dataSheet <- dataSheet[is.na(province), province := sheetname] } if (sheetname == "KOR"){ dataSheet <- dataSheet[province==0, province := "K.Or"] dataSheet <- dataSheet[is.na(province), province := "K.Or"] } if (sheetname == "SK"){ dataSheet <- dataSheet[province==0, province := "SK"] dataSheet <- dataSheet[is.na(province), province := "SK"] } # delete first row if it's first value is "NA" or "PROVINCE" as a way to # only delete those unnecessary rows, and not any others accidentally - these # were the column headers in the original datasheet in excel. dataSheet <- dataSheet[!province %in% c('PROVINCE', 'Province')] # BDD 2016 sheet has total row in the middle of the data, the other sheets have it # in the last row of the sheet, sometimes in the first column, sometimes in the second; # sometimes as "Total" and sometimes "TOTAL" dataSheet <- dataSheet[!grepl(("TOTAL"), (dataSheet$province)),] dataSheet <- dataSheet[!grepl(("Total"), (dataSheet$province)),] dataSheet <- dataSheet[!grepl(("total"), (dataSheet$province)),] dataSheet <- dataSheet[!grepl(("TOTAL"), (dataSheet$dps)),] dataSheet <- dataSheet[!grepl(("Total"), (dataSheet$dps)),] dataSheet <- dataSheet[!grepl(("total"), (dataSheet$province)),] # ---------------------------------------------- # translate french to numeric version of month Janvier=1 # dataSheet[month=='Janvier', month:="01"] # grepl() to make sure that any that may have trailing white space are also changed dataSheet[grepl("Janvier", month), month:="01"] dataSheet[grepl("Février", month), month:="02"] dataSheet[grepl("Mars", month), month:="03"] dataSheet[grepl("Avril", month), month:="04"] dataSheet[grepl("Mai", month), month:="05"] dataSheet[grepl("Juin", month), month:="06"] dataSheet[grepl("Juillet", month), month:="07"] dataSheet[grepl("Août", month), month:="08"] dataSheet[grepl("Septembre", month), month:="09"] dataSheet[grepl("Octobre", month), month:="10"] dataSheet[grepl("Novembre", month), month:="11"] dataSheet[grepl("Décembre", month), month:="12"] dataSheet[grepl("janvier", month), month:="01"] dataSheet[grepl("février", month), month:="02"] dataSheet[grepl("mars", month), month:="03"] dataSheet[grepl("avril", month), month:="04"] dataSheet[grepl("mai", month), month:="05"] dataSheet[grepl("juin", month), month:="06"] dataSheet[grepl("juillet", month), month:="07"] dataSheet[grepl("août", month), month:="08"] dataSheet[grepl("septembre", month), month:="09"] dataSheet[grepl("octobre", month), month:="10"] dataSheet[grepl("novembre", month), month:="11"] dataSheet[grepl("décembre", month), month:="12"] # accounting for spelling mistakes/typos/other variations dataSheet[grepl("fevrier", month), month:="02"] dataSheet[grepl("Fevrier", month), month:="02"] dataSheet[grepl("JUIN", month), month:="06"] dataSheet[grepl("Aout", month), month:="08"] dataSheet[grepl("Septembr", month), month:="09"] dataSheet[grepl("Decembre", month), month:="12"] # make string version of the date dataSheet[, stringdate:=paste('01', month, year, sep='/')] # combine year and month into one variable dataSheet[, date:=as.Date(stringdate, "%d/%m/%Y")] # make names of health zones consistent (change abbreviatons to full name in select cases) if (PNLP_files$year[index] == 2017 & sheetname == "KOR"){ dataSheet <- dataSheet[(health_zone=="5" & month=="03" & totalCasesAllDiseases_under5=="3297"), health_zone:= "Kole"] } if (PNLP_files$year[index] == 2015 & sheetname == "EQ"){ dataSheet <- dataSheet[(health_zone=="Libenge" & month=="01" & totalCasesAllDiseases_under5=="1375"), health_zone:= "Mawuya"] } if (PNLP_files$year[index] == 2017 & sheetname == "EQ"){ dataSheet <- dataSheet[(health_zone=="Libenge" & month=="01" & totalCasesAllDiseases_under5=="1838"), health_zone:= "Mawuya"] } if (PNLP_files$year[index] == 2016 & sheetname == "EQ"){ dataSheet <- dataSheet[(health_zone=="Libenge" & month=="01" & totalCasesAllDiseases_under5=="2213"), health_zone:= "Mawuya"] } if (PNLP_files$year[index] == 2014 & sheetname == "EQ"){ dataSheet <- dataSheet[(health_zone=="Libenge" & month=="01" & totalCasesAllDiseases_under5=="754"), health_zone:= "Mawuya"] } if (PNLP_files$year[index] == 2013 & sheetname == "EQ"){ dataSheet <- dataSheet[(health_zone=="Libenge" & month=="01" & totalCasesAllDiseases_under5=="1628"), health_zone:= "Mawuya"] } if ((PNLP_files$year[index] == 2014 | PNLP_files$year[index] == 2015 | PNLP_files$year[index] == 2016 | PNLP_files$year[index] == 2017) & sheetname == "KAT"){ dataSheet <- dataSheet[health_zone=="Mutshat", health_zone:= "Mutshatsha"] dataSheet <- dataSheet[health_zone=="Malem Nk", health_zone:= "Malemba Nkulu"] } if (PNLP_files$year[index] == 2013 & sheetname == "BDD"){ dataSheet <- dataSheet[health_zone=="Koshiba", health_zone:= "Koshibanda"] } if ((PNLP_files$year[index] == 2013 | PNLP_files$year[index] == 2012 )& sheetname == "KOR"){ dataSheet <- dataSheet[health_zone=="Mbuji May", health_zone:= "Bimpemba"] } if ((PNLP_files$year[index] == 2011 | PNLP_files$year[index] == 2010)& sheetname == "BDD"){ dataSheet <- dataSheet[health_zone=="KIKWITS", health_zone:= "Kikwit S"] } if ((PNLP_files$year[index] == 2010)& sheetname == "SK"){ dataSheet <- dataSheet[!is.na(health_zone)] } # there are still some added rows that happen to have something in the month column but are missing data everywhere else dataSheet <- dataSheet[!is.na(province)] dataSheet <- dataSheet[health_zone=="Kabond D", health_zone:= "Kabond Dianda"] dataSheet <- dataSheet[health_zone=="Malem Nk", health_zone:= "Malemba Nkulu"] dataSheet <- dataSheet[health_zone=="Mutshat", health_zone:= "Mutshatsha"] dataSheet <- dataSheet[health_zone=="Kilela B", health_zone:= "Kilela Balanda"] dataSheet <- dataSheet[health_zone=="Mufunga", health_zone:= "Mufunga sampwe"] dataSheet <- dataSheet[health_zone=="Kafakumb", health_zone:= "Kafakumba"] dataSheet <- dataSheet[health_zone=="Kamalond", health_zone:= "Kamalondo"] dataSheet <- dataSheet[health_zone=="Kampem", health_zone:= "Kampemba"] dataSheet <- dataSheet[health_zone=="Tshamile", health_zone:= "Tshamilemba"] dataSheet <- dataSheet[health_zone=="Lshi", health_zone:= "Lubumbashi"] dataSheet <- dataSheet[health_zone=="Mumbund", health_zone:= "Mumbunda"] dataSheet <- dataSheet[health_zone=="Fungurum", health_zone:= "Fungurume"] dataSheet$health_zone <- tolower(dataSheet$health_zone) dataSheet[health_zone=="omendjadi", health_zone := "omondjadi"] dataSheet[health_zone=="kiroshe", health_zone := "kirotshe"] dataSheet[health_zone=="boma man", health_zone := "boma mangbetu"] dataSheet[health_zone=="mutshat", health_zone := "mutshatsha"] dataSheet[health_zone=="mumbund", health_zone := "mumbunda"] dataSheet[health_zone=="fungurum", health_zone := "fungurume"] dataSheet[health_zone=="yasa", health_zone := "yasa-bonga"] dataSheet[health_zone=="malem nk", health_zone := "malemba nkulu"] dataSheet[health_zone=="kampem", health_zone := "kampemba"] dataSheet[health_zone=="kamalond", health_zone := "kamalondo"] dataSheet[health_zone=="pay", health_zone := "pay kongila"] dataSheet[health_zone=="kafakumb", health_zone := "kafakumba"] dataSheet[health_zone=="tshamile", health_zone := "tshamilemba"] dataSheet[health_zone=="ntandem", health_zone := "ntandembele"] dataSheet[health_zone=="masi", health_zone := "masimanimba"] dataSheet[health_zone=="koshiba", health_zone := "koshibanda"] dataSheet[health_zone=="djalo djek", health_zone := "djalo djeka"] dataSheet[health_zone=="ludimbi l", health_zone := "ludimbi lukula"] dataSheet[health_zone=="mwela l", health_zone := "mwela lembwa"] dataSheet[health_zone=="bena le", health_zone := "bena leka"] dataSheet[health_zone=="vanga ket", health_zone := "vanga kete"] dataSheet[health_zone=="bomineng", health_zone := "bominenge"] dataSheet[health_zone=="bogosenu", health_zone := "bogosenusebea"] dataSheet[health_zone=="bwamand", health_zone := "bwamanda"] dataSheet[health_zone=="banga lu", health_zone := "banga lubaka"] dataSheet[health_zone=="bosomanz", health_zone := "bosomanzi"] dataSheet[health_zone=="bosomond", health_zone := "bosomondanda"] dataSheet[health_zone=="bonganda", health_zone := "bongandanganda"] dataSheet[health_zone=="lilanga b", health_zone := "lilanga bobanga"] dataSheet[health_zone=="mondomb", health_zone := "mondombe"] dataSheet[health_zone=="tshitshim", health_zone := "tshitshimbi"] dataSheet[health_zone=="basankus", health_zone := "basankusu"] dataSheet[health_zone=="mobayi m", health_zone := "mobayi mbongo"] dataSheet[health_zone=="kabond d", health_zone := "kabond dianda"] dataSheet[health_zone=="kilela b", health_zone := "kilela balanda"] dataSheet[health_zone=="ndjoko m", health_zone := "ndjoko mpunda"] dataSheet[health_zone=="benatshia", health_zone := "benatshiadi"] dataSheet[health_zone=="tshudi lo", health_zone := "tshudi loto"] dataSheet[health_zone=="pania mut", health_zone := "pania mutombo"] dataSheet[health_zone=="ndjoko mp", health_zone := "ndjoko mpunda"] dataSheet[health_zone=="kalonda e", health_zone := "kalonda est"] dataSheet[health_zone=="kata k", health_zone := "kata kokombe"] dataSheet[health_zone=="lshi", health_zone := "lubumbashi"] dataSheet[health_zone=="bdd", health_zone := "bandundu"] dataSheet[health_zone=="kikwit n", health_zone := "kikwit nord"] dataSheet[health_zone=="kikwit s", health_zone := "kikwit sud"] dataSheet[health_zone=="kasongo l", health_zone := "kasongo lunda"] dataSheet[health_zone=="popoka", health_zone := "popokabaka"] dataSheet[health_zone=="kanda k", health_zone := "kanda kanda"] dataSheet[health_zone=="muene d", health_zone := "muene ditu"] dataSheet[health_zone=="wembo n", health_zone := "wembo nyama"] dataSheet[health_zone=="bena dib", health_zone := "bena dibele"] dataSheet[health_zone=="wamba l", health_zone := "wamba luadi"] dataSheet[health_zone=="kabeya", health_zone := "kabeya kamwanga"] dataSheet[health_zone=="mampoko", health_zone := "lolanga mampoko"] dataSheet[health_zone=="mufunga", health_zone := "mufunga sampwe"] dataSheet[health_zone=="wembo nyana", health_zone := "wembo nyama"] dataSheet[health_zone=="kamonya", health_zone := "kamonia"] dataSheet[health_zone=="kitangwa", health_zone := "kitangua"] # ---------------------------------------------- # Return current data sheet return(dataSheet) # ---------------------------------------------- } # ---------------------------------------------- # currentSheet[health_zone=="Mbuji May"| health_zone== "Bimpemba", c(1:9)]

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/R/seqtest.R

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lnalborczyk/ESTER

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2021-01-11T17:03:10.560357

2018-05-19T08:57:35

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seqtest.R

#' Sequential testing with evidence ratios #' #' Computes sequential evidence ratios, either based on the AIC, BIC, WAIC, or LOOIC. #' Supported models currently include \code{lm}, \code{merMod}, or \code{brmsfit} models. #' When data involve repeated measures (and so multiple lines per subject), #' a column indicating the subject "id" should be provided to the \code{id} argument. #' If nothing is passed to the \code{id} argument, \code{seqtest} will suppose #' that there is only one observation (i.e., one line) per subject. #' #' @param ic Indicates whether to use the aic or the bic. #' @param mod1 A model of class \code{lm} or \code{lmerMod}. #' @param mod2 A model of class \code{lm} or \code{lmerMod} (of the same class of mod1). #' @param nmin Minimum sample size from which start to compute sequential evidence ratios. #' @param id If applicable (i.e., repeated measures), name of the "id" column of your #' dataframe, in character string. #' @param boundary The Evidence Ratio (or its reciprocal) at which #' the run is stopped as well #' @param blind If true, the function only returns a "continue or stop" message #' @param nsims Number of permutation samples to evaluate (is ignored if blind = TRUE) #' #' @importFrom stats family formula lm update #' @importFrom magrittr %>% set_names #' @importFrom lme4 lmer glmer #' @importFrom rlang f_lhs #' @import ggplot2 #' @import dplyr #' @import utils #' @import brms #' #' @examples #' \dontrun{ #' # A first simple example #' data(mtcars) #' mod1 <- lm(mpg ~ cyl, mtcars) #' mod2 <- lm(mpg ~ cyl + disp, mtcars) #' seqtest(ic = aic, mod1, mod2, nmin = 10) #' #' # Plotting the results #' seqtest(ic = aic, mod1, mod2, nmin = 10) %>% plot #' #' # Example with 10 permutation samples #' seqtest(ic = aic, mod1, mod2, nmin = 10, nsims = 10) #' #' # Example with blinding #' seqtest(ic = aic, mod1, mod2, nmin = 10, boundary = 10, blind = TRUE) #' #' # Example with repeated measures #' library(lme4) #' data(sleepstudy) #' mod1 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy) #' mod2 <- lmer(Reaction ~ Days + I(Days^2) + (1|Subject), sleepstudy) #' seqtest(ic = aic, mod1, mod2, nmin = 10, id = "Subject", nsims = 10) #' #' # Example with brmsfit models #' library(brms) #' mod1 <- brm(Reaction ~ Days + (1|Subject), sleepstudy) #' mod2 <- brm(Reaction ~ Days + I(Days^2) + (1|Subject), sleepstudy) #' seqtest(ic = WAIC, mod1, mod2, nmin = 10, id = "Subject") #' } #' #' @author Ladislas Nalborczyk <\email{ladislas.nalborczyk@@gmail.com}> #' #' @seealso \code{\link{ictab}} #' #' @export seqtest <- function( ic = aic, mod1, mod2, nmin = 10, id = NULL, boundary = Inf, blind = FALSE, nsims = NULL) { if (!class(mod1) == class(mod2) ) { stop("Error: mod1 and mod2 have to be of the same class") } if (nmin < 10) { warning("nmin should usually be set above 10...") } if (class(mod1) == "lm") { data <- data.frame(eval(mod1$call[["data"]], envir = parent.frame() ) ) } if (class(mod1) == "glmerMod" | class(mod1) == "lmerMod") { data <- data.frame(eval(mod1@call$data, envir = parent.frame() ) ) } if (class(mod1) == "brmsfit") { data <- get(mod1$data.name) } if (is.null(id) == TRUE) { id <- deparse(f_lhs(formula(mod1) ) ) nobs <- 1 data$ppt <- rep(seq(1, length(data[, id]), 1), each = nobs) } else { count <- data.frame(table(data[, id]) ) nobs <- max(count$Freq) a <- as.vector(count$Var1[count$Freq < nobs]) data <- data[order(data[, id]), ] data$ppt <- rep(seq(1, length(unique(data[, id]) ), 1), each = nobs) if (length(a) > 0) { for (i in 1:length(a) ) { data <- data[!data[, id] == as.numeric(a[i]), ] } warning("Different numbers of observation by subject. Subjects with less than max(nobs) have been removed.") } } startrow <- min(which(as.numeric(as.character(data$ppt) ) == nmin) ) endrow <- nrow(data) for (i in seq(startrow, endrow, nobs) ) { maxrow <- i - 1 + nobs if ( (class(mod1) == "glmerMod") ) { mod1 <- glmer(formula(mod1), family = family(mod1)$family, data[1:maxrow, ]) mod2 <- glmer(formula(mod2), family = family(mod2)$family, data[1:maxrow, ]) } if ( (class(mod1) == "lmerMod") ) { mod1 <- lmer(formula(mod1), REML = FALSE, data[1:maxrow, ]) mod2 <- lmer(formula(mod2), REML = FALSE, data[1:maxrow, ]) } if ( (class(mod1) == "lm") ) { mod1 <- lm(formula(mod1), data[1:maxrow, ]) mod2 <- lm(formula(mod2), data[1:maxrow, ]) } if ( (class(mod1) == "brmsfit") ) { mod1 <- update( mod1, newdata = data[1:maxrow, ], recompile = FALSE, refresh = 0 ) mod2 <- update( mod2, newdata = data[1:maxrow, ], recompile = FALSE, refresh = 0 ) } tabtab <- ictab(list(mod1 = mod1, mod2 = mod2), ic) temp_er <- data.frame(cbind(data$ppt[i], tabtab$ic_wt[tabtab$modnames == "mod2"] / tabtab$ic_wt[tabtab$modnames == "mod1"]) ) if (!exists("er") ) er <- temp_er else er <- rbind(er, temp_er) rm(temp_er) } colnames(er) <- c("ppt", "ER") if (blind == TRUE) { if (tail(abs(log(er$ER) ), 1) >= log(boundary) ) { return("stop the recruitment") } else { return("continue the recruitment") } } erb <- er %>% mutate(ERi = rep("er", max(.$ppt) - nmin + 1) ) %>% select_(~ERi, ~ppt, ~ER) if (!is.null(nsims) ) { for (i in 1:nsims) { data_temp <- data[sample(nrow(data), replace = FALSE), ] if (nobs > 1) { data_temp <- data_temp[order(factor(data_temp$ppt, levels = unique(data_temp$ppt) ) ), ] data_temp$ppt <- data$ppt } else { data_temp$ppt <- data$ppt } for (j in seq(startrow, endrow, nobs) ) { maxrow <- j - 1 + nobs if ( (class(mod1) == "glmerMod") ) { mod1 <- glmer(formula(mod1), family = family(mod1)$family, data_temp[1:maxrow, ]) mod2 <- glmer(formula(mod2), family = family(mod2)$family, data_temp[1:maxrow, ]) } if ( (class(mod1) == "lmerMod") ) { mod1 <- lmer(formula(mod1), REML = FALSE, data_temp[1:maxrow, ]) mod2 <- lmer(formula(mod2), REML = FALSE, data_temp[1:maxrow, ]) } if ( (class(mod1) == "lm") ) { mod1 <- lm(formula(mod1), data_temp[1:maxrow, ]) mod2 <- lm(formula(mod2), data_temp[1:maxrow, ]) } if ( (class(mod1) == "brmsfit") ) { mod1 <- update( mod1, newdata = data_temp[1:maxrow, ], recompile = FALSE, refresh = 0 ) mod2 <- update( mod2, newdata = data_temp[1:maxrow, ], recompile = FALSE, refresh = 0 ) } tabtab <- ictab(list(mod1 = mod1, mod2 = mod2), ic) temp_temp_erb <- data.frame( cbind(data_temp$ppt[j], tabtab$ic_wt[tabtab$modnames == "mod2"] / tabtab$ic_wt[tabtab$modnames == "mod1"]) ) if (!exists("temp_erb") ) { temp_erb <- temp_temp_erb } else { temp_erb <- rbind(temp_erb, temp_temp_erb) } rm(temp_temp_erb) } temp_erb <- temp_erb %>% mutate(ERi = rep(paste0("er", i), nrow(.) ) ) %>% select(3, 1, 2) %>% set_names(c("ERi", "ppt", "ER") ) erb <- rbind(erb, temp_erb) rm(temp_erb) set.seed(NULL) } } class(erb) <- c("seqtest", "data.frame") return(erb) } #' @export plot.seqtest <- function(x, ... ) { aes_lines <- sqrt(0.75 / n_distinct(x$ERi) ) ggplot(x, aes_string(x = "ppt", y = "ER", group = "ERi") ) + scale_y_log10() + geom_line(alpha = aes_lines, size = aes_lines) + geom_line( aes_string(x = "ppt", y = "ER", group = NULL), data = x[x$ERi == "er", ], size = 0.75 ) + theme_bw(base_size = 12) + xlab("Sample size") + ylab(expression(Evidence~ ~Ratio~ ~ (ER[10]) ) ) }

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/get_files.R

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Kikzz/WorkWithDataAssignment

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get_files.R

## get_files.R ## 1. download the zip file as data.zip into working directory ## 2. unzip data.zip into working directory ## 3. Remove zip file from HDD ## download files into working directory download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip",method="curl",dest="data.zip") ## unzip the file. This cretates a "UCI HAR Dataset" directory and unzips all files into it. unzip(zipfile="data.zip") ## deletes data.zip from hard drive unlink("data.zip")

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/draft.R

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weslwz/RepData_PeerAssessment1

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refs/heads/master

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draft.R

library(knitr) library(markdown) knit("PA1_template.Rmd") markdownToHTML("PA1_template.md", "PA1_template.html") X <- read.csv("activity.csv") library(plyr) library(ggplot2) A <- ddply(X, "date", summarise, total_steps=sum(steps,na.rm=TRUE), mean_steps=mean(steps,na.rm=TRUE), median_steps=median(steps,na.rm=TRUE)) A$date <- as.Date(A$date) print(A) p <- ggplot(A, aes(x=date,y=total_steps)) + geom_bar(stat="identity",fill="blue") + ggtitle("Steps taken per day") + xlab("Date") + ylab("Number of steps") print(p) B <- ddply(X, "interval", summarise, mean_steps=mean(steps,na.rm=TRUE)) print(B) p <- ggplot(B, aes(x=interval,y=mean_steps)) + geom_line(colour="blue") + ggtitle("Average steps taken per interval") + xlab("Interval") + ylab("Average number of steps") print(p) ind <- which.max(B$mean_steps) B$interval[ind]

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/0415_pdbs/ras/sd_of_net_community.r

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Hongyang449/2016

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2016-12-21T21:04:08

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sd_of_net_community.r

## name: sd_of_net_community.r ## date: 10/31/2016 ## Here I want to examine the sd (variance) of community edges within and between states (gtp vs gdp) library(abind) load("/Users/hyangl/project/ras/results/2016/0415_pdbs/ras/net_ras_cmap_noh.RData") ## structure of net_complete: ## list(nets_ras_gtp_vs_gdp_signif, community_cij_ras_gtp, community_cij_ras_gdp, p_community_cij_ras) ## noh 4.5; calculate the sum of sd of all community edges sum(apply(net_complete[['4.5']][[2]]$raw, 1:2, sd)) # [1] 40.88062 # within gtp sum(apply(net_complete[['4.5']][[3]]$raw, 1:2, sd)) # [1] 41.30724 # within gdp sum(apply(abind(net_complete[['4.5']][[2]]$raw,net_complete[['4.5']][[3]]$raw,along=3), 1:2, sd)) # [1] 50.63161 # combine gtp and gdp - much larger ## noh 6; calculate the sum of sd of all community edges sum(apply(net_complete[['6']][[2]]$raw, 1:2, sd)) # [1] 65.65264 # within gtp sum(apply(net_complete[['6']][[3]]$raw, 1:2, sd)) # [1] 68.9406 # within gdp sum(apply(abind(net_complete[['6']][[2]]$raw,net_complete[['6']][[3]]$raw,along=3), 1:2, sd)) # [1] 81.83473 # combine gtp and gdp - much larger # plot load("/Users/hyangl/project/ras/results/2016/info/layout_2d.RData") layout(matrix(1:2, nrow=1)) sd_gtp <- apply(net_complete[['6']][[2]]$raw, 1:2, sd) sd_gtp[upper.tri(sd_gtp)] <- 0; weight <- sd_gtp[sd_gtp != 0] plot.cna(net_ras_cmap_noh[["6"]]$gtp, layout=layout_ras, w=weight, vertex.label=NA, edge.label=NA, edge.color="gray") mtext("gtp_noh_6", outer=F, line=-5) sd_gdp <- apply(net_complete[['6']][[3]]$raw, 1:2, sd) sd_gdp[upper.tri(sd_gdp)] <- 0; weight <- sd_gdp[sd_gdp != 0] plot.cna(net_ras_cmap_noh[["6"]]$gdp, layout=layout_ras, w=weight, vertex.label=NA, edge.label=NA, edge.color="gray") mtext("gdp_noh_6", outer=F, line=-5) mtext("sd_of_cmap_noh_6", outer=T, line=-3) dev.copy2pdf(file="figures/sd_ras_cmap_noh_6.pdf")

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/plot2.R

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amicarelli/ExData_Plotting1

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2021-01-18T12:09:53.659536

2016-03-22T02:35:34

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plot2.R

## # This script is for the course: exploratory data analysis # The script reads in data from household power consumption # and creates graphs of the data # # # Check to see if the data already exists in the local directory, if not, download data # fileDir <- paste(getwd(),"/data/HPC",sep="") #use this directory for data and plots if(!file.exists(fileDir)){ fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip" download.file(fileUrl,destfile = "HPC.zip") unzip("HPC.zip") file.rename("exdata-data-household_power_consumption",fileDir) file.remove("HPC.zip")} # # Read in data table and convert dates to R date format # hpc <- read.table(paste(fileDir,"/household_power_consumption.txt",sep=""),header=T,sep=";",na.strings = "?") #read data hpc$Date <- as.Date(hpc$Date,format="%d/%m/%Y") #convert date from factor to POSIX date hpc2 <- subset(hpc,Date=="2007-02-01" | Date=="2007-02-02") #select data from specific dates hpc2$DateTime <- strptime(paste(hpc2$Date,hpc2$Time),format = "%Y-%m-%d %H:%M:%S") #create a new column that #includes date and time # # Plot 2 - render plot to screen and to file # par(mfcol=c(1,1)) with(hpc2,plot(DateTime,Global_active_power,type="l",ylab="Global Active Power(kilowats)")) png(filename = paste(fileDir,"/plot2.png",sep=""), width = 480, height = 480) with(hpc2,plot(DateTime,Global_active_power,type="l",ylab="Global Active Power(kilowats)")) dev.off()

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/Lalonde 3-ways.R

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tuantvh/CS112---Causal-Inference

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refs/heads/master

2021-10-25T19:54:45.385703

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Lalonde 3-ways.R

library("Matching") library("stargazer") library("ggplot2") library(randomForest) data <- data("lalonde") #MODELS #PART1 #high school degree and no degree Degree <- lalonde[lalonde$nodegr ==0,] NoDegree <- lalonde[lalonde$nodegr ==1,] #regression model fit.Degree <- lm(re78 ~ treat + age + black + hisp +married +re74 + re75, data = Degree) fit.NoDegree <- lm(re78 ~ treat + age + black + hisp +married +re74 + re75, data = NoDegree) CI.Degree <- confint(fit.Degree) CI.NoDegree <- confint(fit.NoDegree) #output image stargazer(fit.Degree, fit.NoDegree,title="Table 1. Regression Results", type = "html", align=TRUE, column.labels = c("Degree", "No Degree"), dep.var.labels= "Real Earnings in 1978", covariate.labels=c("Treatment","Age", "Black","Hispanic","Married","Real Earnings in 1974", "Real Earnings in 1975"), omit.stat=c("LL","ser","f"), single.row= TRUE, out = "regression.htm") #confidence intervals stargazer(CI.Degree, type = "html", title="Degree", out ="CIDegree.htm") stargazer(CI.NoDegree, type = "html", title="No Degree", out ="CINoDegree.htm") #correlation matrix correlation.matrix <- cor(Degree[,c("treat","age","black", "hisp", "married", "re74", "re75")]) stargazer(correlation.matrix, title="Correlation Matrix Degree", out ="corDegree.htm") correlation.matrix2 <- cor(NoDegree[,c("treat","age","black", "hisp", "married", "re74", "re75")]) stargazer(correlation.matrix, title="Correlation Matrix No Degree", out ="corNoDegree.htm") #PART2 #random forest set.seed(1) #DEGREE rf.Degree <- randomForest(re78 ~ treat + age + black+ hisp+ married + re74 + re75, data = Degree, mtry = 3, importance = TRUE) importance(rf.Degree) varImpPlot(rf.Degree) #predicting the counterfactual treated.Degree <- Degree[Degree$treat == 1,] treated.Degree$treat = 0 #change the treated value for prediction counterfactual.degree <- predict(rf.Degree, newdata = treated.Degree) #average treatment effects mean(treated.Degree$re78) - mean(counterfactual.degree) #bootstrap confidence interval of treatment effects: storage.effect <- NULL for (i in 1:1000) { sample.index <- sample(c(1:nrow(Degree)), nrow(Degree), replace= TRUE) sample <- Degree[sample.index,] rf.sample <- randomForest(re78 ~ treat + age + black+ hisp+ married + re74 + re75, data = sample, mtry = 3, importance = TRUE) #predicting the counterfactual treated.sample <- sample[sample$treat == 1,] treated.sample$treat = 0 #change the treated value for prediction counterfactual.sample <- predict(rf.sample, newdata = treated.sample) #average treatment effects storage.effect[i] <- mean(treated.sample$re78) - mean(counterfactual.sample) } qplot(storage.effect, geom="histogram", main = "Histogram for Estimated Treatment Effect (Degree)", xlab = "Treatment Effect" ) quantile(storage.effect, c(.025, .975)) #NODEGREE rf.NoDegree <- randomForest(re78 ~ treat + age + black+ hisp+ married + re74 + re75, data = NoDegree, mtry = 3, importance = TRUE) importance(rf.NoDegree) varImpPlot(rf.NoDegree) #predicting the counterfactual treated.NoDegree <- NoDegree[NoDegree$treat == 1,] treated.NoDegree$treat = 0 #change the treated value for prediction counterfactual.NoDegree <- predict(rf.NoDegree, newdata = treated.NoDegree) #average treatment effects mean(treated.NoDegree$re78) - mean(counterfactual.NoDegree) #bootstrap confidence interval of treatment effects: storage.effect2 <- NULL for (i in 1:1000) { sample.index <- sample(c(1:nrow(NoDegree)), nrow(NoDegree), replace= TRUE) sample <- NoDegree[sample.index,] rf.sample <- randomForest(re78 ~ treat + age + black+ hisp+ married + re74 + re75, data = sample, mtry = 3, importance = TRUE) #predicting the counterfactual treated.sample <- sample[sample$treat == 1,] treated.sample$treat = 0 #change the treated value for prediction counterfactual.sample <- predict(rf.sample, newdata = treated.sample) #average treatment effects storage.effect2[i] <- mean(treated.sample$re78) - mean(counterfactual.sample) } qplot(storage.effect2, geom="histogram", main = "Histogram for Estimated Treatment Effect (No Degree)", xlab = "Treatment Effect" ) quantile(storage.effect2, c(.025, .975)) #PART 3: #DEGREE #assume the sharp null hypothesis of no treatment test.statistic.degree <- mean(Degree[Degree$treat == 1, ]$re78) - mean(Degree[Degree$treat == 0,]$re78) storage.vector <- NULL for (i in 1:10000) { FishTreat_id <- sample(seq_len(nrow(Degree)), nrow(Degree[Degree$treat==1,])) FishTreat <- Degree[FishTreat_id,] FishControl <- Degree[-FishTreat_id,] storage.vector[i] <- mean(FishTreat$re78) - mean(FishControl$re78) } test.statistic.degree quantile(storage.vector, prob=c(0.975, 0.025)) plot(density(storage.vector)) abline(v = test.statistic.degree, lwd = 2, col = "red") #NODEGREE #assume the sharp null hypothesis of no treatment test.statistic.NoDegree <- mean(NoDegree[NoDegree$treat == 1, ]$re78) - mean(NoDegree[NoDegree$treat == 0,]$re78) storage.vector <- NULL for (i in 1:10000) { FishTreat_id <- sample(seq_len(nrow(NoDegree)), nrow(NoDegree[NoDegree$treat==1,])) FishTreat <- NoDegree[FishTreat_id,] FishControl <- NoDegree[-FishTreat_id,] storage.vector[i] <- mean(FishTreat$re78) - mean(FishControl$re78) } test.statistic.NoDegree quantile(storage.vector, prob=c(0.975, 0.025)) plot(density(storage.vector)) abline(v = test.statistic.NoDegree, lwd = 2, col = "red")

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/R/makeContactMatrix.R

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cdcepi/ACIP-SARS-CoV-2-Vaccine-Modeling

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2023-03-07T13:06:21.763569

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makeContactMatrix.R

#' @title Make contact matrix #' @description Rebins an arbitrary contact matrix to a new group of population ranges. The default behavior is to use the UK POLYMOD data. #' @param ages Vector of ages. Each element represents the upper range of an age range. The lowest bound is presumed to be zero. #' If the oldest age does not reach the end of the population range, an additional element is added to span the full range. #' The final age bracket cannot start after the final bracket of originalContactMatrixAges. #' @param originalContactMatrix Contact matrix to serve as the basis for extrapolation. Defaults to UK POLYMOD. #' This must be a square matrix. #' @param originalContactMatrixAges Age ranges associated with originalContactMatrix. Defaults to UK population in 5-year bins, with #' the final bracket starting at age 70. The format is a data frame with columns representing the age brackets and having two rows: #' AgeStart and AgeEnd. The first column has ageStart of 0. The last column has ageEnd of NA. #' @param originalPopulationFractions Vector of age fractions associated with originalContactMatrixAges. #' Length must equal the dimension of originalContactMatrix. The vector will be normalized, so it can represent population fractions #' or population in each age bin. #' @return A contact matrix. #' @author Jason Asher <jason.m.asher@gmail.com> #' @export makeContactMatrix <- function( ages, originalContactMatrix = flumodels_data$POLYMOD.matrix, originalContactMatrixAges = flumodels_data$POLYMOD.age.ranges, originalPopulationFractions = flumodels_data$population.fractions.US) { if (is.unsorted(ages)) stop("Ages must be increasing order") if (sum(ages - round(ages)) != 0 | min(ages) < 1) stop("Ages must be positive integers") if (ages[length(ages)] > originalContactMatrixAges[1, length(originalContactMatrixAges)]) stop(paste("Final age range is older than the maximum age possible based upon originalContactMatrixAges:", originalContactMatrixAges[1, length(originalContactMatrixAges)])) # Should check here to see if the matrix is one that we've determined before. This will help speed things up considerably. if(nrow(originalContactMatrix) != ncol(originalContactMatrix)) stop("originalContactMatrix is not a square matrix") if (nrow(originalContactMatrix) != length(originalContactMatrixAges) || length(originalContactMatrixAges) != length(originalPopulationFractions)) stop("Dimension mismatch between originalContactMatrix, originalContactMatrixAges, and originalPopulationFractions") newAgeRanges <- makeAgeRangesFromInputs(ages) #Setting adjusts whether entered data is normalized by column (default) or row (assuming the original Mossong data has been transposed) byColumn <- TRUE #Calculate the matrix of expected number of contacts between single-year age groups if (byColumn) { matrixOfContacts <- t(apply(originalContactMatrix, 1, function(t){t*originalPopulationFractions})) #Multiply each column by the corresponding population } else { matrixOfContacts <- apply(originalContactMatrix, 2, function(t){t*originalPopulationFractions}) #Multiply each row by the corresponding population } #Symmetrize this matrix of contacts (to remove artifacts from study and to ensure consistency with the population) symmetrizedMatrixOfContacts <- (matrixOfContacts + t(matrixOfContacts))/2 #Re-group the matrix of expected contacts regroupedMatrixOfContacts <- matrix(0, nrow = ncol(newAgeRanges), ncol = ncol(newAgeRanges), dimnames = list(names(newAgeRanges),names(newAgeRanges))) #Initialize with a zero matrix for (newRowIndex in seq_along(newAgeRanges)) { for (newColumnIndex in seq_along(newAgeRanges)){ for (rowIndex in seq_along(originalContactMatrixAges)) { for (columnIndex in seq_along(originalContactMatrixAges)) regroupedMatrixOfContacts[newRowIndex, newColumnIndex] <- regroupedMatrixOfContacts[newRowIndex, newColumnIndex] + (symmetrizedMatrixOfContacts[rowIndex, columnIndex] * getAgeRangeFraction(rowIndex, as.numeric(unlist(newAgeRanges[, newRowIndex])), ages = originalContactMatrixAges) * getAgeRangeFraction(columnIndex, as.numeric(unlist(newAgeRanges[, newColumnIndex])), ages = originalContactMatrixAges)) } } } #debugOutput(regroupedMatrixOfContacts) #Re-normalize the new matrix of expected contacts newPopulation <- apply(newAgeRanges, 2, function(x){getPopulationForAgeRange(ageRange = as.numeric(unlist(x)), ages = originalContactMatrixAges, population = originalPopulationFractions)}) if (byColumn) { newContactMatrix <- t(apply(regroupedMatrixOfContacts, 1, function(t){t/newPopulation})) #Divide each column by the corresponding population } else { newContactMatrix <- apply(regroupedMatrixOfContacts, 2, function(t){t/newPopulation}) #Divide each row by the corresponding population } return(newContactMatrix) } #Returns the population that would be assigned to the given age range c(ageStart, ageEnd) # Ages is POLYMODAgeRanges #Assumes constant interpolation between age groups when subdividing ranges getPopulationForAgeRange <- function(ageRange, ages, population) { ageRangePopulation <- 0 for (columnIndex in seq_along(ages)) { ageRangePopulation <- ageRangePopulation + (getAgeRangeFraction(columnIndex, ageRange, ages) * as.numeric(population[columnIndex])) } ageRangePopulation } #Returns the fraction of the corresponding age range with column index columnIndex that is between ageRange = c(ageStart,ageEnd) # Ages is POLYMODAgeRanges #Warning, cannot subdivide a column whose upper bound is NA - this function will return the whole population in that case getAgeRangeFraction <- function(columnIndex, ageRange, ages) { ageStart <- ageRange[1] ageEnd <- ageRange[2] if (is.na(ages[2, columnIndex])) { if (is.na(ageEnd) || (ages[1, columnIndex] <= ageEnd)) { #Return 1 } else { #Return 0 } } else { overlapEnd <- min(ageEnd, ages[2, columnIndex], na.rm = TRUE) overlapStart <- max(ageStart, ages[1, columnIndex]) yearsInOverlapOfAgeRanges <- max(overlapEnd - overlapStart + 1, 0) #Return yearsInOverlapOfAgeRanges / getAgeRangeLength(columnIndex, ages) } } getAgeRangeLength <- function(index, ages) { ages[2, index] - ages[1, index ] + 1 } # Changes the age ranges provided into what the rebinning module prefers makeAgeRangesFromInputs <- function(ages) { newFrame <- data.frame("col1" = c(AgeStart = 0, AgeEnd = ages[1])) names(newFrame) <- paste0("Age00to", ages[1]) if (length(ages) != 1) { for (currentAge in seq.int(2, length(ages))) { newFrame[, paste0("Age", ages[currentAge-1]+1, "to", ages[currentAge])] <- c(AgeStart = ages[currentAge-1]+1, AgeEnd = ages[currentAge]) } } newFrame[, paste0("Age", ages[length(ages)]+1, "plus")] <- c(AgeStart = ages[currentAge]+1, AgeEnd = NA) return(newFrame) }

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/R/33_stripSP.R

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Epiconcept-Paris/GADMTools

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33_stripSP.R

## --------------------------------------------------------------------------- ## Method : stripSP ## Return : a GADMWrapper ready to use with ggplot2 ## --------------------------------------------------------------------------- stripSP <- function(x, level=NULL) UseMethod("stripSP", x) stripSP.gadm_sp <- function(x, level=NULL) { .level <- x$level if (is.null(level)) { if (x$level == 0) { .name <-"ISO" } else { .name <- sprintf("NAME_%d", x$level) } } else { if (level > x$level || level < 0) { .name <- sprintf("NAME_%d", x$level) } else { .name <- sprintf("NAME_%d", level) .level <- level } } .map <- fortify(x$spdf, region=.name) # ---- Create GADMWrapper object structure(list("basename"=x$basefile, "spdf"=.map, "level"=.level, "stripped" = TRUE), class = "GADMWrapper") } ## =========================================================================== gadm_loadStripped <- function(name, level, basefile='./') { FILENAME = sprintf("STRIP_%s_adm%d.rds", name,level) LOCAL_FILE = sprintf("%s%s", basefile, FILENAME) print(LOCAL_FILE) .map <- readRDS(LOCAL_FILE) if (is.null(.map)) { stop("Error: Enable to read file!") } .map } saveAsStripped <- function(x, fname, name= NULL, basefile = './') UseMethod("saveAsStripped") saveAsStripped.gadm_sp <- function(x, fname, name = NULL, basefile = './') { SP <- x if (x$stripped == FALSE) { SP <- stripSP(x, name) } gadm_saveStripped(SP, fname, basefile) } strippedExists <- function(name, level, basefile = './') { FILENAME = sprintf("STRIP_%s_adm%d.rds", name,level) LOCAL_FILE = sprintf("%s%s", basefile, FILENAME) file.exists(LOCAL_FILE) } gadm_saveStripped <- function(x, fname, basefile = './') { FILENAME = sprintf("STRIP_%s_adm%d.rds", fname,x$level) LOCAL_FILE = sprintf("%s%s", basefile, FILENAME) saveRDS(x, file = LOCAL_FILE) TRUE }

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/man/clust_remove.Rd

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juba/umapscan

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refs/heads/master

2020-12-19T18:33:34.025036

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clust_remove.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/clustering.R \name{clust_remove} \alias{clust_remove} \title{Remove a cluster from an umapscan object} \usage{ clust_remove(us, id, rm_root = FALSE) } \arguments{ \item{us}{umapscan object} \item{id}{id of the cluster to remove} \item{rm_root}{if TRUE, also remove the root cluster node. Otherwise, only remove its children (should not be used directly, only for recursive call).} } \value{ An updated umapscan object. } \description{ If the cluster has children, they will be removed too. } \examples{ library(dplyr) iris_num <- iris \%>\% select_if(is.numeric) us <- new_umapscan(iris_num, n_neighbors = 25, min_dist = 0.1, seed = 1337) us <- clust_compute(us, minPts = 3, eps = 0.5) us <- clust_compute(us, parent = "3" ,minPts = 3, eps = 0.45) clust_remove(us, "3_1") us clust_remove(us, "3") us }

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f_indicator.R

#' Lag function #' @param x A vector to perform lag on #' @param n number of elements to shift. Default is 1. #' @export #' @examples #' f_lag(x = 1:10, n = 1) #' f_lag <- function(x, n = 1) { if (n == 0) { return(x) } id <- 1:n x <- c(x[-id], rep(NA, n)) return(x) } #' This function calculates the slope of a moving average #' @param data data.table. data should be a obj an outcome from f_indicators to ensure MA is present in data. #' @param n Moving average based on n days #' @return The slope of the moving average #' @import data.table #' @importFrom stats coef lm na.omit f_slope <- function(data, n) { id <- NULL data[, id := 1:.N] ma_var <- paste0("MA", n) subdata <- data[, c(ma_var, "id")] subdata <- na.omit(subdata) if (nrow(subdata) < 5) { return(NA) } m <- lm(MA ~ id, subdata) as.numeric(coef(m)[2]) } #' This function calculates the slope of a moving average based on n data points #' @param data data.table. data should be a obj an outcome from f_indicators to ensure MA is present in data. #' @param n number of days used to calculate the slope #' @return data.table object with moving average slope for each day #' @import data.table f_ma_slope <- function(data, n = 10) { m <- nrow(data) data$MA_slope <- rep(NA, nrow(data)) for (i in (n + n):m) { loopdata <- data[(i - n):i, ] data$MA_slope[i] <- f_slope(data = loopdata, n = n) cat("\r", i, "of", m) flush.console() } return(data$MA_slope) } #' This function calculates common indicators such as moving averages and RSI #' @param data a data.table obtained from f_load #' @param n number of days used to calculate the slope of moving average. Default is 10 #' @param m number of days used to calculate moving average. Default is 5 #' @return A data.table #' @export #' @import data.table f_indicator <- function(data, n = 10, m = 5) { Change <- MACD <- NULL data <- data[!is.na(Change), ] ## OSCILLATORS ## Relative Strength Index data$RSI <- TTR::RSI(data$Close) ## Commodity Channel Index (20) data$CCI <- TTR::CCI(HLC = data[, c("High", "Low", "Close")], n = 20) ## Average Directional Index (14) df_adi <- TTR::ADX(HLC = data[, c("High", "Low", "Close")], n = 14) df_adi <- data.frame(df_adi) data$ADI <- df_adi$ADX ## Awesome Oscillator ## Momentum (10) data$momentum <- TTR::momentum(x = data$Close, n = 10) ## MACD Level (12, 26) MACD_res <- data.frame(TTR::MACD(data$Close,Fast = 12, nSlow = 26)) data$MACD <- MACD_res$macd ## Stochastic RSI Fast (3, 3, 14, 14) ## Williams Percent Range (14) data$Williams_pct <- TTR::WPR(HLC = data[, c("High", "Low", "Close")], n = 14) ## Bull Bear Power ## Ultimate Oscillator (7, 14, 28) data$ultimateOscillator <- TTR::ultimateOscillator(HLC = data[, c("High", "Low", "Close")], n = c(7, 14, 28)) ## MOVING AVERAGES ## Exponential Moving Average (5) data$EMA5 <- TTR::EMA(x = data$Close, n = 5) ## Simple Moving Average (5) data$MA5 <- TTR::SMA(x = data$Close, n = 5) ## Exponential Moving Average (10) data$EMA10 <- TTR::EMA(x = data$Close, n = 10) ## Simple Moving Average (10) data$MA10 <- TTR::SMA(x = data$Close, n = 10) ## Exponential Moving Average (20) data$EMA20 <- TTR::EMA(x = data$Close, n = 20) ## Simple Moving Average (20) data$MA20 <- TTR::SMA(x = data$Close, n = 20) ## Exponential Moving Average (30) data$EMA30 <- TTR::EMA(x = data$Close, n = 30) ## Simple Moving Average (30) data$MA30 <- TTR::SMA(x = data$Close, n = 30) ## Exponential Moving Average (50) data$EMA50 <- TTR::EMA(x = data$Close, n = 50) ## Simple Moving Average (50) data$MA50 <- TTR::SMA(x = data$Close, n = 50) ## Exponential Moving Average (100) data$EMA100 <- TTR::EMA(x = data$Close, n = 100) ## Simple Moving Average (100) data$MA100 <- TTR::SMA(x = data$Close, n = 100) ## Exponential Moving Average (200) data$EMA200 <- TTR::EMA(x = data$Close, n = 200) ## Simple Moving Average (200) data$MA200 <- TTR::SMA(x = data$Close, n = 200) ## Ichimoku Cloud Base Line (9, 26, 52, 26) ## Volume Weighted Moving Average (20) data$WMA <- TTR::WMA(x = data$Close, wts = data$Volume, n = 20) ## Hull Moving Average (9) data$HMA <- TTR::HMA(x = data$Close, n = 9) ## Signal data$Signal <- MACD_res$signal ## MACD Signal difference data$MACD_Signal_Diff <- data$MACD - data$Signal ## MA slopes data$MA5_slope <- f_ma_slope(data, n = 5) data$MA10_slope <- f_ma_slope(data, n = 10) data$MA20_slope <- f_ma_slope(data, n = 20) data$MA50_slope <- f_ma_slope(data, n = 50) data$MA100_slope <- f_ma_slope(data, n = 100) data$MA200_slope <- f_ma_slope(data, n = 200) ## Bollinger bands: bb <- TTR::BBands(HLC = data[, c("High", "Low", "Close")]) data$lower_bb <- bb[, "dn"] data$upper_bb <- bb[, "up"] data$ma_bb <- bb[, "mavg"] data$pct_bb <- bb[, "pctB"] data[, pct_close_lower_bb := (Close - lower_bb) / Close] data[, pct_upper_bb_close := (upper_bb - Close) / Close] data[, bb_dif := upper_bb - lower_bb] return(data) }

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source("./PaulOctopus/R/eloratings/ern_scrap_domains.R") source("./PaulOctopus/R/infra/bigQuery.R") # faz o scrap das informações do site EloRating scrapEloRatings <- function(scrap.years = 1930:2018){ # basic domains elo.labels <- elo_scrapLabels() elo.teams <- elo_scrapTeam() elo.tourn <- elo_scrapTournaments() # resultados dos jogos scrap.years %>% map(possibly(elo_scrapResults, otherwise = NULL)) %>% bind_rows() -> elo.results # ranking anuais scrap.years[1:(length(scrap.years)-1)] %>% map(possibly(elo_scrapRank, otherwise = NULL)) %>% set_names(scrap.years[1:(length(scrap.years)-1)]) %>% bind_rows(.id = "rank.year") %>% mutate(rank.year=as.integer(rank.year)) -> elo.rank # agrupa as tabelas e retorna como um alista list( labels = elo.labels, teams = elo.teams, tournaments = elo.tourn, results = elo.results, rank = elo.rank ) %>% return() } # salva um conjunto de tabelas (numa lista nomeada) no BigQuery saveTablesToBigQuery <- function(.tables){ 1:length(.tables) %>% map(function(.idx, .tblist){ tn <- names(.tblist)[.idx] tb <- .tblist[[.idx]] print(paste0("Saving ", tn, " [", nrow(tb), ",", ncol(tb), "] to BigQuery...")) createTable(tn,tb) }, .tblist=.tables) } importEloRatings <- function(scrap.years = 1930:2018){ scrap.years %>% scrapEloRatings() %T>% saveTablesToBigQuery() %>% return() }

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#include "PIDefines.h" #ifdef __PIMac__ #include <Carbon.r> #include "PIGeneral.r" #include "PIUtilities.r" #elif defined(__PIWin__) #define Rez #include "PIGeneral.h" #endif #include "PIActions.h" resource 'PiPL' ( 16000, "GmicPlugin", purgeable ) { { Kind { Filter }, Name { "G'MIC-Qt..." }, Category { "GMIC" }, Version { (latestFilterVersion << 16 ) | latestFilterSubVersion }, #ifdef __PIMac__ #if (defined(__i386__)) CodeMacIntel32 { "Gmic_Entry_Point" }, #endif #if (defined(__ppc__)) CodeMachOPowerPC { 0, 0, "Gmic_Entry_Point" }, #endif #else #if defined(_WIN64) CodeWin64X86 { "Gmic_Entry_Point" }, #else CodeWin32X86 { "Gmic_Entry_Point" }, #endif #endif SupportedModes { noBitmap, doesSupportGrayScale, noIndexedColor, doesSupportRGBColor, noCMYKColor, noHSLColor, noHSBColor, noMultichannel, noDuotone, noLABColor }, EnableInfo { "in (PSHOP_ImageMode, RGBMode, GrayScaleMode, RGB48Mode, Gray16Mode)" }, /* Limit large documents to 100,000 x 100,000 pixels. */ PlugInMaxSize { 100000, 100000 }, FilterCaseInfo { { /* Flat data, no selection */ inStraightData, outStraightData, doNotWriteOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Flat data with selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Floating selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Editable transparency, no selection */ inStraightData, outStraightData, doNotWriteOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Editable transparency, with selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Preserved transparency, no selection */ inStraightData, outStraightData, doNotWriteOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Preserved transparency, with selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination } } } }; resource 'PiPL' ( 16001, "GmicOutputSettingsPlugin", purgeable ) { { Kind { Filter }, Name { "Input/Output Settings for G'MIC-Qt..." }, Category { "GMIC" }, Version { (latestFilterVersion << 16 ) | latestFilterSubVersion }, #ifdef __PIMac__ #if (defined(__i386__)) CodeMacIntel32 { "Gmic_IO_Settings_Entry_Point" }, #endif #if (defined(__ppc__)) CodeMachOPowerPC { 0, 0, "Gmic_IO_Settings_Entry_Point" }, #endif #else #if defined(_WIN64) CodeWin64X86 { "Gmic_IO_Settings_Entry_Point" }, #else CodeWin32X86 { "Gmic_IO_Settings_Entry_Point" }, #endif #endif SupportedModes { noBitmap, doesSupportGrayScale, noIndexedColor, doesSupportRGBColor, noCMYKColor, noHSLColor, noHSBColor, noMultichannel, noDuotone, noLABColor }, EnableInfo { "in (PSHOP_ImageMode, RGBMode, GrayScaleMode, RGB48Mode, Gray16Mode)" }, /* Limit large documents to 100,000 x 100,000 pixels. */ PlugInMaxSize { 100000, 100000 }, FilterCaseInfo { { /* Flat data, no selection */ inStraightData, outStraightData, doNotWriteOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Flat data with selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Floating selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Editable transparency, no selection */ inStraightData, outStraightData, doNotWriteOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Editable transparency, with selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Preserved transparency, no selection */ inStraightData, outStraightData, doNotWriteOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination, /* Preserved transparency, with selection */ inStraightData, outStraightData, writeOutsideSelection, filtersLayerMasks, worksWithBlankData, copySourceToDestination } } } };

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cochrane.Rd

\name{cochrane} \alias{cochrane} \non_function{} \title{Data for Cochrane Collaboration logo } \usage{data(cochrane)} \description{ Data from randomised trials before 1980 of corticosteroid therapy in premature labour and its effect on neonatal death. } \format{ This data frame contains the following columns: \describe{ \item{name}{Identifier for the study } \item{ev.trt}{ Number of deaths in the treated group } \item{n.trt}{ Number in the treated group } \item{ev.ctrl}{ Number of deaths in the control group } \item{n.ctrl}{ Number in the control group } } } \details{ This meta-analysis, if done, would likely have resulted in the treatment being widely used a decade earlier than it was, saving many lives. The graph is part of the logo of the Cochrane Collaboration, a group aiming to perform systematic reviews of the entire clinical trial literature. } \source{ \url{http://www.cochrane.org} } \examples{ data(cochrane) steroid <- meta.MH(n.trt, n.ctrl, ev.trt, ev.ctrl, names=name, data=cochrane) plot(steroid, col=meta.colors("RoyalBlue")) } \keyword{datasets}

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full_test.R

# R script for DESeq2 processing of 1 setwd('..') #### installing and loading packages #### if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager", repos='http://cran.us.r-project.org') knitr::opts_chunk$set(tidy=FALSE, cache=TRUE, dev="png", message=FALSE, error=FALSE, warning=TRUE) options(scipen = 5) # scientific notation for plotting later packages <- c("reshape", "ggplot2", "ggrepel", "RColorBrewer", "pheatmap", "data.table") if (length(setdiff(packages, rownames(installed.packages()))) > 0) { install.packages(setdiff(packages, rownames(installed.packages())), repos='http://cran.us.r-project.org') } install.packages("data.table", dependencies=TRUE) BiocManager::install("DESeq2") BiocManager::install("DEGreport") BiocManager::install("GenomicFeatures") library(reshape) library(ggplot2) library(ggrepel) library(DEGreport) library(RColorBrewer) suppressPackageStartupMessages(library(DESeq2)) library(pheatmap) suppressPackageStartupMessages(library('GenomicFeatures')) library(tximport) library(readr) library(tximportData) ## ----config---- species <- 'hg38' ## ---- making an tx2gene for hg38 ------------ txdb <-makeTxDbFromGFF("Homo_sapiens.GRCh38.100.gff") keytypes(txdb) k <- keys(txdb, keytype = "GENEID") df <- select(txdb, keys = k, columns = "TXNAME", keytype = "GENEID") tx2gene <- df[, 2:1] head(tx2gene) write.csv(tx2gene, paste0("out/tx/", species, "_tx2gene.csv"), row.names = FALSE) ## ----txiSetup------------------------------------------------------------ #library("tximport") #library("readr") library("tximportData") dir <- system.file("/chrisRNA/quants/", package="tximportData") samples <- read.table(file.path(dir, paste0("Volumes/MiniDrive/Dropbox/daugherty-lab/Helitrons/DESeq2/lists/", species, ".txt")), header=TRUE) samples$Condition rownames(samples) <- samples$Run samples ## ----txiFiles------------------------------------------------------------ files <- file.path(paste0("/Volumes/MiniDrive/Dropbox/daugherty-lab/Helitrons/DESeq2/quants/", samples$Run, ".sf")) files names(files) <- samples$run tx2gene <- read_csv(file.path(paste0("/Volumes/MiniDrive/Dropbox/daugherty-lab/Helitrons/DESeq2/tx/", species, "_tx2gene.csv"))) ## ----tximport, results="hide"-------------------------------------------- txi <- tximport(files, type="salmon", tx2gene=tx2gene, ignoreAfterBar = TRUE) ## ----txi2dds, results="hide"--------------------------------------------- library("DESeq2") ddsTxi <- DESeqDataSetFromTximport(txi, colData = samples, design = ~ Condition) dds <- ddsTxi

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/NNhuMG/R/phenoReduce.R

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phenoReduce.R

#' @title Minimal Marker Cutoff Combinations #' @description finds low-dimensional phenotypic representations of target subsets #' @param con object returned by \code{\link{findCutoffs}} #' @param target.num.marker desired least number of markers, Default: NA #' @param b weighs recall and precision in F-score, Default: 0.5 #' @return \code{con} object with updated and added fields: #' \describe{ #' \item{\code{cutoffs}}{list with updated cutoffs per marker element} #' \item{\code{targets}}{named indices of subsets that phenotypically differ from at least one other subset} #' \item{\code{reduc}}{\describe{ #' \item{\code{keep}}{logical vector indicating final marker-cutoff-combinations} #' \item{\code{solutions}}{matrix containing all feasible solutions returned by \code{\link{lp}}} #' \item{\code{fscore}}{matrix with computed fscores for all feasible solutions} #' }} #' } #' @details The function solves an Integer Linear Programm (ILP) to find a set of at most 3 cutoffs and a minimal and/or required #' number of markers that still allow to discriminate between subsets. In the likely case that multiple solutions are found, #' these are used to gate the target phenotypes and compute F-scores. The optimal solution is selected based on the best average #' accuracy among the subsets. If target.num.marker is specified, the ILP maximizes the sum of effective differences \eqn{Delta} #' between the subsets and a minimal set as well as a solution with at least the required number of markers is returned. See #' \code{\link{findCutoffs}} for examples. Note that currently no print, show, or summary functions are implemented. #' @seealso #' \code{\link{findCutoffs}} #' \code{\link[lpSolve]{lp}} #' @rdname phenoReduce #' @export #' @importFrom lpSolve lp #' @importFrom utils head phenoReduce <- function(con, target.num.marker = NA, b = 0.5) { required <- !is.na(target.num.marker) # need to copy cutoff.comb <- con$reduc$cutoff.comb cutoff.comb.ind <- con$reduc$cutoff.comb.ind cutoff.comb.keep <- con$reduc$cutoff.comb.keep cutoff.comb.constraint <- con$reduc$cutoff.comb.constraint cutoff.comb.phenocode <- con$reduc$cutoff.comb.phenocode comb.subset <- con$subset # solve for minimal sets of markers, use negated ranks to select according to # separation keep.ord <- t(apply(-abs(con$delta.mat), 1, rank, ties.method = "min")) dimnames(keep.ord) <- dimnames(con$keep.mat) keep.comb.ord <- keep.ord[, cutoff.comb.ind] # TODO: if findCutoffs(find.all==FALSE) make sure that # all(apply(!!keep.solve, 1, any)) # keep.comb.mat <- 1 * (con$keep.mat[ , cutoff.comb.ind] & cutoff.comb.keep) keep.comb.mat <- 1 * cutoff.comb.keep if (required) { # solve for required number of markers target.num.marker <- min(target.num.marker, ncol(con$delta.mat)) cat(paste0("\ncomputing optimal combination for ", target.num.marker, " markers\n")) delta.sums <- abs(con$delta.mat)[, cutoff.comb.ind] delta.sums[!cutoff.comb.keep] <- 0 colnames(delta.sums) <- colnames(cutoff.comb.constraint) delta.solve <- 1 * cutoff.comb.keep delta.num.cuts <- sapply(cutoff.comb, length) num.cuts <- 1:con$reduc$max.cuts delta.sol <- vector("list", length(num.cuts)) for (n in num.cuts) { d.con <- rbind(cutoff.comb.constraint, delta.solve) d.con[, delta.num.cuts > n] <- 0 d.obj <- 1 + max(colSums(delta.sums)) - colSums(delta.sums) # set target minimum number of markers d.rhs <- c(target.num.marker, rep(1, nrow(d.con) - 1)) m.dir <- rep("<=", nrow(cutoff.comb.constraint) - 1) d.dir <- c(">=", m.dir, rep(">=", nrow(delta.solve))) print(d.sol <- lpSolve::lp("min", d.obj, d.con, d.dir, d.rhs, all.bin = TRUE, num.bin.solns = 100)) delta.sol[[n]] <- matrix(utils::head(d.sol$solution, ncol(d.con) * d.sol$num.bin.solns), nrow = d.sol$num.bin.solns, byrow = TRUE) } delta.sol <- do.call("rbind", delta.sol) keep.comb.mat[, !apply(!(!delta.sol), 2, any)] <- 0 } solutions.l <- vector("list", max(keep.ord)) cat("\ncalculating minimal marker combinations, starting with best separating markers\n") for (i in seq_along(solutions.l)) { keep.solve <- keep.comb.mat keep.solve[keep.comb.ord > i] <- 0 f.con <- rbind(cutoff.comb.constraint, keep.solve) f.obj <- rep(1, ncol(f.con)) # minimize objective function, i.e. minimal combo that satisfies constraints f.rhs <- c(1, rep(1, nrow(f.con) - 1)) m.dir <- rep("<=", nrow(cutoff.comb.constraint) - 1) f.dir <- c(">=", m.dir, rep(">=", nrow(keep.solve))) cat("\n", i, "\n") # Please note, we need to restrict number of feasible solution for there will be # combinatorial explosion for many markers included in the model # encountered crashes when 100 < num.bin.solns < 200 with expected solutions >> 200 print(sol <- lpSolve::lp("min", f.obj, f.con, f.dir, f.rhs, all.bin = TRUE, num.bin.solns = 100)) solutions.l[[i]] <- matrix(utils::head(sol$solution, ncol(f.con) * sol$num.bin.solns), nrow = sol$num.bin.solns, byrow = TRUE) } # remove infeasible solutions feasible <- sapply(solutions.l, function(sol) any(!(!sol))) if (!any(feasible)) stop("try to increase delta, max number of cutoffs, or select subsets manually") solutions.l <- solutions.l[feasible] # check also for max number of possible sols solutions.l <- solutions.l[sapply(solutions.l, function(sol) nrow(sol) < 100)] if (required) { solutions.l <- c(list(delta.sol), solutions.l) } # setup list to track (delta separation) ranks, number of solutions, number of # markers, additional combinations of markers/cuts will be appended, duplicates # removed sol.track <- sapply(seq_along(solutions.l), function(d.rank) { tmp <- cbind(d.rank, n.marker = apply(solutions.l[[d.rank]], 1, sum)) tmp <- cbind(tmp, solution = seq_len(nrow(tmp))) tmp }, simplify = FALSE) # collapse solutions with equal number of markers (same within each) sol.colps <- sapply(sol.track, "[", 1, 2) sol.track <- lapply(unique(sol.colps), function(sc) { do.call("rbind", sol.track[sol.colps %in% sc]) }) solutions <- lapply(unique(sol.colps), function(sc) { do.call("rbind", solutions.l[sol.colps %in% sc]) }) # remove duplicates (caused by replicated solutions) sol.track <- mapply(function(sol.h, sols) { sol.h[!duplicated(sols), , drop = FALSE] }, sol.track, solutions, SIMPLIFY = FALSE) solutions <- lapply(solutions, function(sols) sols[!duplicated(sols), , drop = FALSE]) # take resulting marker combinations and pick the one with best average f-score # among the subsets. sol.track <- do.call("rbind", sol.track) all.sols <- do.call("rbind", solutions) colnames(all.sols) <- colnames(cutoff.comb.constraint) # rename names(cutoff.comb) <- colnames(keep.solve) rownames(cutoff.comb.phenocode) <- names(comb.subset) # create matrix of true positives (sampled from raw data) tsne.trupos <- sapply(con$res$bin.idx[comb.subset], function(b) seq_len(nrow(con$comb$data)) %in% b) colnames(tsne.trupos) <- names(comb.subset) # exclude the exclusion (i.e. NOT-subset-gated cells/bins) tsne.trupos <- tsne.trupos[, !grepl("\\*\u00B7root", names(comb.subset))] cutoff.comb.phenocode <- cutoff.comb.phenocode[!grepl("\\*\u00B7root", names(comb.subset)), ] sol.phenotypes <- sapply(seq_len(nrow(all.sols)), function(l) { cutoff.comb.phenocode[, !(!all.sols[l, ]), drop=FALSE] }, simplify = FALSE) sol.cutoff.comb <- sapply(seq_len(nrow(all.sols)), function(l) { cutoff.comb[!(!all.sols[l, ])] }, simplify = FALSE) # compute f-measure for each gated phenotype/solution fms.sols <- sapply(seq_len(nrow(all.sols)), function(l) { phenocode <- cutoff.comb.phenocode[, !(!all.sols[l, ]), drop=FALSE] dat <- con$comb$data[, names(cutoff.comb[!(!all.sols[l, ])]), drop=FALSE] sapply(rownames(phenocode), function(s) { gated <- apply(sapply(colnames(dat), function(m) { testCutoffs(x = dat[, m], pheno = phenocode[s, m], cutoffs = cutoff.comb[!(!all.sols[l, ])][[m]]) }), 1, all) fmeasure(pred = gated, true = tsne.trupos[, s], b = b) }) }) # w/o any gating fms.null <- apply(tsne.trupos, 2, function(tr) { fmeasure(pred = !logical(nrow(con$comb$data)), true = tr, b = b) }) # max per subset fms.max <- apply(fms.sols, 1, max) fms.opt <- apply(fms.sols, 1, function(x) which(x == max(x))) fms.opt <- unique(unlist(fms.opt)) # calculate f-measure only within a combo so we need a hierarchy here hierarchy.sol <- apply(all.sols, 1, function(x) { apply(all.sols, 1, function(y) { all(x[!(!x)] == y[!(!x)]) }) }) if (!is.matrix(hierarchy.sol)) { hierarchy.sol <- as.matrix(hierarchy.sol) } # calculate row max for each family # fms.sols.n <- sweep(fms.sols, 1, fms.max, '/') fms.max.hierarchy <- apply(hierarchy.sol, 1, function(x) { apply(fms.sols[, x, drop = FALSE], 1, max) }) colnames(fms.max.hierarchy) <- seq_len(ncol(fms.max.hierarchy)) # pick the best average opt <- which.max(apply(fms.max.hierarchy, 2, mean)) # find maxima for each subset in (possibly) different numbers of markers targets <- apply(fms.sols[, hierarchy.sol[opt, ], drop = FALSE], 1, which.max) # cbind(fms.null, hierarchy.max=fms.max.hierarchy[ , opt], # fms.subset.max=fms.max, fms.sols[, hierarchy.sol[opt, ], drop=FALSE]) # the target marker-cutoff-combo cuts.target <- apply(!(!all.sols[hierarchy.sol[opt, ], , drop = FALSE]), 2, any) # check, this should never happen, for testthat if (required && !any(apply(delta.sol, 1, function(x) all(all.sols[hierarchy.sol[opt, ], , drop = FALSE][1, ] == x)))) { stop("check the ILP model!") } target.hierarchy <- sol.phenotypes[hierarchy.sol[opt, ]] target.phenos <- sol.phenotypes[which(hierarchy.sol[opt, ])[targets]] # extract the target phenotyp for each subset (the 'list' diagonal) target.phenos <- sapply(seq_along(target.phenos), function(t) target.phenos[[t]][t, , drop = FALSE], simplify = FALSE) target.phenos <- sapply(target.phenos, function(x) paste(ifelse(nzchar(x[1, ]), colnames(x), ""), x[1, ], sep = ""), simplify = FALSE) names(target.phenos) <- paste("m\u00B7", rownames(fms.sols), sep = "") # phenotypes for required number of markers or for any more than the minimal full.phenos <- unlist(apply(target.hierarchy[[1]], 1, function(x) list(paste(ifelse(nzchar(x), colnames(target.hierarchy[[1]]), ""), x, sep = ""))), recursive = FALSE) names(full.phenos) <- paste("f\u00B7", names(full.phenos), sep = "") target.phenos <- c(full.phenos, target.phenos) # target.phenos <- target.phenos[!duplicated(target.phenos)] con$cutoffs <- cutoff.comb[cuts.target] con$targets <- target.phenos con$reduc$keep <- cuts.target con$reduc$solutions <- all.sols con$reduc$fscores <- fms.sols return(con) } ## @title helper function ## @description simple 1D-gating ## @param x numeric vector of expression values ## @param pheno char-encoded phenotype of a given marker expression ## @param cutoffs numeric vector of cutoff-values ## @return logical vector ## @details gating without flowCore overhead. see also \code{\link{pheno}} #' @keywords internal testCutoffs <- function(x, pheno, cutoffs) { n <- length(cutoffs) # cutoffs should be sorted already if (n < 1 || n > 3) { NULL } else { switch(n, { if (pheno == "") return(!logical(length(x))) if (pheno == "-") return(x <= cutoffs[1]) if (pheno == "+") return(cutoffs[1] < x) }, { if (pheno == "") return(!logical(length(x))) if (pheno == "--") return(x <= cutoffs[1]) if (pheno == "-") return(x <= cutoffs[2]) if (pheno == "+-") return(cutoffs[1] < x & x <= cutoffs[2]) if (pheno == "+") return(cutoffs[1] < x) if (pheno == "++") return(cutoffs[2] < x) }, { if (pheno == "") return(!logical(length(x))) if (pheno == "---") return(x <= cutoffs[1]) if (pheno == "--") return(x <= cutoffs[2]) if (pheno == "-") return(x <= cutoffs[3]) if (pheno == "+--") return(cutoffs[1] < x & x <= cutoffs[2]) if (pheno == "+-") return(cutoffs[1] < x & x <= cutoffs[3]) if (pheno == "++-") return(cutoffs[2] < x & x <= cutoffs[3]) if (pheno == "+") return(cutoffs[1] < x) if (pheno == "++") return(cutoffs[2] < x) if (pheno == "+++") return(cutoffs[3] < x) }) } } ## @title helper function ## @description computes the harmonic mean of precision and recall ## @param pred logical vector of predicted positives ## @param true logical vector of true positives ## @param b (beta-) parameter weighing precision vs. recall, Default: 1 ## @return F-beta score ## @details b = 0.5 weighs recall lower than precision #' @keywords internal fmeasure <- function(pred, true, b = 1) { retrieved <- sum(pred) if (retrieved != 0) { precision <- sum(pred & true)/retrieved } else precision <- 0 recall <- sum(pred & true)/sum(true) if ((recall != 0) && (precision != 0)) { Fm <- (1 + b^2) * precision * recall/((b^2) * precision + recall) } else Fm <- 0 Fm }

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/code/prepare_data_elections.R

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prepare_data_elections.R

# Replication files for 'The Emergence of Party-Based Political Careers in the UK, 1801-1918' # Cox & Nowacki (Journal of Politics, forthcoming) # prepare_data_elections.R: Tidy data for election-level analysis # ------------- # DEPENDENCIES # ------------- source("code/0_functions.R") # --------- # LOAD DATA # --------- d_orig <- import("data/eggers_spirling/elections.csv") f <- import("data/eggers_spirling/election_returns.csv") m <- import("data/eggers_spirling/mps.csv") s <- import("data/eggers_spirling/services.csv") of <- import("data/eggers_spirling/officeholdings.csv") ofid <- import("data/eggers_spirling/offices.csv") af <- import("data/aidt-franck/pre-reform.dta") hss <- import("data/hss/hopt-v3.dta") cdum <- import("data/eggers_spirling/constituencies.dta") # --------- # PREPARE DATA # --------- # Prepare Eggers-Spirling Data: Election-Level elecs <- d_orig %>% mutate( date = as.Date(date), year = year(date) ) %>% filter(date > as.Date("1832-01-01") & date < as.Date("1930-01-01")) %>% mutate(year = ifelse(date == as.Date("1910-12-03"), 1911, year)) %>% filter(by_election == 0) # Get IDs ge_dates <- unique(elecs$date) ge_dates2 <- unique(d_orig$date[d_orig$by_election == 0]) ge_ids <- unique(elecs$election_id) # Clean party labels con_labels <- c("C", "C (Ind C)", "C (Nat P)", "C (Nat P) (C)", "C*", "Ind L/Crf (LU)", "LU", "LU (C)", "LU (Nat P) (C)", "LU*", "U", "Co C") lib_labels <- c("L", "Ind L (L)", "LU (L)", "Co L") # how many party non-standard party labels? trunc <- f %>% filter(party %in% con_labels[c(-1, -7)] | party %in% lib_labels[-1]) %>% left_join(elecs) %>% filter(year < 1912) nrow(trunc) # Prepare contest-level dataset winners <- f %>% group_by(election_id) %>% mutate(party = case_when( party %in% con_labels ~ "C", party %in% lib_labels ~ "L", TRUE ~ "O" )) %>% mutate( v_share = votes / sum(votes), con_share = sum(v_share[party == "C"]), lib_share = sum(v_share[party == "L"]) ) %>% ungroup() %>% filter(winner == 1 & election_id %in% ge_ids) %>% mutate( Con = party == "C" | party == "LU" | party == "LU (C)", Lib = party == "L", Other = party != "C" & party != "L" ) %>% group_by(election_id, unopposed) %>% summarise( Con = sum(Con), con_votes = mean(con_share), Lib = sum(Lib), lib_votes = mean(lib_share), Other = sum(Other) ) %>% left_join(elecs) # add contest-level info # Contest-level dataset: add lagged seat shares / contest indicators shares <- winners %>% mutate( tpty = Con + Lib, Con = Con / tpty, Lib = Lib / tpty, tpty_v = con_votes + lib_votes, con_votes = con_votes / tpty_v, lib_votes = lib_votes / tpty_v, con_sub = ifelse( con_votes == 0 | is.na(con_votes) == TRUE, Con, con_votes ) ) %>% group_by(constituency.id) %>% arrange(date) %>% mutate( Con_lg = lag(Con), Con_lg2 = lag(Con, 2), unopposed_lg = lag(unopposed), unopposed_lg2 = lag(unopposed, 2), con_votes_lg = lag(con_votes), con_sub_lg = lag(con_sub), con_sub_lg2 = lag(con_sub, 2), year = substr(date, 1, 4) ) %>% mutate(year = ifelse(date == as.Date("1910-12-03"), 1911, year)) %>% filter(year != "1831") %>% ungroup() %>% dplyr::select(election_id, constituency.id, constituency.name, date, unopposed, year, Con, Lib, total_seats, seats_up) # Get constituency ID for pre-1832 constituencies d_pre <- d_orig %>% mutate( date = as.Date(date), year = year(date) ) %>% filter(date < as.Date("1832-01-01"), by_election == 0) %>% dplyr::select(election_id, constituency.id, constituency.name, year) # Clean HSS data (pre-1832) to get contest-level hss_shares <- hss %>% rowwise() %>% mutate(date = make_date(year, month, day)) %>% filter(bye == 0 & winner == 1) %>% group_by(election) %>% mutate( Con = party == "T", Lib = party == "W", unopposed = as.numeric(ncands == nwinners) ) %>% group_by(election, constituency, unopposed, year, date, nwinners) %>% summarise( Con = sum(Con), Lib = sum(Lib) ) %>% mutate( tpty = Con + Lib, Con = Con / tpty, Lib = Lib / tpty ) %>% ungroup() %>% group_by(constituency) %>% arrange(year) %>% mutate( Con_lg = lag(Con), Con_lg2 = lag(Con, 2), unopposed_lg = lag(unopposed), unopposed_lg2 = lag(unopposed, 2) ) %>% left_join(d_pre, by = c("constituency" = "constituency.name", "year")) %>% mutate(constituency.name = constituency, total_seats = nwinners, seats_up = nwinners) %>% ungroup() %>% dplyr::select(election_id, constituency.id, constituency.name, date, unopposed, year, Con, Lib, total_seats, seats_up) # Bind pre-1832 and post-1832 together mg_df <- rbind(hss_shares, shares) %>% # mutate_at(vars(con, lib), ~ replace_na(., 0)) %>% group_by(constituency.name) %>% arrange(year) %>% mutate( Con_lg = lag(Con), Con_lg2 = lag(Con, 2), unopposed_lg = lag(unopposed), unopposed_lg2 = lag(unopposed, 2), year_fac = factor(year) ) %>% distinct() # prepare and merge in constituency dummies cdum <- cdum %>% select( constituency_id, start_year, end_year, const_type, country, patron_sack, patron_gash, patron_hanham ) # add const dummies and recode patronal districts mg_df <- mg_df %>% left_join(cdum, by = c("constituency.id" = "constituency_id")) %>% mutate(patronal = case_when( year < 1832 & patron_sack == 1 ~ 1, year %in% 1832:1867 & const_type == "borough" & patron_sack == 1 & patron_hanham == 1 ~ 1, year %in% 1832:1867 & const_type == "county" & patron_hanham == 1 ~ 1, year > 1867 & patron_hanham == 1 ~ 1, TRUE ~ 0 )) # keep important variables export <- mg_df %>% dplyr::select(election_id, constituency.id, constituency.name, date, year_fac, unopposed, year, Con, Lib, Con_lg, Con_lg2, unopposed_lg, unopposed_lg2, const_type, country, patronal, total_seats) # Export for next steps write.csv(export, "output/mod_data/seat_shares.csv")

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/Household_power_consumption/plot3.R

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refs/heads/master

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readFile <- function() { require(data.table) dates <- fread("household_power_consumption.txt", na.strings="?") intervals <- subset(dates, Date == "1/2/2007" | Date == "2/2/2007") intervals } plot3 <- function() { require(lubridate) data <- readFile() times <- dmy_hms(paste(data$Date, data$Time, sep = " ")) png("plot3.png",width=480,height=480, bg="white") plot(times, data$Sub_metering_1, type="n", ylab="Energy sub metering", xlab="" ) lines(times, data$Sub_metering_1, col="black" ) lines(times, data$Sub_metering_2, col="red" ) lines(times, data$Sub_metering_3, col="blue" ) legend("topright", lty = c(1,1,1), col=c("black","red","blue"), legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), cex=0.85 ) dev.off() }

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/rsiconfi/gastos_estaduais_SP_com_saude_2018.R

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carolina-quiterio-ifood/APIs

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gastos_estaduais_SP_com_saude_2018.R

# -*- coding: utf-8 # Abraji (https://www.abraji.org.br) # Reinaldo Chaves (reinaldo@abraji.org.br) # Script exemplo de uso do rsiconfi # Baixa gastos com saúde do Estado de SP # #install.packages("devtools") #devtools::install_github("tchiluanda/rsiconfi") library(rsiconfi) library(dplyr) library(tidyr) # Códigos de UFs do Brasil # https://atendimento.tecnospeed.com.br/hc/pt-br/articles/360021494734-Tabela-de-C%C3%B3digo-de-UF-do-IBGE # São Paulo é 35 - entity # Vamos ver Despesas por Função (I-E) # A função get_account_dca retorna os códigos possíveis # Coloco o ano 2018, o I-E e um código relacionado com SP # Aqui a lista de todos: # https://siconfi.tesouro.gov.br/siconfi/pages/public/conteudo/conteudo.jsf?id=581 clique em Tabela dos códigos de instituição utilizados no Siconfi - Código das Instituições df_conta_dca <- get_account_dca(2018, "I-E", c("35") ) ## Captura gastos de SP com Saúde (11) gasto_uf_sp_2019 <- get_dca(year = 2018, annex = "I-E", entity = "35", arg_cod_conta = "10") # Os dados de 2019 ainda não estão na API # Com um vetor dos anos posso baixar tudo e comparar depois gasto_uf_sp <- get_dca(year = c(2013, 2014, 2015, 2016, 2017, 2018), annex = "I-E", entity = "35", arg_cod_conta = "10")

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refs/heads/master

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zillow.R

library(ggplot2) library(readr) library(igraph) library(dplyr) library(tidyr) library(plyr) library(data.table) library(ggmap) library(maptools) library(ggthemes) library(rgeos) library(broom) library(gridExtra) library(reshape2) library(scales) library(rgdal) # Plot themes for ggplot and ggmap to be used throughout plotTheme <- function(base_size = 12) { theme( axis.text = element_blank(), axis.line = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), panel.border = element_blank(), panel.grid = element_blank(), text = element_text( color = "black"), plot.title = element_text(size = 18,colour = "black"), plot.subtitle = element_text(face="italic"), plot.caption = element_text(hjust=0), panel.background = element_blank(), strip.background = element_rect(fill = "grey80", color = "white"), strip.text = element_text(size=12), plot.background = element_blank(), legend.background = element_blank(), legend.title = element_text(colour = "black", face = "italic"), legend.text = element_text(colour = "black", face = "italic")) } mapTheme <- function(base_size = 12) { theme( text = element_text( color = "black"), plot.title = element_text(size = 18,colour = "black"), plot.subtitle=element_text(face="italic"), plot.caption=element_text(hjust=0), axis.ticks = element_blank(), panel.background = element_blank(), panel.grid.major = element_line("grey80", size = 0.1), strip.text = element_text(size=12), axis.title = element_blank(), axis.text = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), panel.grid.minor = element_blank(), strip.background = element_rect(fill = "grey80", color = "white"), plot.background = element_blank(), legend.background = element_blank(), legend.title = element_text(colour = "black", face = "italic"), legend.text = element_text(colour = "black", face = "italic")) } ########################### Graph 1 ############################# # Getting All Data pertaining to WA & convert to long format price <- read.csv("price.csv", header = TRUE) p1 <- na.omit(price) p1.f<-filter(p1, State %in% c("WA")) p1.wa<-gather(p1.f, year, price, Nov.10:Jan.17,factor_key = TRUE) # Group price data by county and manipulate data p1.wa<- p1.wa %>% group_by(County) p1.wa$Loc<-paste(p1.wa$County, p1.wa$State) # Code to Geocode locations and save as new file # for(i in 1:nrow(p1.wa)) # { # result <- geocode(p1.wa$Loc[i], output = "latlona", source="google") # p1.wa$lon[i]<-as.numeric(result[1]) # p1.wa$lat[i]<-as.numeric(result[2]) # } # # write.csv(p1.wa, "wageo.csv", row.names=FALSE) wageo <- read.csv("wageo.csv", header = TRUE) waCounty <- na.omit(wageo) # Reduce data to get the areas with highest increase in price # Sort Data by minimum price and group by county wa.min<- waCounty %>% group_by(County) %>% slice(which.min(price)) # Sort data by max price and group by county wa.max<- waCounty %>% group_by(County) %>% slice(which.max(price)) # Subtract max from min and sort by highest difference wa.growth<-within(merge(wa.min,wa.max,by="Loc"), { P <- price.y - price.x })[,c("Loc","P")] # Had to Geocode again # for(i in 1:nrow(wa.growth)) # { # result <- geocode(as.character(wa.growth$Loc[i]), output = "latlona", source="google",override_limit = TRUE) # wa.growth$long[i]<-as.numeric(result[1]) # wa.growth$lat[i]<-as.numeric(result[2]) # } #write.csv(wa.growth, "wageo2.csv", row.names=FALSE) wageo2 <- read.csv("wageo2.csv", header = TRUE) wac2 <- na.omit(wageo2) # Removing WA from Location wac2$Loc = substr(wac2$Loc,1,nchar(as.character(wac2$Loc))-3) # Create WA basic map states <- map_data("state") counties <- map_data("county") wa_df <- subset(states, region == "washington") wa_county <- subset(counties, region == "washington") # Cleaning/Prepping data wc <-subset(wa_county, select = c(group,subregion)) wc <-unique(wc) colnames(wc)[2]<-"County" colnames(wac2)[1]<-"County" wat <- subset(wac2, select = c(County, P)) colnames(wa_county)[6]<-"County" wat$County<-tolower(wat$County) # Joining the DF by "County" wa_final <- inner_join(wa_county, wat, by = c("County"), all = TRUE) w2 <- na.omit(wa_final) # Create the base map of WA wa_base <- ggplot(data = wa_df, mapping = aes(x = long, y = lat, group = group)) + coord_fixed(1.3) + geom_polygon(color = "black", fill = "gray") + plotTheme() # Add county lines wa_base<-wa_base + geom_polygon(data = wa_county, fill = NA, color = "white") + geom_polygon(color = "black", fill = NA) # Plotting the Graph, adding colors, labels wamap <- wa_base + geom_polygon(data = w2, aes(fill = P), color = "white") + geom_polygon(color = "black", fill = NA) + scale_fill_gradient(low = "#a3fc25", high = "#ff2828", space = "Lab", na.value = "grey50", guide = "colourbar", trans = "log10", labels = scales::dollar_format(prefix = "$"), breaks = c(0,30,100,300,1000, 3000)) + labs(title="Rise in price of Rent", subtitle="WA by county (2010 - 2017)", fill = "Price") + plotTheme() wamap ########################### Graph 2 ############################# # Read in data, convert to long format, manipulate data ppsq <- read.csv("pricepersqft.csv", header = TRUE) ppsq1 <- na.omit(ppsq) # Take the average of the price after combining by county and state pps<-aggregate(ppsq1[, 7:81], list(ppsq1$County,ppsq1$State), mean) pps <- na.omit(pps) pps<-gather(pps, year, price, November.2010:January.2017,factor_key = TRUE) # Extract date in YYYY format pps$year <- gsub("[^0-9]", "", pps$year) # Create base map states <- map_data("state") counties <- map_data("county") state <- ggplot(data = states, mapping = aes(x = long, y = lat, group = group)) + coord_fixed(1.3) + geom_polygon(color = "black", fill = "gray") base <- state + geom_polygon(data = counties, fill = NA, color = "white") + geom_polygon(color = "black", fill = NA) # Convert data type to factor colnames(counties)[6]<-"Group.1" counties$Group.1 <- as.factor(counties$Group.1) pps$Group.1<-tolower(pps$Group.1) pps<-aggregate(pps[, 4], list(pps$Group.1,pps$year), mean) # Make factor levels equal in order to join two df together combined <- sort(union(levels(counties$Group.1), levels(pps$Group.1))) n <- inner_join(mutate(counties, Group.1=factor(Group.1, levels=combined)), mutate(pps, Group.1=factor(Group.1, levels=combined))) n$x<-as.numeric(n$x) # For each year, print a graph lapply(sort(unique(n$Group.2)), function(i) { base + geom_polygon(data = n[n$Group.2==i,], aes(fill = x), color = "white") + geom_polygon(color = "black", fill = NA) + scale_fill_gradient("Price/sqf",low = "#a3fc25", high = "#ff2828", space = "Lab", na.value = "grey50", guide = "colourbar", trans = "log10", labels = scales::dollar_format(prefix = "$"), breaks = c(0,.5,1,2, 3)) + labs(title="Average price per square foot by county ", subtitle=i, fill = "Price") + plotTheme() }) ########################### Graph 3 ############################# sfhomes <-read.csv("sfhomes.csv", header = TRUE, as.is = T) sfhs <- na.omit(sfhomes) sfhs$SaleYr <- as.factor(sfhs$SaleYr) # Plot the neighborshoods in SF nb <- readOGR("SF", "geo_export_6cb760e3-ca2c-47f6-9af2-01ec1009ce71") plot(nb) bbox <- nb@bbox # Add some padding sf_bbox <- c(left = bbox[1, 1] - .01, bottom = bbox[2, 1] - .005, right = bbox[1, 2] + .01, top = bbox[2, 2] + .005) # Download basemap basemap <- get_stamenmap( bbox = sf_bbox, zoom = 13, maptype = "toner-lite") ggmap(basemap) + geom_point(data = sfhs, aes(x = long, y = lat, color = SalePrice), size = 0.25, alpha = .6) + facet_wrap(~SaleYr, scales = "fixed", ncol = 4) + coord_map() + mapTheme() + theme(legend.position = c(.85, .25)) + scale_color_gradientn("Price", colors = c("#e9ff00","#ffff00","#ffd400","#ffbf00","#ff9000","#ff6100","#ff0000"), labels = scales::dollar_format(prefix = "$")) + labs(title="San Francisco home prices", subtitle="2009 - 2015") ########################### Graph 4 ############################# # Eviction Heat Map Data for SF evict <-read.csv("Eviction_Notices.csv", header = TRUE, as.is = T) sfrent <-read.csv("sfrent.csv", header = TRUE, as.is = T) e1 <- na.omit(evict) sfr <- na.omit(sfrent) # convert to numeric e1[,7:25]<-suppressWarnings(sapply(e1[,7:25,drop=FALSE],as.numeric)) e1<-na.omit(e1) # Separate "Location" col into "Lat", "Long" e1<-e1 %>% extract("Location", c("Lat", "Long"), "\$([^,]+), ([^)]+)\$") e1$freq <- rowSums(e1[,7:25]) e1 e1[,29:30]<-suppressWarnings(sapply(e1[,29:30,drop=FALSE],as.numeric)) # Plot the neighborshoods in SF nb <- readOGR("SF", "geo_export_6cb760e3-ca2c-47f6-9af2-01ec1009ce71") plot(nb) bbox <- nb@bbox # Add some padding sf_bbox <- c(left = bbox[1, 1] - .01, bottom = bbox[2, 1] - .005, right = bbox[1, 2] + .01, top = bbox[2, 2] + .005) # Download basemap basemap <- get_stamenmap( bbox = sf_bbox, zoom = 13, maptype = "toner-lite") e1<-data.frame(e1,stringsAsFactors=FALSE) e1 sapply(e1, class) sfr ggmap(basemap) + stat_density2d(data = e1, aes(x = Long, y = Lat, fill = ..level.., alpha = ..level..), size = 0.01, bins = 16, geom = 'polygon') + scale_fill_gradient(low = "green", high = "red") + facet_wrap(~File.Date) + theme(axis.title = element_blank(), axis.text = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), axis.ticks = element_blank()) + theme(legend.position="none")+ labs(title="Heat Map of San Francisco Evictions", subtitle="1997 - 2015")

58e6cbb3038cc4ca98ce7f1c4f83911f1b1291c2

a19fc7dd26a2acdda9b2442fc490cf29928eade7

/multiSpeciesTeDistribution/Fig_1_ploter.R

891dca318fa9ab7917e37d1c595d4876d742f763

[]

no_license

ReubenBuck/Domain_manuscript_scripts

96d2fed7042f5a0b6e7d79f10f80f20128b6e4e8

5c290e5334920e9dd70285cce242eefee3fd8be1

refs/heads/master

2020-04-16T19:30:03.535423

2016-10-23T23:08:53

46,104,045

0

null

UTF-8

R

false

11,897

r

Fig_1_ploter.R

## lets do PCA analysis library(ggplot2) library(ks) library(gplots) library(GenomicRanges) require(ggbio) setwd("~/Desktop/Dist_matrix_TE_div/") source(file="~/Desktop/Domain_manuscript/Domain_manuscript_scripts/functions.R") spec1 <- "Human" spec2 <- "Horse" spec3 <- "Bovine" spec4 <- "Dog" spec5 <- "Mouse" ucsc1 <- "hg19" rem.un <- "yes" no.xy <- F keep.limit <- 1350000 mb <- 1000000 # bin.size is set so that the indexing stays consitent across pipeline bin.size = 500000 # trying out the unscaled method keep.NGenes = "yes" keep.NG4s = "no" keep.NCpGI = "no" keep.CpGBP = "no" keep.GC = "no" SCALE = "no" #create objects into whcih i will store the binsizes # make a loop here to get all the species' names s1name <- paste("count_tables/",spec1, "_AllBinCounts.txt", sep = "") s2name <- paste("count_tables/",spec2, "_AllBinCounts.txt", sep = "") s3name <- paste("count_tables/",spec3, "_AllBinCounts.txt", sep = "") s4name <- paste("count_tables/",spec4, "_AllBinCounts.txt", sep = "") s5name <- paste("count_tables/",spec5, "_AllBinCounts.txt", sep = "") s1 <- read.table(s1name, header = TRUE) s2 <- read.table(s2name, header = TRUE) s3 <- read.table(s3name, header = TRUE) s4 <- read.table(s4name, header = TRUE) s5 <- read.table(s5name, header = TRUE) slist <- list(s1,s2,s3,s4,s5) for(i in seq(along=slist)){ count <- slist[[i]] count <- count[count$Known >= bin.size,] count$GC <- count$GC/count$Known*100 count[,5:(length(count)-2)] <- ((count[,5:(length(count)-2)]/count$Known) * mb) if(keep.NGenes == "no"){count <- count[,!(colnames(count) == "NGenes")]} if(keep.NG4s == "no"){count <- count[,!(colnames(count) == "NG4s")]} if(keep.NCpGI == "no"){count <- count[,!(colnames(count) == "NCpGI")]} if(keep.CpGBP == "no"){count <- count[,!(colnames(count) == "CpGBP")]} if(keep.GC == "no"){count <- count[,!(colnames(count) == "GC")]} if(SCALE == "yes"){count[,5:(length(count)-1)] <- scale(count[,5:(length(count)-1)])} #count <- count[,!(colnames(count) == "Known")] colnames(count)[1:4] <- c("chr", "binID", "start", "end") count$binID <- 1:dim(count)[1] slist[[i]] <- count } KnownS1 <- data.frame(slist[[1]]$binID, slist[[1]]$Known) KnownS2 <- data.frame(slist[[2]]$binID, slist[[2]]$Known) KnownS3 <- data.frame(slist[[3]]$binID, slist[[3]]$Known) KnownS4 <- data.frame(slist[[4]]$binID, slist[[4]]$Known) KnownS5 <- data.frame(slist[[5]]$binID, slist[[5]]$Known) s1 <- slist[[1]][,!(colnames(slist[[1]]) == "Known")] s2 <- slist[[2]][,!(colnames(slist[[2]]) == "Known")] s3 <- slist[[3]][,!(colnames(slist[[3]]) == "Known")] s4 <- slist[[4]][,!(colnames(slist[[4]]) == "Known")] s5 <- slist[[5]][,!(colnames(slist[[5]]) == "Known")] if(rem.un == "yes"){ if(length(grep("U", s1$chr)) > 0){s1 <- s1[-(grep( "U", s1$chr)),]} if(length(grep("_", s1$chr)) > 0){s1 <- s1[-(grep("_", s1$chr)),]} if(length(grep("M", s1$chr)) > 0){s1 <- s1[-(grep("M", s1$chr)),]} } if(rem.un == "yes"){ if(length(grep("U", s2$chr)) > 0){s2 <- s2[-(grep( "U", s2$chr)),]} if(length(grep("_", s2$chr)) > 0){s2 <- s2[-(grep("_", s2$chr)),]} if(length(grep("M", s2$chr)) > 0){s2 <- s2[-(grep("M", s2$chr)),]} } if(rem.un == "yes"){ if(length(grep("U", s3$chr)) > 0){s3 <- s3[-(grep( "U", s3$chr)),]} if(length(grep("_", s3$chr)) > 0){s3 <- s3[-(grep("_", s3$chr)),]} if(length(grep("M", s3$chr)) > 0){s3 <- s3[-(grep("M", s3$chr)),]} } if(rem.un == "yes"){ if(length(grep("U", s4$chr)) > 0){s4 <- s4[-(grep( "U", s4$chr)),]} if(length(grep("_", s4$chr)) > 0){s4 <- s4[-(grep("_", s4$chr)),]} if(length(grep("M", s4$chr)) > 0){s4 <- s4[-(grep("M", s4$chr)),]} } if(rem.un == "yes"){ if(length(grep("U", s5$chr)) > 0){s5 <- s5[-(grep( "U", s5$chr)),]} if(length(grep("_", s5$chr)) > 0){s5 <- s5[-(grep("_", s5$chr)),]} if(length(grep("M", s5$chr)) > 0){s5 <- s5[-(grep("M", s5$chr)),]} } human <- s1 horse <- s2 bovine <- s3 dog <- s4 mouse <- s5 keep.human <- KnownS1[KnownS1[,2] > keep.limit,1] keep.horse <- KnownS1[KnownS2[,2] > keep.limit,1] keep.bovine <- KnownS1[KnownS3[,2] > keep.limit,1] keep.dog <- KnownS1[KnownS4[,2] > keep.limit,1] keep.mouse <- KnownS2[KnownS5[,2] > keep.limit,1] human <- human[human$binID %in% keep.human,] horse <- horse[horse$binID %in% keep.horse,] bovine <- bovine[bovine$binID %in% keep.bovine,] dog <- dog[dog$binID %in% keep.dog,] mouse <- mouse[mouse$binID %in% keep.mouse,] pca.human <- prcomp(sqrt(human[,5:ncol(human)]), scale.=T, center=T) pca.horse <- prcomp(sqrt(horse[,5:ncol(horse)]), scale.=T, center=T) pca.bovine <- prcomp(sqrt(bovine[,5:ncol(bovine)]), scale.=T, center=T) pca.dog <- prcomp(sqrt(dog[,5:ncol(dog)]), scale.=T, center=T) pca.mouse <- prcomp(sqrt(mouse[,5:ncol(mouse)]), scale.=T, center=T) ### fix the above code #### but can we chnage the colours of our arrows ? so we can highlight the important principal components # now we can write whatever we want to set the colors. # we can make the other names smaller ycols_human <- rep("black", nrow(pca.human$rotation)) ycols_human[rownames(pca.human$rotation) == "SINE2_MIR"] = "blue" ycols_human[rownames(pca.human$rotation) == "LINE_L2"] = "blue" ycols_human[rownames(pca.human$rotation) == "LINE_L1"] = "red" ycols_human[grep("SINE1_7SL",rownames(pca.human$rotation))] = "darkgreen" ycols_human[rownames(pca.human$rotation) == "NGenes"] = "purple" y_cex_human = rep(.9, nrow(pca.human$rotation)) y_cex_human[ycols_human == "black"] =.7 ycols_horse <- rep("black", nrow(pca.horse$rotation)) ycols_horse[rownames(pca.horse$rotation) == "SINE2_MIR"] = "blue" ycols_horse[rownames(pca.horse$rotation) == "LINE_L2"] = "blue" ycols_horse[rownames(pca.horse$rotation) == "LINE_L1"] = "red" ycols_horse[grep("ERE",rownames(pca.horse$rotation))] = "darkgreen" ycols_horse[rownames(pca.horse$rotation) == "NGenes"] = "purple" y_cex_horse = rep(.9, nrow(pca.horse$rotation)) y_cex_horse[ycols_horse == "black"] =.7 ycols_bovine <- rep("black", nrow(pca.bovine$rotation)) ycols_bovine[rownames(pca.bovine$rotation) == "SINE2_MIR"] = "blue" ycols_bovine[rownames(pca.bovine$rotation) == "LINE_L2"] = "blue" ycols_bovine[rownames(pca.bovine$rotation) == "LINE_L1"] = "red" ycols_bovine[grep("BOV",rownames(pca.bovine$rotation))] = "darkgreen" ycols_bovine[grep("ov",rownames(pca.bovine$rotation))] = "darkred" ycols_bovine[rownames(pca.bovine$rotation) == "NGenes"] = "purple" y_cex_bovine = rep(.9, nrow(pca.bovine$rotation)) y_cex_bovine[ycols_bovine == "black"] =.7 ycols_dog <- rep("black", nrow(pca.dog$rotation)) ycols_dog[rownames(pca.dog$rotation) == "SINE2_MIR"] = "blue" ycols_dog[rownames(pca.dog$rotation) == "LINE_L2"] = "blue" ycols_dog[rownames(pca.dog$rotation) == "LINE_L1"] = "red" ycols_dog[grep("Can",rownames(pca.dog$rotation))] = "darkgreen" ycols_dog[rownames(pca.dog$rotation) == "NGenes"] = "purple" y_cex_dog = rep(.9, nrow(pca.dog$rotation)) y_cex_dog[ycols_dog == "black"] =.7 ycols_mouse <- rep("black", nrow(pca.mouse$rotation)) ycols_mouse[rownames(pca.mouse$rotation) == "SINE2_MIR"] = "blue" ycols_mouse[rownames(pca.mouse$rotation) == "LINE_L2"] = "blue" ycols_mouse[rownames(pca.mouse$rotation) == "LINE_L1"] = "red" ycols_mouse[rownames(pca.mouse$rotation) == "NGenes"] = "purple" ycols_mouse[grep("7SL",rownames(pca.mouse$rotation))] = "darkgreen" y_cex_mouse = rep(.9, nrow(pca.mouse$rotation)) y_cex_mouse[ycols_mouse == "black"] =.7 pdf(file = "~/Desktop/GSA_poster/plots_pca.pdf", onefile=T) reuben.biplot(pca.human$x[,c(1,2)], pca.human$rotation[,c(1,2)], x.col= "grey", y.col=ycols_human, text.col=ycols_human, text.cex=y_cex_human, arrow.lwd=y_cex_human*2.5) legend("bottomright",legend=spec1, bty="n", cex=1.5) # green needs to go up df.x <- pca.horse$x[,c(1,2)] df.y <- pca.horse$rotation[,c(1,2)] df.x[,2] = df.x[,2] * -1 df.y[,2] = df.y[,2] * -1 reuben.biplot(df.x[,c(1,2)], df.y[,c(1,2)], x.col= "grey", y.col=ycols_horse, text.col=ycols_horse, text.cex=y_cex_horse, arrow.lwd=y_cex_horse*2.5) legend("bottomright",legend=spec2, bty="n", cex=1.5) # blue needs to turn around df.x <- pca.bovine$x[,c(1,2)] * -1 df.y <- pca.bovine$rotation[,c(1,2)] * -1 reuben.biplot(df.x[,c(1,2)], df.y[,c(1,2)], x.col= "grey", y.col=ycols_bovine, text.col=ycols_bovine, text.cex=y_cex_bovine, arrow.lwd=y_cex_bovine*2.5) legend("bottomright",legend=spec3, bty="n", cex=1.5) reuben.biplot(pca.dog$x[,c(1,2)], pca.dog$rotation[,c(1,2)], x.col= "grey", y.col=ycols_dog, text.col=ycols_dog, text.cex=y_cex_dog, arrow.lwd=y_cex_dog*2.5) legend("bottomright",legend=spec4, bty="n", cex=1.5) #green needs to go up df.x <- pca.mouse$x[,c(1,2)]*-1 df.y <- pca.mouse$rotation[,c(1,2)] *-1 reuben.biplot(df.x[,c(1,2)], df.y[,c(1,2)], x.col= "grey", y.col=ycols_mouse, text.col=ycols_mouse, text.cex=y_cex_mouse, arrow.lwd=y_cex_mouse*2.5) legend("bottomright",legend=spec5, bty="n", cex=1.5) dev.off() # write it that i can get every pca image i need # with the correct features pointed out pdf(file = "~/Desktop/GSA_poster/legend.pdf") plot(NA) legend("bottomleft", c("Ancestral element", "Clade specific SINE", "Number of genes", "L1", "BovB" ), fill = c("blue", "darkgreen", "purple", "red", "darkred"), cex=2, box.col = "white") dev.off() ##### so lets turn them te right way around and colour them in # so these are the pca plots of the names of things pca.2 <- prcomp(t(scale(dog[,5:ncol(dog)])), scale.=TRUE) plot(pca.2$x, cex = .5) text(pca.2$x, labels=rownames(pca.2$x)) my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 19) my_p_val_palette <- colorRampPalette(c( "white", "black"))(n = 19) pv <- seq(1,0, by = -.1) pv.text <- rep("", length(pv)) pv.text[as.integer(quantile(1:(length(pv))))] <- as.character(pv[as.integer(quantile(1:(length(pv))))]) # now i can get the row side colours to give me a p,val heatmap.2(matrix(seq(0,1, length.out = 100),nrow=10),scale="none", col = my_p_val_palette,dendrogram="n",keysize=2, # symkey = TRUE, breaks = seq(from = 0, to = 1, length = 20), trace = "none", # symbreaks = TRUE, density.info = "none", key.title = "", key.xlab = "P-value" ) heatmap.2(matrix(seq(-1.01,1.01, length.out = 100),nrow=10),scale="none", col = my_palette,dendrogram="n",keysize=2, # symkey = TRUE, breaks = seq(from = -1, to = 1, length = 20), trace = "none", # symbreaks = TRUE, density.info = "none", key.title = "", key.xlab = "Pearson's r" ) pdf(file = "~/Desktop/GSA_poster/keys.pdf", onefile = T) layout(matrix(1:3,ncol=3), width = c(1,1,1),height = c(1,1,1)) legend_image <- as.raster(my_palette[19:1]) plot(c(0,2),c(-1,1),type = 'n', axes = F,xlab = '', ylab = '', main = "Pearson's r", cex.main=2) text(x=1.5, y = c(1,0.5,0,-0.5, -1), label = c(1,0.5,0,-0.5, -1), cex = 2) rasterImage(legend_image, 0, -1, 1,1) plot(1:20, 1:20, pch = 19, cex=2, col = colfunc(20)) plot(1:20, 1:20, pch = 19, cex=2, col = colfunc(20)) legend_image <- as.raster(my_p_val_palette[19:1]) plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = "P_value", cex.main=2) text(x=1.5, y = c(0,0.2,.4,.6,.8, 1), label = c(0,0.2,.4,.6,.8, 1), cex = 2) rasterImage(legend_image, 0, 0, 1,1) dev.off() # I think this script has what I'm looking for # hoefully for each of these species we have already converted them over.

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/analyses/scripts/FSI_sitesRisk.R

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cchambe12/freezingexperiment

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FSI_sitesRisk.R

## 31 August 2017 # working on weather data to try and evaluate FSI across 4 field sites # Clear workspace rm(list=ls()) # remove everything currently held in the R memory options(stringsAsFactors=FALSE) graphics.off() # Load libraries library(dplyr) library(tidyr) library(ggplot2) library(lubridate) # Set Working Directory setwd("~/Documents/git/freezingexperiment/analyses/input") d<-read.csv("weather_allsites.csv", header=TRUE) d<-dplyr::rename(d, doy=Julian.Date) d$year<-substr(d$Date, 7, 10) ### Harvard Forest ##### hf<-d%>%filter(site=="hf")%>%filter(year>=1990) hf<-hf %>% filter(doy <=240) hf$gdd <- hf$AirTMax - 5 # Can be 0 here if want 0 degC as threshold hf$gdd <-ifelse(hf$gdd>0, hf$gdd, 0) hf$count <- ave( hf$gdd, hf$year, FUN=function(x) cumsum(c(0, head(x, -1))) ) hf$frz<- ifelse((hf$AirTMin<=-2.2), 1, 0) hf$fs<- ifelse((hf$count >= 200 & hf$frz == 1 & hf$count<=300), TRUE, NA) hf.fsi<-hf%>%dplyr::select(year, fs) hf.fsi<-hf.fsi[!duplicated(hf.fsi),] hf.fsi<-na.omit(hf.fsi) ### White Mountains ##### wm<-d%>%filter(site=="bart")%>%filter(year>=1990) wm<-wm %>% filter(doy <=240) wm$gdd <- wm$AirTMax - 5 # Can be 0 here if want 0 degC as threshold wm$gdd <-ifelse(wm$gdd>0, wm$gdd, 0) wm$count <- ave( wm$gdd, wm$year, FUN=function(x) cumsum(c(0, head(x, -1))) ) wm$frz<- ifelse((wm$AirTMin<=-2.2), 1, 0) wm$fs<- ifelse((wm$count >= 200 & wm$frz == 1 & wm$count<=300), TRUE, NA) wm.fsi<-wm%>%dplyr::select(year, fs) wm.fsi<-wm.fsi[!duplicated(wm.fsi),] wm.fsi<-na.omit(wm.fsi) ### Grant ##### Need to find average because two stations... gr<-d%>%filter(site=="berlin")%>%filter(year>=1990) gr<-gr %>% filter(doy <=240) gr$gdd <- gr$AirTMax - 5 # Can be 0 here if want 0 degC as threshold gr$gdd <-ifelse(gr$gdd>0, gr$gdd, 0) gr$count <- ave( gr$gdd, gr$year, FUN=function(x) cumsum(c(0, head(x, -1))) ) gr$frz<- ifelse((gr$AirTMin<=-2.2), 1, 0) gr$fs<- ifelse((gr$count >= 200 & gr$frz == 1 & gr$count<=300), TRUE, NA) gr.fsi<-gr%>%dplyr::select(year, fs) gr.fsi<-gr.fsi[!duplicated(gr.fsi),] gr.fsi<-na.omit(gr.fsi) gr1<-d%>%filter(site=="merr")%>%filter(year>=1990) gr1<-gr1 %>% filter(doy <=240) gr1$gdd <- gr1$AirTMax - 5 # Can be 0 here if want 0 degC as threshold gr1$gdd <-ifelse(gr1$gdd>0, gr1$gdd, 0) gr1$count <- ave( gr1$gdd, gr1$year, FUN=function(x) cumsum(c(0, head(x, -1))) ) gr1$frz<- ifelse((gr1$AirTMin<=-2.2), 1, 0) gr1$fs<- ifelse((gr1$count >= 200 & gr1$frz == 1 & gr1$count<=300), TRUE, NA) gr1.fsi<-gr1%>%dplyr::select(year, fs) gr1.fsi<-gr1.fsi[!duplicated(gr1.fsi),] gr1.fsi<-na.omit(gr1.fsi) gr2<-d%>%filter(site=="ct")%>%filter(year>=1990) gr2<-gr2 %>% filter(doy <=240) gr2$gdd <- gr2$AirTMax - 5 # Can be 0 here if want 0 degC as threshold gr2$gdd <-ifelse(gr2$gdd>0, gr2$gdd, 0) gr2$count <- ave( gr2$gdd, gr2$year, FUN=function(x) cumsum(c(0, head(x, -1))) ) gr2$frz<- ifelse((gr2$AirTMin<=-2.2), 1, 0) gr2$fs<- ifelse((gr2$count >= 200 & gr2$frz == 1 & gr2$count<=300), TRUE, NA) gr2.fsi<-gr2%>%dplyr::select(year, fs) gr2.fsi<-gr2.fsi[!duplicated(gr2.fsi),] gr2.fsi<-na.omit(gr2.fsi) ### Saint Hipp ##### sh<-d%>%filter(site=="sh")%>%filter(year>=1990) sh<-sh %>% filter(doy <=240) sh$gdd <- sh$AirTMax - 5 # Can be 0 here if want 0 degC as threshold sh$gdd <-ifelse(sh$gdd>0, sh$gdd, 0) sh$count <- ave( sh$gdd, sh$year, FUN=function(x) cumsum(c(0, head(x, -1))) ) sh$frz<- ifelse((sh$AirTMin<=-2.2), 1, 0) sh$fs<- ifelse((sh$count >= 200 & sh$frz == 1 & sh$count<=300), TRUE, NA) sh.fsi<-sh%>%dplyr::select(year, fs) sh.fsi<-sh.fsi[!duplicated(sh.fsi),] sh.fsi<-na.omit(sh.fsi) ############### Find Last Freeze Dates for each site..... ## Harvard Forest ## hf.last<-hf%>%filter(frz>0) hf.last<-hf.last[order(hf.last$doy,hf.last$year),] hf.last<-hf.last[!duplicated(hf.last$year, fromLast=TRUE),] hf.last$last<-hf.last$Date #write.csv(hf.last, file="~/Documents/git/freezingexperiment/analyses/output/hf_fsi.csv", row.names = FALSE) ## White Mountains ## wm.last<-wm%>%filter(frz>0) wm.last<-wm.last[order(wm.last$doy,wm.last$year),] wm.last<-wm.last[!duplicated(wm.last$year, fromLast=TRUE),] wm.last$last<-wm.last$Date ## Grant(s)... # 1: gr.last<-gr%>%filter(frz>0) gr.last<-gr.last[order(gr.last$doy,gr.last$year),] gr.last<-gr.last[!duplicated(gr.last$year, fromLast=TRUE),] gr.last$last<-gr.last$Date # 2: gr1.last<-gr1%>%filter(frz>0) gr1.last<-gr1.last[order(gr1.last$doy,gr1.last$year),] gr1.last<-gr1.last[!duplicated(gr1.last$year, fromLast=TRUE),] gr1.last$last1<-gr1.last$Date # 3: gr2.last<-gr2%>%filter(frz>0) gr2.last<-gr2.last[order(gr2.last$doy,gr2.last$year),] gr2.last<-gr2.last[!duplicated(gr2.last$year, fromLast=TRUE),] gr2.last$last2<-gr2.last$Date gr.last<-full_join(gr.last, gr1.last) gr.last<-full_join(gr.last, gr2.last) ## Saint Hipp ## sh.last<-sh%>%filter(frz>0) sh.last<-sh.last[order(sh.last$doy,sh.last$year),] sh.last<-sh.last[!duplicated(sh.last$year, fromLast=TRUE),] sh.last$last<-sh.last$Date

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katenambiar/pmpa

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bmedian-methods.Rd

% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/accessor-methods.R \docType{methods} \name{bmedian} \alias{bmedian} \alias{bmedian,MultiSet-method} \title{Median Background Intensity Accessor} \usage{ bmedian(x) \S4method{bmedian}{MultiSet}(x) } \arguments{ \item{x}{MultiSet object} } \value{ matrix of median background intensities } \description{ Extracts the matrix of median background intensities (bMedian) from a MultiSet object created by \link{readArrays} }

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ElderResearch/DAPM

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set_param_num.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{set_param_num} \alias{set_param_num} \title{Assign a parameter or optionally fill from NRDB} \usage{ set_param_num(...) } \arguments{ \item{...}{Arguments to pass to \code{set_param}.} } \value{ A numeric. } \description{ Assign a parameter or optionally fill from NRDB } \examples{ \dontrun{ i <- set_param_num(param = "K", con = db_conn$conn, input = k) } }

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## Including the required R packages. #packages <- c('shiny', 'shinyjs') #if (length(setdiff(packages, rownames(installed.packages()))) > 0) { # install.packages(setdiff(packages, rownames(installed.packages()))) #} library(shiny) library(shinyjs) shinyUI(fluidPage( conditionalPanel(condition='!output.json', tags$head(tags$script(src = "script.js"), tags$script(src = "google-analytics.js"), tags$style(HTML("a { font-weight: bold; } .shiny-output-error-validation { color: red; } .shiny-progress .progress { background-color: #ff00ff; } .fa-info { margin: 0 0 0 10px; cursor: pointer; font-size: 15px; color: #808080; } .fa-headphones { margin: 0 5px 0 2px; } .average-pitch { font-size: 18px; } .detail-summary { font-size: 16px; } .detail-summary .detail-header { font-size: 18px; margin: 0 0 10px 0; } .detail-summary span { font-weight: bold; }")) ), titlePanel('Cual es tu genero segun tu voz?'), div(style='margin: 30px 0 0 0;'), mainPanel(width = '100%', useShinyjs(), h4(id='main', 'Sube un archivo .WAV or escribe un url de ', a(href='http://vocaroo.com', target='_blank', 'vocaroo.com'), ' o ', a(href='http://clyp.it', target='_blank', 'clyp.it'), ' para detectar el genero.'), div(style='margin: 20px 0 0 0;'), inputPanel( div(id='uploadDiv', class='', style='height: 120px; border-right: 1px solid #ccc;', fileInput('file1', 'Subir Archivo WAV.', accept = c('audio/wav'), width = '100%') ), div(id='urlDiv', class='', strong('Url (vocaroo o clyp.it)'), textInput('url', NULL, width = '100%'), actionButton('btnUrl', 'Cargar Url', class='btn-primary', icon=icon('cloud')) ) ), div(style='margin: 20px 0 0 0;'), div(id='result', style='font-size: 22px;', htmlOutput('content')), div(style='margin: 20px 0 0 0;'), conditionalPanel(condition='output.content != null && output.content.indexOf("Please Enter") == -1', tabsetPanel(id='graphs', tabPanel('Frequency Graph', plotOutput("graph1", width=1000, height=500)), tabPanel('Spectrogram', plotOutput("graph2", width=1000, height=500)) ), div(style='margin: 20px 0 0 0;') ), h4('Truquitos'), p('- La entonación y tonos son factores importantes en la clasificacion hombre/mujer.'), p('- Voces clasificadas como masculinas suelen ser monotonas(tono bajo).'), p('- Voces clasificadas como femeninas tienden a ser de tono alto y de cambios de frequencia alta.'), p('- Voces clasificadas como femeninas suelen aumentar en tono al final de una oración.'), div(style='margin: 20px 0 0 0;'), span(style='font-style: italic;', 'Forkeado de https://github.com/primaryobjects/voice-gender') )) ))

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/waves/waveHspVCyto.R

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Danko-Lab/polymeraseWaves

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waveHspVCyto.R

## ## callWaves.R -- identify the position of the leading edge of the wave of Pol II as it moves ## across gene bodies. ## require(groHMM) require(bigWig) source("../polymeraseWave.bw.R") ## BigWig files. pth <- "/home/cgd24/storage/home/work/polymeraseWaves/data/bigWigs/" wtNHSpl <- paste(pth,"WT_NHS_BRs_pl.bigWig",sep="") wtNHSmn <- paste(pth,"WT_NHS_BRs_mn.bigWig",sep="") wt144pl <- paste(pth,"WT_144sHS_BRs_pl.bigWig",sep="") wt144mn <- paste(pth,"WT_144sHS_BRs_mn.bigWig",sep="") wt12pl <- paste(pth,"WT_12HS_BRs_pl.bigWig",sep="") wt12mn <- paste(pth,"WT_12HS_BRs_mn.bigWig",sep="") koNHSpl <- paste(pth,"Hsf1KO_NHS_BRs_pl.bigWig",sep="") koNHSmn <- paste(pth,"Hsf1KO_NHS_BRs_mn.bigWig",sep="") ko144pl <- paste(pth,"Hsf1KO_144sHS_BRs_pl.bigWig",sep="") ko144mn <- paste(pth,"Hsf1KO_144sHS_BRs_mn.bigWig",sep="") ko12pl <- paste(pth,"Hsf1KO_12HS_BRs_pl.bigWig",sep="") ko12mn <- paste(pth,"Hsf1KO_12HS_BRs_mn.bigWig",sep="") ## Bed files. bedPth <- "/home/cgd24/storage/home/work/polymeraseWaves/data/beds/" readBed <- function(filename, minSize) { dataf <- read.table(filename) dataf <- dataf[(dataf[,3]-dataf[,2]) > minSize,] dataf } cleanup <- function(f.d) { # f.d <- f.d.list[[NROW(f.d.list)]] ## ONLY used for alldata f.d[f.d$minOfMax & f.d$minOfAvg & f.d$KLdivParametric > 1,] } ################################# ## Compare wildtype and HSF1ko minSize=20000 Hsp_bed <- readBed(paste(bedPth, "HspGenes_mm10.bed", sep=""), minSize=minSize)[,c(1:3,6,4:5)] Cyt_bed <- readBed(paste(bedPth, "CytoskeletonGenes_UpHC_WT-144sHS_unique.bed", sep=""), minSize=minSize)[,c(1:3,6,4:5)] approx=5000 hsp_144 <- polymeraseWaveBW(wt144pl, wt144mn, wtNHSpl, wtNHSmn, Hsp_bed, TSmooth= 20, approxDist=approx, returnVal="simple", prefix="IMG/Hsp.") cyt_144 <- polymeraseWaveBW(wt144pl, wt144mn, wtNHSpl, wtNHSmn, Cyt_bed, TSmooth= 20, approxDist=approx, returnVal="simple", prefix="IMG/Cyt.") pdf("Hsf_Cyto.144s.pdf") hist(cleanup(hsp_144)$Rate, breaks=seq(0,40000,3000)) hist(cleanup(cyt_144)$Rate, breaks=seq(0,40000,3000)) indx_hsp <- hsp_144$minOfMax & hsp_144$minOfAvg & hsp_144$KLdivParametric > 1 indx_cyt <- cyt_144$minOfMax & cyt_144$minOfAvg & cyt_144$KLdivParametric > 1 hsp.cdf <- ecdf(hsp_144$Rate[indx_hsp]) cyt.cdf <- ecdf(cyt_144$Rate[indx_cyt]) plot(hsp.cdf, col="dark red", xlim=c(0, 40000), ylim=c(0,1)) par(new=TRUE) plot(cyt.cdf, col="black", xlim=c(0, 40000), ylim=c(0,1)) ks.test(hsp_144$Rate[indx_hsp], cyt_144$Rate[indx_cyt]) ## NOT significant. boxplot(hsp_144$Rate[indx_hsp], cyt_144$Rate[indx_cyt], names=c("HSF1KO", "WT")) wilcox.test(hsp_144$Rate[indx_hsp], cyt_144$Rate[indx_cyt]) dev.off() ## Write out data... save.image("cyto_hsf.RData") write.table(cbind(Hsp_bed, hsp_144), "HSP.144s.tsv", sep="\t", quote=FALSE, row.names=FALSE) write.table(cbind(Cyt_bed, cyt_144), "CYT.144s.tsv", sep="\t", quote=FALSE, row.names=FALSE)

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/gbd_2019/nonfatal_code/encephalitis/nonfatal_code/02b_parallel.R

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Nermin-Ghith/ihme-modeling

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02b_parallel.R

##################################################################################################################################################################################### ## Purpose: This sub-step template is for parallelized jobs submitted from main step code ## Author: ## Last updated: 2019/02/27 ## Description: Parallelization of 02b_acute_survive ##################################################################################################################################################################################### rm(list=ls()) # LOAD SETTINGS FROM MASTER CODE (NO NEED TO EDIT THIS SECTION) ---------------- # Load functions and packages library(argparse) library(data.table) # Get arguments from parser parser <- ArgumentParser() parser$add_argument("--date", help = "timestamp of current run (i.e. 2014_01_17)", default = NULL, type = "character") parser$add_argument("--step_num", help = "step number of this step (i.e. 01a)", default = NULL, type = "character") parser$add_argument("--step_name", help = "name of current step (i.e. first_step_name)", default = NULL, type = "character") parser$add_argument("--code_dir", help = "code directory", default = NULL, type = "character") parser$add_argument("--in_dir", help = "directory for external inputs", default = NULL, type = "character") parser$add_argument("--out_dir", help = "directory for this steps checks", default = NULL, type = "character") parser$add_argument("--tmp_dir", help = "directory for this steps intermediate draw files", default = NULL, type = "character") parser$add_argument("--root_j_dir", help = "base directory on J", default = NULL, type = "character") parser$add_argument("--root_tmp_dir", help = "base directory on clustertmp", default = NULL, type = "character") parser$add_argument("--ds", help = "specify decomp step", default = 'step1', type = "character") args <- parser$parse_args() print(args) list2env(args, environment()); rm(args) # Get location from parameter map task_id <- as.integer(Sys.getenv("SGE_TASK_ID")) parameters <- fread(file.path(code_dir, paste0(step_num, "_parameters.csv"))) location <- parameters[task_id, location_id] # User specified options ------------------------------------------------------- # Source GBD 2019 shared functions k <- # filepath source(paste0(k, "get_draws.R")) source(paste0(k, "get_demographics.R")) # pull demographics from RDS created in model_custom demographics <- readRDS(file.path(in_dir,"demographics_temp.rds")) years <- demographics$year_id sexes <- demographics$sex_id ages <- demographics$age_group_id functional <- "encephalitis" parent_meid <- 1419 # Inputs ----------------------------------------------------------------------- pull_dir_02a <- file.path(root_tmp_dir, "02a_cfr_draws","03_outputs", "01_draws") # Run job ---------------------------------------------------------------------- draws <- get_draws(gbd_id_type = "modelable_entity_id", source = "epi", gbd_id = parent_meid, age_group_id = ages, measure_id = 6, location_id = location, gbd_round_id = 6, decomp_step = ds) setDT(draws) draws[, c("model_version_id", "measure_id"):= NULL] for (y in years) { for (s in sexes) { survive <- readRDS(file.path(pull_dir_02a,paste0("dm-", functional, "-survive-", location, "_", y, "_", s, ".rds"))) draws.tmp <- draws[year_id == y & sex_id ==s] merge.dt <- merge(draws.tmp, survive, by=c("year_id", "sex_id", "age_group_id", "location_id")) # survival rate * incidence draws = survival rate of acute phase merge.dt[, paste0("draw_", 0:999):= lapply(0:999, function(x){get(paste0("draw_",x)) * get(paste0("v_",x))})] cols.remove <- paste0("v_", 0:999) merge.dt[, (cols.remove):= NULL] filename <- paste0("survive_", location, "_", y, "_", s, ".rds") saveRDS(merge.dt, file.path(tmp_dir, "03_outputs", "01_draws", filename)) } } # CHECK FILES (NO NEED TO EDIT THIS SECTION) ----------------------------------- file.create(paste0(tmp_dir, "finished_loc", location, ".txt"), overwrite=T)

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lorenzgerber/tofsims

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plot-PeakList-missing-method.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PeakList.R \name{plot,PeakList,missing-method} \alias{plot,PeakList,missing-method} \title{Method \code{plot()} for \code{MassSpectra}} \usage{ \S4method{plot}{PeakList,missing}( x, y, ..., mzRange = c(0, 200), plotDeriv = FALSE, plotPeaks = TRUE, plotWidths = TRUE ) } \arguments{ \item{x}{object of type PeakList} \item{y}{missing} \item{...}{further args} \item{mzRange}{vector or length two, indicating the mz range to be plotted} \item{plotDeriv}{boolean plot derivate if available} \item{plotPeaks}{boolean plot peaks if available} \item{plotWidths}{boolean plot peak widths if available} } \value{ plot spectra with peaks and peak widths } \description{ Method defining \code{plot()} for the \code{MassSpectra} class plot has no generic by default } \details{ The output of this method is adapted for plotting mass spectra. Uncalibrated data is plotted as xy plot while uncalibrated data is plotted as barplot. The parameter \code{mzRange} allows choosing the plot range directly according to the mz number (when calibrated). }

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makefun.R

`makefun` <- function (.thefun, .thealist, .moveddd = 1, ...) { frms <- formals(.thefun) frn <- names(frms) body <- body(.thefun) na <- length(frms) ddd <- names(frms)[na] == "..." add.ddd <- F if (ddd && .moveddd != 0) { na <- na - 1 frms <- frms[1:na] frn <- frn[1:na] add.ddd <- T } if (.moveddd != 1) add.ddd <- F if (is.null(names(.thealist))) { tmp <- rev(rev(names(frms))[1:min(length(.thealist), na)]) names(.thealist)[1:length(tmp)] <- tmp } frms[names(.thealist)] <- .thealist if (add.ddd) frms <- c(frms, alist(... = )) return(as.function(c(frms, body), ...)) }

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/man/summary.binomialbcp.Rd

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svana1/binomialbcp

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summary.binomialbcp.Rd

% Generated by roxygen2 (4.1.0): do not edit by hand % Please edit documentation in R/binomialbcp.R \name{summary.binomialbcp} \alias{summary.binomialbcp} \title{Summarize a Binomial BCP sample} \usage{ \method{summary}{binomialbcp}(object, ...) } \arguments{ \item{object}{a binomialbcp sample} \item{...}{(unused)} } \value{ a dataframe containing the probability of a change point and estimated proportion for each time point. } \description{ Average over the MCMC samples to estimate changepoints and proportions. }

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/NLCD_script.R

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schefferslab/connectivity_SEUSA

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NLCD_script.R

library(readr) library(maptools) library(sp) library(raster) library(rgdal) library(maps) library(ggplot2) library(tidyverse) library(smoothr) library(devtools) library(ENMTools) library(spatstat) library(rgeos) library(lattice) library(reshape2) library(ggthemes) library(viridis) library(gridExtra) library(rasterVis) library(spatialEco) library(dismo) rm(list = ls()) setwd("C:/Users/jbaecher/Dropbox (UFL)/UF/Scheffers lab/Projects/Landscape_prioritization") HSI <- read_rds("C:/Users/jbaecher/Dropbox (UFL)/UF/Scheffers lab/Projects/Landscape_prioritization/HSI_D_aur.rds") raster <- read_rds("data/circuitscape/se_raster_stack.rds") %>% crop(HSI) %>% mask(HSI) proj4string <- crs(raster) plot(raster) # write_rds(D_aur_spdf, "D_aur_spdf.rds") # write_rds(D_aur_xy, "D_aur_xy.rds") D_aur_spdf <- read_rds("D_aur_spdf.rds") # Load GBIF data (precleaned), spdf version D_aur_xy <- read_rds("D_aur_xy.rds") # Load df version D_aur_coords_decade <- split(cbind(D_aur_xy[,c('lon','lat')]), f = D_aur_xy$decade) # Split occurrence data by decade names(D_aur_coords_decade[2:4]) <- names(D_aur_coords_decade[1]) ############# NLCD processing ############# # Original values: # 1: Open Water # 2: Urban/Developed # 3: Intentionally Left Blank # 4: Intentionally Left Blank # 5: Intentionally Left Blank # 6: Mining # 7: Barren # 8: Deciduous Forest # 9: Evergreen Forest # 10: Mixed Forest # 11: Grassland # 12: Shrubland # 13: Cultivated Cropland # 14: Hay/Pasture # 15: Herbaceous Wetland # 16: Woody Wetland # 17: Perennial Ice/Snow # Reclassify rasters based on resistance to salamander movement ## First, create reclassification matrix reclassify <- matrix( c( 0, 1, 7, #Open water 7 1, 6, 6, #Developed 6 6, 7, 5, #Barren 5 7, 10, 1, #Forest 1 10, 12, 3, #Grassland/Shrubland 3 12, 14, 4, #Planted/cultivated cropland 4 14, 16, 2, #Wetlands 2 16, Inf, NA), #Ice/Snow (none in SE) NA ncol=3, byrow=T) # Create labels for attribute table manipulations during raster processing nlcdclass <- c("Forest", "Wetlands", "Grass/Shrub/Herbaceous", "Planted/Cultivated", "Barren", "Developed", "Open Water") classdf <- data.frame(classvalue1 = c(1,2,3,4,5,6,7), classnames1 = nlcdclass) # Load in backcasted NLCD rasters to project, reclassify, crop, mask, and stack ## First, identify files all_rasters = list.files( path="C:/Users/jbaecher/Dropbox (UFL)/UF/Scheffers lab/Projects/Landscape_prioritization/data/NLCD/Historic", pattern = "\\.tif$", full.names = TRUE) ## Next, remove files outside study time period sub_rasters <- all_rasters[c(23:55)] sub_rasters_list <- list() decades <- c("CONUS_Backcasting_y196", "CONUS_Backcasting_y197", "CONUS_Backcasting_y198", "CONUS_Backcasting_y199") decade_layers <- list() decade_modes <- list() # Big as muthafuckin' for loop to... load, reproject, reclassify, crop, mask, stack,and calculate modes of NLCD rasters from 1960 until 1992 for (i in 1:length(sub_rasters)){ sub_rasters_list[[i]] <- raster(sub_rasters[i]) %>% # Loading rasters in from file names projectRaster(HSI) %>% # Reprojecting crop(HSI) %>% # Croping to study extent mask(HSI) %>% # Masking to study polygon reclassify(reclassify) %>% # Reclassifying habitat types based on resistance value ratify() # Reorganizing attributes table rat <- levels(sub_rasters_list[[i]]) # Setting levels of raster values rat$landcover <- nlcdclass # Assigning category names to attribute table levels(sub_rasters_list[[i]]) <- rat # Saving attribute table in raster object if(length(sub_rasters_list) == length(sub_rasters)){ # Testing if raster processing is complete print(names(sub_rasters_list[[i]])) # If raster processing is complete, printing final raster name sub_rasters_stack <- stack(sub_rasters_list) # If raster processing is complete, stacking list into a raster brick decade_layers <- lapply( # Begin function to... decades, function(x) # use a list of decades from study period... which(grepl(tolower(x), # to find raster years tolower(names(sub_rasters_stack))))) # and return those rasters to decadal groupings decade_layers[-c(5,6)] # removing unnecessary elements from list for (j in 1:length(decade_layers)){ # Begin for loop to... decade_modes[[j]] <- modal(sub_rasters_stack[[ # calculate the mode of a raster brick... decade_layers[[j]] # across layers... ]], # representing decade groupings from previous... ties="random",freq=F) # setting ties to a random outcome if(length(decade_modes) == length(decade_layers)){ # Testing if mode calculation is complete print(names(decade_modes[[j]])) # If mode calculation is complete, printing final name of calculated mode layer decade_stack <- stack(decade_modes) # If mode calculation is complete, stacking list of mode layers into a raster brick names(decade_stack) <- c("NLCD_1960_1969","NLCD_1970_1979", "NLCD_1980_1989","NLCD_1990_1992") } else( # If mode calculation is incomplete... print(names(decade_modes[[j]]))) # print progress... } } else( # If raster processing is incomplete... print(names(sub_rasters_list[[i]]))) # print progress... } barplot(decade_modes[[1]],axes=F,col=plasma(7));axis(1,labels=nlcdclass,at=c(1:7)) ################################################# Maxent ################################################# plot(decade_stack) ## # args to pass to Maxent args <- list( c("-J", "-P", "-q", "-p", "-h", "replicates=3", "randomtestpoints=27", "betamultiplier=1", "askoverwrite=false", "threads=6"), c("-J", "-P", "-q", "-p", "-h", "replicates=3", "randomtestpoints=38", "betamultiplier=1", "askoverwrite=false", "threads=6"), c("-J", "-P", "-q", "-p", "-h", "replicates=3", "randomtestpoints=8", "betamultiplier=1", "askoverwrite=false", "threads=6"), c("-J", "-P", "-q", "-p", "-h", "replicates=3", "randomtestpoints=5", "betamultiplier=1", "askoverwrite=false", "threads=6")) D_aur_maxent_list <- list() D_aur_preds_list <- list() for(k in 1:nlayers(decade_stack)){ D_aur_maxent_list[[k]] <- maxent(x=stack(decade_stack[[k]],raster), # Run MaxEnt on each decade p=coordinates(D_aur_coords_decade[[k]]), # Partition occurrence by decade args=args[[k]]) # Pass decade-specific arguments to MaxEnt if(length(D_aur_maxent_list) == nlayers(decade_stack)){ # Test if Maxent calculations are done D_aur_preds_list[[k]] <- mean(predict(D_aur_maxent_list[[k]], # If complete, calculate predictions... stack(decade_stack[[k]],raster))) # from decadal raster data... } else( # If incomplete, print(names(decade_modes[[k]]))) # print progress... } D_aur_preds_stack <- stack(D_aur_preds_list) plot(D_aur_preds_stack, zlim=c(0,1)) response(D_aur_maxent_list[[1]], var="NLCD_1960_1969")

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/rotinamod2.r

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Frank2857/Disserta-o

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rotinamod2.r

############################################################################ ###################### MODELO 2 - PRIORI NÃO INFORMATIVA ################### ############################################################################ x = as.character(dados$ANO[1:23]);x y= dados$FEV[1:23];y t0=2 #Ponto do possível inicio de tendência n=length(y) ncT=n-t0;ncT ############################################################################ # GEV COM TENDÊNCIA LINEAR sink("Gev_TREND_LINEAR_NI.txt") cat(" model { # Verossimilhança for (i in 1:n) { y[i] ~ dgev(mi[i], sigma, eta) mi[i] <- mu + beta*x[i] } # Prior # dnorm(media, precisao) mu ~ dnorm(0,0.0001) sigma ~ dnorm(0,0.0001) eta ~ dunif(-1.5, 1.5) beta ~ dunif(-5.0, 5.0) #Para Fazer preditiva para Hipóses para a beta H0=B<=B0 beta0<-0 probH0<-step(beta0-beta)# Atribui valor cero, quando 'beta0-beta' menor ou aigual a cero '0' probH1<- 1-probH0 # Complementar de H0 # Para Fazer preditiva ncT<-21 yp5 <- (mu + (beta*(ncT+5))) +((sigma/eta)*(pow(-log(1-1/5),-eta)-1)) yp10 <- (mu + (beta*(ncT+10))) +((sigma/eta)*(pow(-log(1-1/10),-eta)-1)) yp15 <- (mu + (beta*(ncT+15))) +((sigma/eta)*(pow(-log(1-1/15),-eta)-1)) yp20 <- (mu + (beta*(ncT+20))) +((sigma/eta)*(pow(-log(1-1/20),-eta)-1)) } ",fill=TRUE) sink() trend=c(rep(0,t0),seq(1,ncT)) trend dados_bug<- list(x=trend,y=y,n=length(y)) dados_bug inits <- function(){ list(mu=30, sigma=10, beta=0.01, eta=0.01)} params <- c("mu","sigma","eta","beta") nc = 1 #Numero de cadeias ni = 200000 #Tamanho da cadeira nb = 50000 #Numero de simulação que serão descartadas nt = 30 #Salto (thin) # Inicie o Amostrador gev.bayes.trend_NL2 = bugs(data = dados_bug, inits = inits, parameters =c(params,"probH0","probH1","yp5","yp10","yp15","yp20"), model = "Gev_TREND_LINEAR_NI.txt", n.thin = nt, n.chains = nc, n.burnin = nb, n.iter = ni, codaPkg=FALSE, debug=T) print(gev.bayes.trend_NL2, dig = 4) post_gb_t_NL2<-as.mcmc(gev.bayes.trend_NL2$sims.matrix[,]) #salva a saída como cadeia mcmc HPDinterval(post_gb_t_NL2) #Intervalo HPD raftery.diag(post_gb_t_NL2) geweke.diag(post_gb_t_NL2) heidel.diag(post_gb_t_NL2) par(mar=c(2,2,2,2)) plot(post_gb_t_NL2) summary(post_gb_t_NL2 ) post_gb_t_NL2[1:20,] # imprimindo os 10 primeiros valores ##################################################### # CALCULANDO MEDIDAS PARA DECISÃO ################################################## pH0=mean(post_gb_t_NL2[,5]);pH0 pH1=mean(post_gb_t_NL2[,6]);pH1 # Evidencia O_h1h0=pH1/pH0;O_h1h0 # A chance de acontecer H1 resumo2_NL2=print(gev.bayes.trend_NL2,dig=4) # salva a resumo da cadeia mcmc #Preditiva ypredl_t_NL2<-c(resumo2_NL2$mean$yp5, resumo2_NL2$mean$yp10, resumo2_NL2$mean$yp15, resumo2_NL2$mean$yp20) #salva as médias do y predito. VPtNL2 = ypredl_t_NL2;VPtNL2 obs = c(34.6, 36.8, 36.8, 37.7)#Valores observados EpGevT_NL2= abs((obs-VPtNL2)/obs) #Erro de predição round(mean(EpGevT_NL2)*100,2) #Erro médio de predição percentual.

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/new_effect_size.R

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MegaPast2Future/megalinkers

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refs/heads/master

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new_effect_size.R

new_effect_size <- function(x, y, global = T, area = 'World'){ if (str_detect(class(x), "raster|Raster")) { cont.name <- c('Asia', 'North America', 'Europe', 'Africa', 'South America', 'Oceania') if(global == F){ cont.value <- which(cont.name == area) clipped.continent <- clamp(continents, lower = cont.value, upper = cont.value, useValues = F) / cont.value x <- mask(x, clipped.continent) y <- mask(y, clipped.continent) } x <- na.omit(values(x)) y <- na.omit(values(y)) } # rank-sum test p.value <- wilcox.test(x, y)$p.value # all favorable pairs P <- sum(unlist(1:length(x) %>% map(function(i) sum(x[i] > y)))) # all unfavorable pairs N <- sum(unlist(1:length(x) %>% map(function(i) sum(x[i] < y)))) # all ties E <- sum(unlist(1:length(x) %>% map(function(i) sum(x[i] == y)))) # total Tot <- tryCatch(P + N + E, warning = function(w) { warning(w, "Coercing to numeric, results might be inaccurate") as.numeric(P) + as.numeric(N) + as.numeric(E) }) r = (P - N) / (Tot) prob = P / Tot fraction = 10^mean(y - x) return(tibble(`p` = p.value, `r` = r, `P(X > Y)` = prob, Area = area, Fraction = fraction)) } magnitude <- function(superiority){ x <- rep(NA, length(superiority)) for(i in 1:length(superiority)){ if(superiority[i] >= 0.92){ x[i] <- 'Huge' } else if(superiority[i] >= 0.80){ x[i] <- 'Very large' } else if(superiority[i] >= 0.71){ x[i] <- 'Large' } else if(superiority[i] >= 0.64){ x[i] <- 'Medium' } else if(superiority[i] >= 0.56){ x[i] <- 'Small' } else if(superiority[i] < 0.56){ x[i] <- ('No difference') } } return(x) }

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/rstudio/yc_all_pow_cue_effect.R

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elshafeh/own

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refs/heads/master

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yc_all_pow_cue_effect.R

library(car);library(ggplot2) library(dae);library(nlme);library(effects);library(psych) library(interplot);library(plyr);library(devtools) library(ez);library(Rmisc);library(wesanderson);library(lme4);library(lsmeans) library(plotly) library(ggplot2) library(ggpubr) rm(list=ls()) cbPalette <- c( "#009E73","#E69F00","#999999") ext1 <- "~/GoogleDrive/PhD/Fieldtripping/documents/4R/" ext2 <- "yc_all_BroadandNeigh_alpha_cue_effect_minusevoked.txt" pat <- read.table(paste0(ext1,ext2),header=T) pat <- pat[pat$MOD != "occ",] ;pat$CHAN <- factor(pat$CHAN) ; pat$MOD <- factor(pat$MOD) ;pat$HEMI <- factor(pat$HEMI) pat <- pat[pat$MOD != "vis",] ;pat$CHAN <- factor(pat$CHAN) ; pat$MOD <- factor(pat$MOD) ;pat$HEMI <- factor(pat$HEMI) pat <- pat[pat$TIME != "1000ms",] ;pat$TIME <- factor(pat$TIME) #pat <- pat[pat$CHAN != "aud_L",] ;pat$CHAN <- factor(pat$CHAN) ; pat$MOD <- factor(pat$MOD) ;pat$HEMI <- factor(pat$HEMI) new_pat <- pat[pat$CUE_ORIG == "5Neig",] ; pat$CHAN <- factor(pat$CHAN) model.pat <- lme4::lmer(POW ~ (HEMI+CUE_POSITION+FREQ)^3 + (1|SUB), data =new_pat) model_anova <- Anova(model.pat,type=2,test.statistic=c("F")) print(model_anova) new_pat <- pat[pat$CUE_ORIG == "broad",] ; pat$CHAN <- factor(pat$CHAN) model.pat <- lme4::lmer(POW ~ (HEMI+CUE_POSITION+FREQ)^3 + (1|SUB), data =new_pat) model_anova <- Anova(model.pat,type=2,test.statistic=c("F")) print(model_anova) sub_pat <- pat[pat$CUE_ORIG == "5Neig" & pat$HEMI == "L_Hemi",] sub_pat$CHAN <- factor(sub_pat$CHAN) ; sub_pat$HEMI <- factor(sub_pat$HEMI) tgc <- summarySE(sub_pat, measurevar="POW", groupvars=c("CUE_POSITION","FREQ")) ggplot2::ggplot(tgc, aes(x=FREQ, y=POW, fill=CUE_POSITION)) +geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin=POW-se, ymax=POW+se),width=.2,position=position_dodge(.9))+ ylim(-0.2,0.2)+scale_fill_manual(values=cbPalette)+ggtitle("Stat Based Left Acx") sub_pat <- pat[pat$CUE_ORIG == "5Neig" & pat$HEMI == "R_Hemi",] sub_pat$CHAN <- factor(sub_pat$CHAN) ; sub_pat$HEMI <- factor(sub_pat$HEMI) tgc <- summarySE(sub_pat, measurevar="POW", groupvars=c("CUE_POSITION","FREQ")) ggplot2::ggplot(tgc, aes(x=FREQ, y=POW, fill=CUE_POSITION)) +geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin=POW-se, ymax=POW+se),width=.2,position=position_dodge(.9))+ ylim(-0.2,0.2)+scale_fill_manual(values=cbPalette)+ggtitle("Stat Based Right Acx") sub_pat <- pat[pat$CUE_ORIG == "broad" & pat$HEMI == "L_Hemi",] sub_pat$CHAN <- factor(sub_pat$CHAN) ; sub_pat$HEMI <- factor(sub_pat$HEMI) tgc <- summarySE(sub_pat, measurevar="POW", groupvars=c("CUE_POSITION","FREQ")) ggplot2::ggplot(tgc, aes(x=FREQ, y=POW, fill=CUE_POSITION)) +geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin=POW-se, ymax=POW+se),width=.2,position=position_dodge(.9))+ ylim(-0.2,0.2)+scale_fill_manual(values=cbPalette)+ggtitle("Broad Based Left Acx") sub_pat <- pat[pat$CUE_ORIG == "broad" & pat$HEMI == "R_Hemi",] sub_pat$CHAN <- factor(sub_pat$CHAN); sub_pat$HEMI <- factor(sub_pat$HEMI) tgc <- summarySE(sub_pat, measurevar="POW", groupvars=c("CUE_POSITION","FREQ")) ggplot2::ggplot(tgc, aes(x=FREQ, y=POW, fill=CUE_POSITION)) +geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin=POW-se, ymax=POW+se),width=.2,position=position_dodge(.9))+ ylim(-0.2,0.2)+scale_fill_manual(values=cbPalette)+ggtitle("Broad Based Right Acx") # ggarrange(p1, p2 , p3 , p4 , col = 2, nrow = 2,labels = c("Stat Based Left Acx", "Stat Based Right Acx","Broad Based Left Acx", "Broad Based Right Acx"))

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TomKellyGenetics/Stats_Workshop_Osumi_Group

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workshop_data.R

setwd("C:/Users/Tom Kelly/Documents/Downloads/20170927_Stats_Workshop_Osumi_Group") data <- rnorm(1000000, 1, 1.25) data1 <- rnorm(100, 1, 1.25) data2 <- rnorm(50, 1.25, 1) pdf("1_distribution.pdf", width=800/75, 600/75) plot(density(data), main="distribution", ylab="frequency", xlab="value of data") abline(v=mean(data)) abline(v=mean(data)-sd(data), col="grey") abline(v=mean(data)-2*sd(data), col="grey") abline(v=mean(data)+sd(data), col="grey") abline(v=mean(data)+2*sd(data), col="grey") dev.off() pdf("2_boxplot.pdf", width=800/75, 600/75) boxplot(data1, data2, col=c("red", "blue"), names=c("group A", "group B"), xlab="categorical group", ylab="continuous outcome", main="difference between groups?") dev.off() pdf("3_distribution_compare.pdf", width=800/75, 600/75) plot(density(data2), main="distribution", ylab="frequency", xlab="value of data", col="blue") lines(density(data1), main="distribution", ylab="frequency", xlab="value of data", col="red") legend("topright", fill=c("red", "blue"), legend=c("group A", "group B")) dev.off() pdf("4_t_distribution.pdf", width=800/75, 600/75) plot(density(data2), main="distribution", ylab="frequency", xlab="value of data (t)", col="blue") lines(density(data1), main="distribution", ylab="frequency", xlab="value of data", col="red") lines(density(data), main="distribution", ylab="frequency", xlab="value of data") legend("topright", fill=c("red", "blue", "black"), legend=c("group A", "group B", "normal (z)")) dev.off() t.test(data1, data2) #p.val = 0.029, t=-2.2051 dataset <- cbind(data1, data2) colnames(dataset) <- c("placebo", "treatment") write.csv(dataset, file="1_exercise_t_test.csv") data3 <- rnorm(100, 0.95, 2.25) data4 <- rnorm(50, 1.05, 2) pdf("5_boxplot.pdf", width=800/75, 600/75) boxplot(data3, data4, col=c("red", "blue"), names=c("group A", "group B"), xlab="categorical group", ylab="continuous outcome", main="difference between groups?") dev.off() pdf("6_distribution_compare.pdf", width=800/75, 600/75) plot(density(data4), main="distribution", ylab="frequency", xlab="value of data", col="red") lines(density(data3), main="distribution", ylab="frequency", xlab="value of data", col="blue") legend("topright", fill=c("red", "blue"), legend=c("group A", "group B")) dev.off() t.test(data3, data4) #p.val = 0.3624, t=--0.91469 dataset <- cbind(data3, data4) colnames(dataset) <- c("placebo", "treatment") write.csv(dataset, file="1_example_t_test.csv") example <- rbinom(100, 4, 0.5) anime <- factor(c("never", "monthly", "weekly", "daily")[example+1], levels=c("never", "monthly", "weekly", "daily")) table(anime) sex <- factor(c("Male", "Female")[c(rep(1, 50), rep(2, 50))]) table(anime, sex) fisher.test(table(anime, sex)) chisq.test(table(anime, sex)) #chi.sq = 0.80299, p.val = 0.8488 write.csv(table(anime, sex), file="2_example_chi_sq_test.csv") example <- rbinom(100, 2, 0.5) smoking <- factor(c("never", "ex-smoker", "current smoker")[example+1], levels=c("never", "ex-smoker", "current smoker")) table(smoking) cancer_diagnosed <- factor(c("Positive", "Negative")[c(rep(1, 50), rep(2, 50))]) table(smoking, cancer_diagnosed) fisher.test(table(smoking, cancer_diagnosed)) chisq.test(table(smoking, cancer_diagnosed)) #chi.sq = 0.80299, p.val = 0.8488 write.csv(table(smoking, cancer_diagnosed), file="2_exercise_chi_sq_test.csv") example <- rbinom(100, 1, 0.5) risk_allele <- factor(c("A", "T")[example+1], levels=c("A", "T")) table(risk_allele) batten_disease <- factor(c("Case", "Control")[c(rep(1, 50), rep(2, 50))]) table(risk_allele, batten_disease) fisher.test(table(risk_allele, batten_disease)) chisq.test(table(risk_allele, batten_disease)) #chi.sq = 0.80299, p.val = 0.8488 write.csv(table(risk_allele, batten_disease), file="3_exercise_fishers_test.csv") fit <- lm(data1~sex+risk_allele) summary(fit) anova(fit) cholesterol <- c(rnorm(50, 110, 30), rnorm(50, 90, 35)) fit <- lm(cholesterol~sex+risk_allele) summary(fit) anova(fit) fit <- lm(cholesterol~sex*risk_allele) summary(fit) anova(fit) boxplot(cholesterol~sex) t.test(cholesterol~sex) boxplot(cholesterol~risk_allele) t.test(cholesterol~risk_allele) dataset <- cbind(cholesterol, sex, risk_allele) write.csv(dataset, file="4_exercise_anova.csv")

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library(samon) ### Name: samonCombineIM ### Title: Combines results from separate runs of the samonIM function ### Aliases: samonCombineIM ### ** Examples # outputs from samonIM -- run on VAS1 with different seeds data("V1Res001") data("V1Res002") # combine them V1Results <- samonCombineIM( objlist = list(V1Res001,V1Res002) )

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rm(list=ls()) library(zoo) library(rmarkdown) library(knitr) library(pROC) library(rpart) library(rpart.plot) options(scipen = 999) th <- read.table("noshowappointments/KaggleV2-May-2016.csv", header=T, sep=",") # recode the data df <- data.frame( # PATIENT PROPERTIES patid = th$PatientId, male = ifelse(th$Gender=="M",1,0), age = th$Age, burs = th$Scholarship, hiper = th$Hipertension, diabet = th$Diabetes, alcohol = th$Alcoholism, handcap = th$Handcap, rayon = th$Neighbourhood, # APPOINTMENT PROPERTIES appid = th$AppointmentID, daysc = as.Date(th$ScheduledDay), wdaysc = strptime(th$ScheduledDay,"%Y-%m-%dT%H:%M:%SZ","GMT")$wday, hoursc = strptime(th$ScheduledDay,"%Y-%m-%dT%H:%M:%SZ","GMT")$hour, dayap = as.Date(th$AppointmentDay), wdayap = strptime(th$AppointmentDay,"%Y-%m-%dT%H:%M:%SZ","GMT")$wday, sms = th$SMS_received, noshow = ifelse(th$No.show=="Yes",1,0) ) dim(df) #df <- df[ (df$age >= 0) & (df$age <= 97) & (df$dayap >= df$daysc),] df <- df[ (df$age >= 0) & (df$dayap >= df$daysc),] df$daybw <- as.numeric(df$dayap - df$daysc) df$adult <- ifelse(df$age>=18,1,0) df$solbor <- ifelse(df$rayon=="SOLON BORGES",1,0) df$sandum <- ifelse(df$rayon=="SANTOS DUMONT",1,0) df$sancla <- ifelse(df$rayon=="SANTA CLARA",1,0) df$sancec <- ifelse(df$rayon=="SANTA CECÍLIA",1,0) df$itarar <- ifelse(df$rayon=="ITARARÉ",1,0) df$lourde <- ifelse(df$rayon=="DE LOURDES",1,0) df$cabral <- ifelse(df$rayon=="DO CABRAL",1,0) df$quadro <- ifelse(df$rayon=="DO QUADRO",1,0) df$horto <- ifelse(df$rayon=="HORTO",1,0) df$penha <- ifelse(df$rayon=="JARDIM DA PENHA",1,0) df$jesus <- ifelse(df$rayon=="JESUS DE NAZARETH",1,0) df$cypres <- ifelse(df$rayon=="MÁRIO CYPRESTE",1,0) df$sanmar <- ifelse(df$rayon=="SANTA MARTHA",1,0) df <- df[order(df$patid, df$dayap),] df$prev <- rep(0,nrow(df)) df$noprev <- rep(0,nrow(df)) for(i in 2:nrow(df)){ if(df$patid[i] == df$patid[i-1]){ df$prev[i] <- df$prev[i-1]+1 df$noprev[i] <- df$noprev[i-1] + df$noshow[i-1] } } df$noprevpct <- ifelse(is.nan(df$noprev/df$prev),0,df$noprev/df$prev) df$mode_previous <- ifelse(df$noprevpct>=0.5, 1, 0) df <- df[order(df$dayap),] df$prior <- c(0,cumsum(df$noshow))[-nrow(df)]/c(1:nrow(df)) plot(df$prior, type="n") rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = "#e5e5e5") grid(col="white", lwd=2) df$waittime <- ifelse(df$daybw==0,0,ifelse(df$daybw<=7,1,2)) df$morning <- ifelse(df$hoursc>=6 & df$hoursc<=10, 1, 0) df$age18 <- ifelse(df$age>=18,1,0) df$age80 <- ifelse(df$age>=80,1,0) df$age_decade <- floor(df$age/10) #df <- df[order(df$dayap),] trainsize <- round(nrow(df)*0.2) dftrain <- df[1:trainsize,] dftest <- df[(trainsize+1):(trainsize*2),] table(dftrain$noshow) dim(df) zf <- rbind(df[df$noshow==0,][sample(nrow(df[df$noshow==0,]),nrow(df[df$noshow==1,])),], df[df$noshow==1,]) dim(zf) table(zf$noshow) dtree <- rpart(formula=noshow ~ age_decade + burs + diabet + handcap + alcohol + hiper , data = zf, method = "class", maxdepth =3, minsplit = 2, minbucket = 1, cp=-1 ) rpart.plot(dtree) predicted_noshow <- ifelse(predict(dtree, newdata = df)[,2]>=0.5,1,0) actual_noshow <- df$noshow mytab <- table(abs(predicted_noshow - actual_noshow)) accuracy <- mytab[1]/(mytab[1] + mytab[2]) accuracy roc(actual_noshow, predicted_noshow) table(predicted_noshow, actual_noshow)[2,2] / (table(predicted_noshow, actual_noshow)[1,2] + table(predicted_noshow, actual_noshow)[2,2]) logit <- glm(formula = noshow ~ age + age18 + age80 + male + wdaysc + wdayap + burs + diabet + handcap + alcohol + solbor + sandum + itarar + lourde + cabral + quadro + penha + jesus + sanmar + sms + morning + waittime + mode_previous, family = binomial(link="logit"), data = zf, ) summary(logit) # predicted_noshow <- ifelse(predict(logit, df,type = "response")>=0.5,1,0) actual_noshow <- df$noshow mytab <- table(abs(predicted_noshow - actual_noshow)) accuracy <- mytab[1]/(mytab[1] + mytab[2]) accuracy roc(actual_noshow, predicted_noshow)

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library(rgdal) library(leaflet) library(plyr) library(dplyr) ## code adapted from http://adolfoalvarez.cl/code/users.html ##use the mines dataframe from script "Parsing_mining_data.R" # We download the data for the map from naturalearthdata.com url <- "http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip" folder <- getwd() #set a folder where to download and extract the data file <- basename(url) # download.file(url, file) # unzip(file, exdir = folder) #And read it with rgdal library #change working directory to directory that contains ne_50m_admin_0_countries world <- readOGR(dsn = folder, layer = "ne_50m_admin_0_countries", #encoding = "latin1", #you may need to use a different encoding verbose = FALSE) #renaming column 5 colnames(mines)[5] <- "MT_metric_tons" #selecting relevant columns mines2<-select(mines, Project, Country, MT_metric_tons) mines2$MT_metric_tons <-as.numeric(mines2$MT_metric_tons) #This function will extract the top 5 groups and return them as text to be included in the map myfun <- function(x,y){ a <- data.frame(x,y) %>% arrange(-y) %>% slice(1:5) return(paste0(a$x, ": ", a$y, collapse="<br>")) } #Now we group by country and apply the function mines4 <- mines2 %>% group_by(Country) %>% summarise(top5=myfun(Project, MT_metric_tons), MT_metric_tons = sum(MT_metric_tons, na.rm=T) ) %>% ungroup() %>% mutate(Country=toupper(Country)) #We need to reconvert to data.frame to merge it with the SpatialPolygons data frame "world" mines5 <- data.frame(mines4) names(mines5) = c("country", "top5", "MT_metric_tons") world <- sp::merge(world, mines5, by.x = "iso_a3", by.y = "country", sort = FALSE) #Tiles coming from stamen.com tiles <- "http://{s}.tile.stamen.com/toner-lite/{z}/{x}/{y}.png" attribution <- 'Map tiles by <a href="http://stamen.com">Stamen Design</a>, under <a href="http://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>. Map data by <a href="http://www.naturalearthdata.com/">Natural Earth</a>.' #######################################################################################################################

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computeChemicalPower <- function (power.mech,bird,...) UseMethod('computeChemicalPower') computeChemicalPower.power.mechanical <- function (power.mech,bird,...) { warning('computeChemicalPower has been made redundant... power.mechanical class has also been made redundant...') power.chem <- power.mech power.chem$power <- mech2chem(power.mech$power,bird,...) class(power.chem) <- c('data.frame','power.chemical') return(power.chem) } computeChemicalPower.numeric <- function (power.mech,bird,...) { warning('computeChemicalPower has been made redundant... please adjust calling function to use mech2chem() instead.') mech2chem(power.mech,bird,...) }

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yarsa_regression.R

regressors_economic <- c("family_size" ,"harvester_members_household" ,"expenses" ,"yarsha_income_2074" ,"highest_price" ,"average_price" ,"lowest_price" ,"method_curing" ,"time_curing" ,"perception" ,"low_price" ,"low_productivity" ,"weak_coordination" ,"packing" ,"suitable_month" ,"customer_preference" ,"benefits" ,"demerits" ,"geophysical" ,"price_variation") regressors_social <- c("respondent_gender" ,"respondent_age" ,"respondent_education" ,"started_harvesting" ,"time_collection") yarsha_income_model_eco <- lm(reformulate(response = "total_income_2074", termlabels = regressors_economic), data = yarsa) # # economic model # model anova yarsha_income_model_eco %>% anova() %>% broom::tidy() %>% write_csv("./outputs/anova_economic_model.csv", "") # mode coefficients yarsha_income_model_eco_sum <- summary(yarsha_income_model_eco) yarsha_income_model_eco_sum %>% broom::tidy() %>% write_csv("./outputs/regression_economic_model.csv") yarsha_income_model_socioeco <- lm(reformulate(response = "total_income_2074", termlabels = c(regressors_economic, regressors_social)), data = yarsa) # # socio-economic model # model anova yarsha_income_model_socioeco %>% anova() %>% broom::tidy() %>% write_csv("./outputs/anova_socio_economic_model.csv", "") # mode coefficients yarsha_income_model_socioeco_sum <- summary(yarsha_income_model_socioeco) yarsha_income_model_socioeco_sum %>% broom::tidy() %>% write_csv("./outputs/regression_socio_economic_model.csv") yarsha_income_model_eco %>% summary() %>% magrittr::extract(c("adj.r.squared", "fstatistic", "p-value")) %>% map(1) %>% as_tibble() %>% write_csv("./outputs/summary_stat_economic.csv", "") yarsha_income_model_socioeco %>% summary() %>% magrittr::extract(c("adj.r.squared", "fstatistic")) %>% map(1) %>% as_tibble() %>% write_csv("./outputs/summary_stat_socio_economic.csv", "")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ipm_analyse_l.R \name{ipm_analyse_l} \alias{ipm_analyse_l} \title{Analyse data using composite likelihood for a simulation study} \usage{ ipm_analyse_l( data, Plot = FALSE, priors = NULL, maxtime = 5, unit = "mins", save = 20000L, chain = 3 ) } \arguments{ \item{data}{an nlists object, simulation dataset returned by ipm_sim_l.} \item{Plot}{a flag, indicates whether to save the traceplot and the density plot for MCMC outputs.} \item{priors}{a string of code to set the prior.} \item{maxtime}{a scalar, specifying the maximum time to spend on analysis.} \item{unit}{a character string specifying the units of time for \code{max.time}. See \code{difftime}.} \item{save}{a scalar, the number of (potentially thinned) samples to save.} \item{chain}{a scalar, the number of MCMC chains.} } \value{ a mcmcrs object, the MCMC outputs, and plots (If plot=TRUE). } \description{ Using the true joint likelihood model to analyse the simulation by true joint likelihood model. }

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#' @title Create Text #' #' @export create_text <- function(allresults, dbr, startStation, stopStation) { # Get ETA's etas <- allresults$callingpoints %>% lapply( FUN = function(x) { x %>% `[[`("etarr") %>% `[`(x %>% `[[`("Name") %>% as.character %>% `==`(stopStation) %>% which) %>% as.character } ) %>% purrr::flatten_chr() incoming <- allresults$myresults incoming %<>% lapply(as.character) # Convert various timestamps to hh:mm etas %<>% translink.bot::conv_time() incoming$time %<>% translink.bot::conv_time() # Header text headTxt <- paste0("Trains from *", startStation, "* to * ", stopStation, "* (", incoming$originname[1], "/", incoming$name[1], " line)") # Start to accumulate information infoTxt <- paste0("Depart : *", incoming$time, "* // Arrive : *", etas, "*") # Chceck if any trains are delayed delayedTr <- incoming$Status %>% `==`("On time") %>% `!`() # Convert for printing incoming$Status <- paste0(" [ ", incoming$Status, " ]") if (delayedTr %>% any) { todelay <- incoming$Minutes[delayedTr] if ("" %>% `==`(todelay) %>% any) { todelay["" %>% `==`(todelay) %>% which] <- "?" } incoming$Status[delayedTr] <- paste0(" [ Delayed by ", todelay, " minutes ]") } # Get origin destination combo originDest <- incoming$origintiploc %>% paste0(":", incoming$tiploc) lineColors <- c() for (i in 1:(infoTxt %>% length)) { res <- "stationcolors" %>% dbr$HMGET(field = originDest[i]) %>% `[[`(1) lineColors %<>% c(if (res %>% is.null) "#000000" else res) } # Prepare the data structure actualTimes <- paste0(infoTxt, incoming$Status) # Data frame for slack message return( data.frame( pretext = c(headTxt, NA %>% rep(actualTimes %>% length)), fallback = c(headTxt, paste0(" - ", actualTimes)), text = c(NA, actualTimes), color = c(NA, lineColors) ) ) }

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### R code from vignette source 'RcppMLPACK-intro.Rnw' ### Encoding: UTF-8

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r

build_network_window.R

build_network = function(organism, db, ipl, th, method, degree, remove.sdi, remove.ul, remove.si, update, mainpath, f_pos) { cat("\n\n>BUILD NETWORK") # Parametres de la fonction Os <- organism os <- tolower(organism) ex.type <- tolower(method) inter.type <- tolower(degree) r.i <- tolower(remove.sdi) r.s.i <- tolower(remove.si) r.u.l <- tolower(remove.ul) IPL <- ipl DB <- db Th <- th UpDate <- tolower(update) # Lecture des fichiers input selected.database <- load_data(db) input.uniprotID <- read.table(file = ipl, header = FALSE, sep = '\t', stringsAsFactors = F) Thesaurus <- read.table(file = th, header = TRUE, quote = "", sep = "\t", stringsAsFactors = F) ### Tous les parametres et fichiers fournis peuvent etre utilises ### # Completer la liste d'ID a rechercher dans les bases : ajout des anciens ID et isoformes ID listsupp <- c() # Parcours de tout le fichier thesaurus pour trouver tous les elements de la liste input et ajouter tous les autres uniprot-ID associe a la meme proteine for (i in 1:length(Thesaurus[,1])) { if (Thesaurus[i,1] %in% input.uniprotID[,1]) { oldNames <- Thesaurus[i,7] oldNamesList <- unlist(strsplit(oldNames, ";")) if (is.na(Thesaurus[i,7]) == FALSE) { # Ajout des anciens uniprot-ID a la liste for (j in 1:length(oldNamesList[])) { listsupp <- rbind(listsupp, oldNamesList[j]) } } Nisoformes <- Thesaurus[i,8] if (Nisoformes > 0) { # Ajout des uniprot-ID des isoformes associes au meme gene for (k in 1 : Nisoformes) { newID <- paste(Thesaurus[i,1], k, sep = "-") listsupp <- rbind(listsupp, paste(Thesaurus[i,1], k, sep = "-")) } } } } # Apres avoir recupere tous les uniprot-ID associes a ceux de la liste input, les uniprot-ID de la liste input sont ajoutes input <- unlist(input.uniprotID) for (i in 1:length(input)) { uniprotID <- input[i] listsupp <- rbind(listsupp, uniprotID) } input.uniprotID <- listsupp ### La liste des uniprot-ID a rechercher dans les bases est complete ### # Creation d'un dossier pour les fichiers resultats en sortie organismdir <- gsub(" ", "-", Os) organism.path <- paste(mainpath, organismdir, sep = '/') dir.create(organism.path, showWarnings = FALSE) network.path <- paste(organism.path, '/Network', sep = '') dir.create(network.path, showWarnings = FALSE) # Affichage des parametres selectionnes pour la recherche d'interactions cat(paste('\n\n>Species selected :', os)) cat(paste('\n>Experimental type :', ex.type)) cat(paste('\n>Interaction type :', inter.type)) # Filtres des resultats sur l'organisme recherche et la methode souhaitee, dans les bases de donnees selected.database2 <- selected.database[(length(grep(os, selected.database[,8], ignore.case=TRUE)) > 0 && length(grep(os, selected.database[,9], ignore.case = TRUE)) > 0),] if (method == "genetic") { selected.database3 <- selected.database2[(length(grep(ex.type, selected.database2[,10], ignore.case = TRUE)) > 0 || length(grep(ex.type, selected.database2[,10], ignore.case = TRUE)) > 0),] } else { selected.database3 <- selected.database2[selected.database2[,10] == ex.type,] } # Verification de la presence d'interactions dans la base if (dim(selected.database3)[1] == 0) { cat("\n\nWARNING : No result found in database(s)\nProcesuss stop") stop() } # Rassemblement de toutes les databases fournies selected.database4 <- as.matrix(data.frame(selected.database3[,1:15], stringAsFactors = F)) ### Les bases sont filtrees sur la recherche souhaitee ### ########################## ########################## # test.selected.database4 = selected.database4[,c(4,5,1,3,2,6:12)] # unredundant <- remove_redundants(test.selected.database4) # Recherche des interactions directes pour les proteines de l'input list cat('\n\n>Search for first degree interactions') PPI.Direct <- recup_ppi(input.uniprotID, selected.database3) PPI.Direct[!duplicated(PPI.Direct[,c(1:4,6,11:12)]),] # Verification de la presence d'interactions if (dim(PPI.Direct)[1] == 0) { cat("\n\nWARNING : No interaction found\nProcesuss stop") stop() } ### Les interactions avec les proteines de l'input list sont extraites des bases ### # S'il y a bien des interactions identifiees dans les bases # Recherches du nombre d'article par interaction # unredundant <- pubmed_id(PPI.Direct, os,1) # PPI.Direct <- unredundant # Si le reseau demande est de degre 2 on relance une recherche d'interactions if (inter.type == "second-degree") { # Recuperation des uniprot-ID de toutes les proteines du reseau de degre 1 Interactions.matrix <- PPI.Direct # Recuperation des proteines d'interet et de leurs interactants directes col1 <- as.matrix(Interactions.matrix[,1]) col2 <- as.matrix(Interactions.matrix[,2]) # Rassemblement des listes pour recuperer les uniprot-ID en une seule copie listProt <- rbind(col1, col2) listProt <- unique(listProt) ListProt.UniprotID <- as.matrix(listProt) # Seconde recherche d'interactions, pour le degre 2 cat('\n>Search for second degree interactions') PPI.Direct2 <- recup_ppi(ListProt.UniprotID, selected.database3) # Suppression des interactions de degre 2 avec les proteines qui n'ont qu'une seule interaction dans le reseau de degre 1 : si demande if (r.i == 'yes') { PPI.Indirect <- remove_ppi(ListProt.UniprotID, PPI.Direct2, input.uniprotID) } else { PPI.Indirect <- PPI.Direct2 } PPI.Indirect<-unique(PPI.Indirect) PPI.Indirect[!duplicated(PPI.Indirect[,c(1:4,6,11:12)]),] cat('\n>Search for PUBMED IDS for each interaction') # Recherches du nombre d'article par interaction # unredundant <- pubmed_id(PPI.Indirect, os,2) # PPI.Indirect<-unredundant # # Rassemblement des resultats d'interactions de degre 1 et degre 2 network <- rbind(PPI.Direct[,c(3,5,4,1,2,6:12)], PPI.Indirect[,c(3,5,4,1,2,6:12)]) ### Les interactions de degre 2 sont ajoutees au reseau ### } else { # On ne garde que les resultats de la premiere recherche d'interactions si on souhaite un reseau de degre 1 network <- rbind(PPI.Direct[,c(3,5,4,1,2,6:12)]) } # Verification de la presence de resultats if (is.null(network) == T) { cat("WARNING :\nThe network is null!!!\nProcessus stoped") stop() } cat('\n') # Premier trie des interactions du reseau en fonction des parametre de construction selectionnes # Remove redundants network<-DataBases(network) # selected.database3 <- remove_redundants(network) selected.database3 <- network network <- as.matrix(selected.database3) # Remove self-interactant if (r.s.i == 'yes'){ cat('\n>Removing self-interactant ...\n') network <- network[network[,4]!=network[,5],] network <<- network cat(' OK') } else { cat('\n>Proteins which interact with itselves are kept') } cat('\n>Removing redundants ...\n') network = network[,c(4,5,1,3,2,6:12)] unredundant <- pubmed_id(network, os,2) network <- unredundant network = network[,c(3,5,4,1,2,6:13)] ### Le reseau est complet et un premier trie a ete effectue ### # Correction automatique du reseau avec le thesaurus : on met l'uniprot-ID principal pour chaque proteine (a la place des anciens) data <- as.matrix(network) thesaurus <- read.table(file = th, header = TRUE, quote = "", sep = "\t" , stringsAsFactors = F) # Visualisation de la progression des corrections cat ( '\n>Autocorrection' ) cat ( '\n' ) pb1 <<- txtProgressBar(min = 0, max = dim(data)[1], style = 3) nbpassage<<-0 nf<<-c() apply(data,1,cherche_uniprotID,data=data,thesaurus=thesaurus) not_founds<-nf # Parcours de toutes les interactions du reseau # for (i in 1:dim(data)[1]) { # Recherche de l'uniprot ID de reference et association du bon nom de proteine et de gene # Colonne A # resultatA <- search_id(data[i,4], thesaurus) # if (length(resultatA) == 3) { # ID <- resultatA[1] # Proteine <- resultatA[2] # Gene <- resultatA[3] # data[i,4] <- ID # data[i,1] <- Proteine # data[i,11] <- Gene # } # v1<-c(grep(data[i,11],data[,11])) # v2<-c(grep(data[i,11],data[,12])) # if((length(unique(c(data[v1,4],data[v2,5])))>1)||(length(resultatA) != 3)) # { # On recupere les identifiants que le thesaurus ne sait pas remplacer tout seul # not_found <- data[i,c(4,1,11)] # not_founds <- rbind(not_founds, not_found) # } # Colonne B # resultatB <- search_id(data[i,5], thesaurus) # if (length(resultatB) == 3) { # ID <- resultatB[1] # Proteine <- resultatB[2] # Gene <- resultatB[3] # # data[i,5] <- ID # data[i,3] <- Proteine # data[i,12] <- Gene # } # v3<-c(grep(data[i,12],data[,11])) # v4<-c(grep(data[i,12],data[,12])) # if((length(unique(c(data[v3,4],data[v4,5])))>1)||(length(resultatB) != 3)) # { # On recupere les identifiants que le thesaurus ne sait pas remplacer tout seul # not_found <- data[i,c(5,3,12)] # not_founds <- rbind(not_founds, not_found) # } # setTxtProgressBar(pb1, i) # }#end for # Memorisation du nouveau reseau (correction avec le thesaurus) network2 <- data ### Toutes les corrections automatiques sont faites sur le reseau ### # On recupere les identifiants que le thesaurus n'a pas trouves not_founds <<- unique(not_founds) not_founds = unique(not_founds) if (is.null(dim(not_founds)[1]) == F) { cat(paste('\n\n>Search finished,', dim(as.matrix(not_founds))[1], 'IDs are not matched with thesaurus')) colnames (not_founds) <- c('uID', 'proteinname', 'genename') # Correction manuelle pour les identifiants que le thesaurus n'a pas trouves NF <- not_founds not.found.ID <- as.matrix(NF) # Mise en place de la fenetre d'affichage des corrections panelcorrection <- gwindow("Manual correction panel", parent = f_pos, visible = F, expand = T) pc <- ggroup(container = panelcorrection, horizontal = F, use.scrollwindow = T) pcsb <- ggroup(container = pc, horizontal = T, use.scrollwindow = F) lg <- gvbox(container = pcsb) pg <- gvbox(container = pcsb) rg <- gvbox(container = pcsb) fllg <- gformlayout(container = lg) flpg <- gformlayout(container = pg) flrg <- gformlayout(container = rg) not.found.ID<-unique(not.found.ID) if(length(not_founds) >= 50) { print("Warning more than 50 proteins need manual correction. Correction panel skipped.") finish(network, network2, r.s.i, r.u.l, UpDate, selected.database4, mainpath, network.path, Os, r.i, not_founds, ex.type, inter.type, os, IPL, Th, DB, organism.path) # dispose(bpc) } else { # Affichage des identifiants a modifier for (i in 1:dim(not.found.ID)[1]) { uniprotID <- gedit(initial.msg = 'New UniprotID', label = as.character(not.found.ID[i,1]), container = fllg) PROTEINname <- gedit(initial.msg = 'New Protein name', label = as.character(not.found.ID[i,2]), container = flpg) GENEname <- gedit(initial.msg = 'New Gene name', label = as.character(not.found.ID[i,3]), container = flrg) } visible(panelcorrection) <- T # Informations sur la correction manuelle Info <- '\"Correct Network\" : This button will save your manual corrections in the network and in updated databases.\n\n\"Ignore\" : This step will be ignored and the interactions with the uncorrected proteins will be conserved.' bpc <- ggroup(container = pc); addSpring(bpc) # 1 : Informations sur la correction manuelle bouton1 <- gbutton("Info", handler = function(h,...) { winfo <- gwindow("Info..", parent = f_pos) g <- gvbox(container = winfo); g$set_borderwidth(10L) glabel(Info, container = g) gseparator(container = g) bg <- ggroup(container = g); addSpring(bg) gbutton("Return", container = bg, handler = function(...) dispose(winfo)) }, container = bpc) # 2 : Ignorer la correction manuelle, conservation des identifiants bouton2 <- gbutton('Ignore', handler = function(h,...) { # Recuperation des resultats finaux et sauvegardes finish(network, network2, r.s.i, r.u.l, UpDate, selected.database4, mainpath, network.path, Os, r.i, not_founds, ex.type, inter.type, os, IPL, Th, DB, organism.path) dispose(bpc) }, container = bpc) # 3 : Correction manuelle du reseau bouton3 <- gbutton("Correct network", handler = function(h,...) { # Rassemblement des modifications a apporter if(svalue(fllg)!=""){ cor.uniprotID <- cbind(names(svalue(fllg)), svalue(fllg)) } else{ cor.uniprotID <- cbind(names(svalue(fllg)), names(svalue(fllg))) } if(svalue(flpg)!=""){ cor.proteinname <- cbind(names(svalue(flpg)), svalue(flpg)) } else{ cor.proteinname <- cbind(names(svalue(flpg)), names(svalue(flpg))) } if(svalue(flrg)!=""){ cor.genename <- cbind(names(svalue(flrg)), svalue(flrg)) } else{ cor.genename <- cbind(names(svalue(flrg)), names(svalue(flrg))) } cor.uniprotID <- data.frame(cor.uniprotID, row.names = NULL) cor.proteinname <- data.frame(cor.proteinname, row.names = NULL) cor.genename <- data.frame(cor.genename, row.names = NULL) cor.manuel <- cbind(cor.uniprotID, cor.proteinname, cor.genename) cor.manuel <- data.frame(cor.manuel, row.names = NULL) # # colnames(cor.manuel) <- c('old_uid', 'corrected_uid', 'old_proteinname', 'corrected_proteinname', 'old_genename', 'corrected_genename') # Corrections du reseau et ajout a la liste des corrections for (j in 1:dim(network2)[1]) { for (i in 1:dim(cor.manuel)[1]) { # Correction proteine A if (network2[j,4] == cor.manuel[i,1]) { network2[j,4] <- gsub("\n", "", cor.manuel[[i,2]][1]) } if (network2[j,1] == cor.manuel[i,4]) { network2[j,1] <- gsub("\n", "", cor.manuel[[i,4]][1]) } if (network2[j,11] == cor.manuel[i,6]) { network2[j,11] <-gsub("\n", "", cor.manuel[[i,6]][1]) } # Correction proteine B if (network2[j,5] == cor.manuel[i,1]) { network2[j,5] <- gsub("\n", "", cor.manuel[[i,2]][1]) } if (network2[j,3] == cor.manuel[i,4]) { network2[j,3] <- gsub("\n", "", cor.manuel[[i,4]][1]) } if (network2[j,12] == cor.manuel[i,6]) { network2[j,12] <- gsub("\n", "", cor.manuel[[i,6]][1]) } } } # Recuperation des resultats finaux et sauvegardes finish(network, network2, r.s.i, r.u.l, UpDate, selected.database4, mainpath, network.path, Os, r.i, not_founds, ex.type, inter.type, os, IPL, Th, DB, organism.path) dispose(bpc) }, container = bpc) } } else if (is.null(not_founds)[1] == T) { cat(paste('\n\n>Search finished, all IDs are matched with thesaurus ')) # Recuperation des resultats finaux et sauvegardes finish(network, network2, r.s.i, r.u.l, UpDate, selected.database4, mainpath, network.path, Os, r.i, not_founds, ex.type, inter.type, os, IPL, Th, DB, organism.path) } else { cat(paste("\n\n>Search finished, 1 IDs isn't matched with thesaurus ")) # Recuperation des resultats finaux et sauvegardes finish(network, network2, r.s.i, r.u.l, UpDate, selected.database4, mainpath, network.path, Os, r.i, not_founds, ex.type, inter.type, os, IPL, Th, DB, organism.path) } setwd(mainpath) visible(mainpanel) <<- T } build_network_window <- function(f_pos, mainpanel, mainpath) { db <- c() return.parameter <- c() ipl <- c() th <- c() panel_para <- gwindow("Build network : ", parent = f_pos, visible = T) pp <- gvbox(container = panel_para) pp$set_borderwidth(10L) flyt <- gformlayout(container = pp, expand = TRUE) # Selection des options de construction du reseau gcombobox(c('Caenorhabditis elegans', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Rattus norvegicus', 'Saccharomyces cerevisiae','Arabidopsis thaliana', 'Other'), label = "Organism", selected = 7, container = flyt) gradio(c("Physical", "Genetic"), selected = 1, horizontal = T, label = "Experimental method", container = flyt) gradio(c("First-degree", "Second-degree"), selected = 1, horizontal = TRUE, label = "Interaction type", container = flyt) gradio(c("Yes", "No"), selected = 1, horizontal = TRUE, label = "Remove second degree unique links", container = flyt) gradio(c("Yes", "No"), selected = 2, horizontal = TRUE, label = "Remove all unique links", container = flyt) gradio(c("Yes", "No"), selected = 1, horizontal = TRUE, label = "Remove self-interactant", container = flyt) gradio(c("Yes", "No"), selected = 1, horizontal = TRUE, label = "Update databases if necessary", container = flyt) # Selection des bases de donnees chdb <- ggroup(container = pp, horizontale = T) addSpring(chdb) bouton1 <- gbutton("Select database", container = chdb, handler = function(...) { db <<- gfile(text = "Select database", type = "open", multi = T, container = chdb) if (is.null(db) == T) { gmessage('Selected database is null', icon = 'error') } if (is.null(db) == F) { bouton1$set_value(paste(length(db), 'databases selected')) cat(paste('\n>Database selected : ', db, sep = '')) } }) # Selection du fichier contenant les noms des proteines d'interet bouton2 <- gbutton("Select input list", container = chdb, handler = function(...) { ipl <<- gfile(text = "Select input list", type = "open", multi = T,container = chdb) if (is.null(ipl) == T) { gmessage('Selected input list is null', icon = 'error') } if (is.null(ipl) == F) { bouton2$set_value(paste(length(ipl), 'input list selected')) cat(paste('\n>Inputlist selected : ', ipl, sep = '')) } }) # Selection du fichier thesaurus bouton3 <- gbutton("Select thesaurus", container = chdb, handler = function(...) { th <<- gfile(text = "Select a thesaurus", type = "open", multi = F, container = chdb) if (is.null(th) == T) { gmessage('Selected thesaurus is null', icon = 'error') } if (is.null(th) == F) { bouton3$set_value(paste(length(th), 'thesaurus selected')) cat(paste('\n>Thesaurus selected : ', th, sep = '')) } }) ppb <- ggroup(container = pp) addSpring(ppb) gbutton("Build the network", handler = function(h,...) { return.parameter <<- svalue(flyt) visible(panel_para) <- F # Memorisation des parametres de recherche selectionnes organism <- as.character(return.parameter[1]) method <- return.parameter[2] degree <- as.character(return.parameter[3]) remove.sdi <- as.character(return.parameter[4]) remove.ul <- as.character(return.parameter[5]) remove.si <- as.character(return.parameter[6]) update <- as.character(return.parameter[7]) # Verification de la presence de tous les elements necessaires a la construction du reseau # Lancement de la construction du reseau avec les parametres et fichiers donnes if (is.null(db) == F && is.null(ipl) == F && is.null(th) == F) { ############################################################################################################# ############################################## build_network() ############################################## ### Construction du reseau if (organism == "Other") { panelorganism <- gwindow("Organism description", parent = f_pos, visible = F, expand = T) pc <- ggroup(container = panelorganism, horizontal = F, use.scrollwindow = T) pcsb <- ggroup(container = pc, horizontal = F, use.scrollwindow = F) lg <- gvbox(container = pcsb) fllg <- gformlayout(container = lg) organismName <- gedit(initial.msg = 'Organism Name', label = "NAME", container = fllg) visible(panelorganism) <- T bpc <- ggroup(container = pc); addSpring(bpc) # 1 : Autre organism bouton1 <- gbutton("OK", handler = function(h,...) { # Rassemblement des modifications a apporter org.name <- cbind(names(svalue(fllg)), svalue(fllg)) org.name <- data.frame(org.name, row.names = NULL) org <- cbind(org.name) colnames(org) <- c('Organism_name') organism <- as.character(org[1,2]) build_network(organism, db, ipl, th, method, degree, remove.sdi, remove.ul, remove.si, update, mainpath, f_pos) dispose(bpc) }, container = bpc) } else { build_network(organism, db, ipl, th, method, degree, remove.sdi, remove.ul, remove.si, update, mainpath, f_pos) } dispose(panel_para) dispose(ppb) } # Affichage d'un message d'erreur s'il manque un fichier else if (is.null(db) == T) { gmessage('Database selected is null', icon = 'error') dispose(ppb) visible(panel_para) <- F visible(mainpanel) <<- T } else if (is.null(ipl) == T) { gmessage('Input list selected is null', icon = 'error') dispose(ppb) visible(panel_para) <- F visible(mainpanel) <<- T } else if (is.null(th) == T) { gmessage('Thesaurus selected is null', icon = 'error') dispose(ppb) visible(panel_para) <- F visible(mainpanel) <<- T } else { gmessage('Error : Unable to start search', icon = 'error') dispose(ppb) visible(panel_para) <- F visible(mainpanel) <<- T } }, container = ppb) gbutton("Return", handler = function(h,...) { dispose(panel_para) visible(mainpanel) <<- T }, container = ppb) visible(panel_para) <- T }

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/man/ninvwish.Rd

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cran/norm

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refs/heads/master

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ninvwish.Rd

\name{ninvwish} \alias{ninvwish} \title{ Random normal-inverted Wishart variate } \description{ Simulates a value from a normal-inverted Wishart distribution. This function may be useful for obtaining starting values of the parameters of a multivariate normal distribution for multiple chains of data augmentation. } \usage{ ninvwish(s, params) } \arguments{ \item{s}{ summary list of an incomplete normal data matrix produced by the function \code{prelim.norm}. } \item{params}{ list of parameters of a normal-inverted Wishart distribution. In order, the elements of the list are: tau (a scalar), m (a scalar), mu0 (a vector of length ncol(x)), and lambdainv (a matrix of dimension c(ncol(x),ncol(x))). When using this function to create starting values for data augmentation, mu0 and lambdainv should be chosen in relation to the data matrix after the columns have been centered and scaled to have mean zero and variance one. }} \value{ a vector in packed storage representing the simulated normal-inverted Wishart variate. This vector has the same form as parameter vectors produced by functions such as \code{em.norm} and \code{da.norm}, and may be used directly as a starting value for these functions. This vector can also be put into a more understandable format by \code{getparam.norm}. } \section{WARNING}{ Before this function may be used, the random number generator seed must be initialized with \code{rngseed} at least once in the current S session. } \references{ See Section 5.4.2 of Schafer (1996). } \seealso{ \code{\link{rngseed}}, \code{\link{getparam.norm}}, \code{\link{em.norm}} and \code{\link{da.norm}}. } \examples{ data(mdata) s <- prelim.norm(mdata) #do preliminary manipulations params <- list(1,.5,rep(0,ncol(mdata)), .5*diag(rep(1,ncol(mdata)))) # gives widely dispersed values rngseed(1234567) start <- ninvwish(s,params) # draw a variate thetahat <- em.norm(s,start=start) # run EM from this starting value } \keyword{multivariate} % Converted by Sd2Rd version 0.3-3.

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cran/depth

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refs/heads/master

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rd

ctrmean.Rd

\name{ctrmean} \alias{ctrmean} \title{Centroid trimmed mean} \description{ Computes the centroid of a Tukey depth-based trimmed region. } \usage{ ctrmean(x ,alpha, eps = 1e-8, mustdith = FALSE, maxdith = 50, dithfactor = 10 ,factor = .8) } \arguments{ \item{x}{Bivariate data as a matrix, data frame or list. If it is a matrix or data frame, then each row is viewed as one bivariate observation. If it is a list, both components must be numerical vectors of equal length (coordinates of observations).} \item{alpha}{Outer trimming fraction (0 to 0.5). Observations whose depth is less than \code{alpha} to be trimmed.} \item{eps}{Error tolerance to control the calculation.} \item{mustdith}{Logical. Should dithering be applied? Used when data set is not in general position or a numerical problem is encountered.} \item{maxdith}{Positive integer. Maximum number of dithering steps.} \item{dithfactor}{Scaling factor used for horizontal and vertical dithering.} \item{factor}{Proportion (0 to 1) of outermost contours computed according to a version of the algorithm ISODEPTH of Rousseeuw and Ruts (1998); remaining contours are derived from an algorithm in Rousseeuw \emph{et al.} (1999). } } \details{Dimension 2 only. Centroid trimmed mean is defined to be the centroid of a Tukey depth-based trimmed region relative to the uniform measure. Contours are derived from algorithm ISODEPTH by Ruts and Rousseeuw (1996) or, more exactly, revised versions of this algorithm which appear in Rousseeuw and Ruts (1998) and Rousseeuw \emph{et al.} (1999). Argument \code{factor} determines which version to use. If \eqn{n} is the number of observations, contours of depth \eqn{\le } \code{factor} \eqn{n/2} are obtained from the 1998 version, while the remaining contours are derived from the 1999 version. When the data set is not in general position, dithering can be used in the sense that random noise is added to each component of each observation. Random noise takes the form \code{eps} times \code{dithfactor} times U for the horizontal component and \code{eps} times \code{dithfactor} times V for the vertical component, where U, V are independent uniform on [-.5, 5.]. This is done in a number of consecutive steps applying independent U's and V's.} \value{Centroid trimmed mean vector } \references{Masse, J.C. (2008), Multivariate Trimmed means based on the Tukey depth, \emph{J. Statist. Plann. Inference}, in press. Ruts, I. and Rousseeuw, P.J. (1996), Computing depth contours of bivariate point clouds, \emph{Comput. Statist. Data Anal.}, \bold{23}. 153--168. Rousseeuw, P.J. and Ruts, I. (1998), Constructing the bivariate Tukey median, \emph{Stat. Sinica}, \bold{8}, 828--839. Rousseeuw, P.J., Ruts, I., and Tukey, J.W. (1999), The Bagplot: A Bivariate Boxplot, \emph{The Am. Stat.}, \bold{53}, 382--387.} \author{Jean-Claude Masse and Jean-Francois Plante, based on Fortran code by Ruts and Rousseeuw from University of Antwerp.} \seealso{\code{\link{med}} for multivariate medians and \code{\link{trmean}} for classical-like depth-based trimmed means.} \examples{ ## exact centroid trimmed mean set.seed(345) xx <- matrix(rnorm(1000), nc = 2) ctrmean(xx, .2) ## second example of an exact centroid trimmed mean set.seed(159); library(MASS) mu1 <- c(0,0); mu2 <- c(6,0); sigma <- matrix(c(1,0,0,1), nc = 2) mixbivnorm <- rbind(mvrnorm(80, mu1 ,sigma), mvrnorm(20, mu2, sigma)) ctrmean(mixbivnorm, 0.3) ## dithering used for data set not in general position data(starsCYG, package = "robustbase") ctrmean(starsCYG, .1, mustdith = TRUE) } \keyword{multivariate} \keyword{nonparametric} \keyword{robust}

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/pso.R

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sylwesterf/scripts

c09aec1bd500be0f8e856b845c0f7523040825a9

9d1fd41e79e827c962cb2b5c79ba71652ae2c311

refs/heads/master

2021-05-03T12:37:08.108358

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pso.R

particleSwarm = function(f, popSize=10, d=2, l.bound=0, u.bound=1, w, c1, c2, maxIter=100, criterion=FALSE) { # psoAlgorithm # INPUT # - f objective function # - popSize number of particles # - d number of variables # - l.bound lower boundary (for initial particles) # - u.bound upper boundary (sup.) # - w inertia weight (vector of length 2 for dynamic inertia, i.e. decreasing over time) # - c1 learning factor (individual experience) # - c2 learning factor (social communication) # - maxIter number of generations # - criterion MaxDistQuick stopping criterion (with threshold) #store results result <- list(x.opt = numeric(d), f.opt = numeric(1), x.hist = c(), f.hist = numeric(maxIter), iter.time = c(), epoch = c(), particles = matrix(numeric(), nrow=popSize, ncol=d)) #pso start time starttime <- Sys.time() #initialize particles #particle.matrix <- t(matrix((u.bound - l.bound) * runif(popSize*d) + l.bound, nrow=d, ncol=popSize)) particle.matrix <- t(matrix(runif(popSize*d, l.bound, u.bound), nrow=d, ncol=popSize)) #initial pbest #f.pbest <- apply(particle.matrix, 1, f) pbest <- particle.matrix f.pbest <- apply(pbest, 1, f) #initial gbest gbest <- pbest[which.min(f.pbest), 1:d] f.gbest <- min(f.pbest) #f.gbest <- f(gbest) result$x.hist <- gbest result$f.hist <- f.gbest #initial velocity velocity <- matrix(runif(popSize*d), nrow=popSize, ncol=d) #update velocity velocity <- max(w) * velocity + c1 * runif(1) * (pbest - particle.matrix) + c2 * runif(1) * (t(matrix(rep(gbest, popSize), d, popSize)) - particle.matrix) #new coordinates particle.matrix <- particle.matrix + velocity #first iteration end result$iter.time <- Sys.time() - starttime #iterate over particles for (i in 2:maxIter){ #MaxDistQuick stopping criterion if (criterion[1] == TRUE){ if (i/maxIter > 0.2){ best.20 <- unname(tail(result$x.hist, floor(i*0.2))) max.euclidean.dist <- max(sqrt(rowSums(best.20 - t(matrix(gbest, d, floor(i*0.2))))^2)) if (max.euclidean.dist < criterion[2]){ result <- c(result, setNames((i-1), "no.ite")) break } } } #epoch start.iter <- Sys.time() #calculate the objective function for new coordinates f.particle.matrix <- apply(particle.matrix, 1, f) #update pbest pbest <- ifelse(matrix(rep(f.particle.matrix, d), nrow=popSize, ncol=d) < matrix(rep(f.pbest, d), nrow=popSize, ncol=d), particle.matrix, pbest) #update f.pbest f.pbest <- apply(pbest, 1, f) #update gbest, f.gbest gbest <- pbest[which.min(f.pbest), 1:d] f.gbest <- f(gbest) #append results result$x.hist <- rbind(result$x.hist, gbest) result$f.hist <- append(result$f.hist, f.gbest) #update velocity velocity <- (max(w) - (max(w) - min(w)) * i/maxIter) * velocity + c1 * runif(1) * (pbest - particle.matrix) + c2 * runif(1) * (t(matrix(rep(gbest, popSize), d, popSize)) - particle.matrix) #new coordinates particle.matrix <- particle.matrix + velocity #iteration time result$iter.time <- append(result$iter.time, Sys.time() - start.iter) } #solution result$x.opt <- gbest result$f.opt <- f.gbest #pso finish time result$epoch <- Sys.time() - starttime #end state particles' coordinates result$particles <- particle.matrix return(result) }

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/tabs/server/project_specific/data_form/plant_tissue_culture_module.R

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mkaranja/Banana-Tracker

fe72155f8593813c4a7c608898ae60ceadeb2d7a

3fbb615ac61ba48fa37f72c788217ae1d9e95e3c

refs/heads/master

2021-02-17T23:21:47.655521

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plant_tissue_culture_module.R

tab_files <- list.files(path = "tabs/server/project_specific/data_form/plant_tissue_culture_module", full.names = T, recursive = T) suppressMessages(lapply(tab_files, source)) observeEvent(input$plant_tissue_culture_module, { showModal(modalDialog(tags$h2(style="color:#800000;text-align:center;",paste("Plant Tissue Culture Module - ",input$project_selected)), tabsetPanel(type = "pills",id = "PTC", search_PTC, updating_last_subculture#, #new_PTC #PTC_deployment, #TMPL_UMPL ), easyClose = F, size = "l" )) }) ## SEARCH PLANT TISSUE CULTURE ----------------------------------------------------------- observeEvent(input$plant_tissue_culture_module, { updateSelectInput(session, "searchPTC_ID", "Identity", choices = c('', unique(PTC()$PTCIdentity))) updateSelectInput(session, "searchPTC_VectorID", "Vector ID", choices = c('', unique(PTC()$VectorID1))) updateDateRangeInput(session, "searchPTC_DateOfStarterCulture", "Date of Starter Culture", min=min(PTC()$DateOfStarterCulture), max=max(PTC()$DateOfStarterCulture), start=min(PTC()$DateOfStarterCulture), end=max(PTC()$DateOfStarterCulture)) }) # Clear observeEvent(input$searchPTC_ClearForm,{ confirmSweetAlert( session = session, inputId = "searchPTC_ClearForm_Confirm", type = "", title = "", text = "Do you really want to clear the fields?", btn_labels = c("Cancel", "Yes, Clear!"), btn_colors = c("#D3D3D3", "#DD6B55") ) }) observeEvent(input$searchPTC_ClearForm_Confirm, { if(input$searchPTC_ClearForm_Confirm == TRUE){ reset("searchPTC_form") shinyjs::hide("searchPTC_Table") shinyjs::hide("searchPTCDeleted_Table") shinyjs::hide("searchPTC_Culture_Form") } }, ignoreInit = TRUE) # Search observeEvent(input$searchPTC_LoadData,{ dt <- vector_inventory() %>% dplyr::filter(trimws(VectorID) == trimws(input$searchPTC_VectorID)) updateTextInput(session, "searchPTC_PlantSelection", "Plant Selection", value = as.character(dt$PlantSelection[1])) # update plantSelection }) # Search Table output$searchPTC_Table_Output <- renderUI({ if(input$search_deletedPTC==TRUE){ rHandsontableOutput("searchPTCDeleted_Table") }else { rHandsontableOutput("searchPTC_Table") } }) # PTC table searchPTC_input <- reactive({ dt <- PTC() df <- dt %>% dplyr::filter(between(lubridate::ymd(DateOfStarterCulture), input$searchPTC_DateOfStarterCulture[1], input$searchPTC_DateOfStarterCulture[2])) if(input$searchPTC_ID !=""){ df <- df[trimws(df$PTCIdentity)==trimws(input$searchPTC_ID),] } if(input$searchPTC_VectorID !=""){ df <- df[trimws(df$VectorID1)==trimws(input$searchPTC_VectorID),] } df }) observeEvent(input$searchPTC_Search,{ if(input$search_deletedPTC==FALSE){ output$searchPTC_Table <- renderRHandsontable({ rhandsontable(searchPTC_input(), selectCallback = T, readOnly = T, rowHeaders=F) %>% hot_table(stretchH = "all") }) } }) # deleted PTC table searchPTCDeleted_input <- reactive({ dt <- deletedPTC() df <- dt %>% dplyr::filter(between(lubridate::ymd(DateOfStarterCulture), input$searchPTC_DateOfStarterCulture[1], input$searchPTC_DateOfStarterCulture[2])) if(input$searchPTC_ID !=""){ df <- dt[trimws(dt$PTCIdentity)==trimws(input$searchPTC_ID),] } if(input$searchPTC_VectorID !=""){ df <- dt[trimws(dt$VectorID1)==trimws(input$searchPTC_VectorID),] } }) observeEvent(input$searchPTC_Search,{ if(input$search_deletedPTC==TRUE){ if(!is.null(searchPTCDeleted_input())){ output$searchPTCDeleted_Table <- renderRHandsontable({ rhandsontable(searchPTCDeleted_input(), selectCallback = T, readOnly = T, rowHeaders=F) %>% hot_table(stretchH = "all") }) }else{ showNotification("Not data to display.", type = "warning") } } }) output$test10 <- renderPrint({ length(input$searchPTC_Table_select$select) }) # culture observeEvent(input$searchPTC_Table_select$select,{ r <- isolate(input$searchPTC_Table_select$select$r) c <- searchPTC_input()[r,] output$searchPTC_Culture_Table <- renderRHandsontable({ rhandsontable(c, selectCallback = T, readOnly = T, rowHeaders=F) %>% hot_table(stretchH = "all") }) }) observeEvent(input$searchPTC_Culture,{ r <- isolate(input$searchPTC_Table_select$select$r) c <- searchPTC_input()[r,] id <- c$PTCIdentity media <- tbl(pool, "tblMedia") %>% collect() culturedby <- tbl(pool, "tblCulturedBy") %>% collect() if(length(r)>0){ output$searchPTC_Culture_Output <- renderUI({ div(id="searchPTC_Culture_Form", column(7, panel_div(class_type = "default", content = tags$div( column(6, disabled(textInput("searchPTC_SelectedIdentity","Identity", value = id, width = "100%")), numericInput("searchPTC_NumberOfCultures",labelMandatory("Number of Cultures"), min = 0, value = NULL, width = "100%"), dateInput("searchPTC_DateOfCulture",labelMandatory("Date of Cultures"), width = "100%"), selectInput("searchPTC_CulturedBy",labelMandatory("Cultured By"), choices = c('', loadData("tblCulturedBy")$CulturedBy), width = "100%"), selectInput("searchPTC_MediaForCultures",labelMandatory("Media"), choices = c('', loadData("tblMedia")$Media), width = "100%"), numericInput("searchPTC_LabBookNumberForCultures",labelMandatory("Lab Book Number"), min = 0, value = NULL, width = "100%"), numericInput("searchPTC_PageNumberForCultures",labelMandatory("Page Number"), min = 0, value = NULL, width = "100%"), textInput("searchPTC_Comments", "Comments", width = "100%") ), column(6, br(), br(),br(),br(), br(),br(), panel_div(class_type = "default", content = tags$div( tags$b("Additives"), awesomeCheckboxGroup(inputId = "searchPTC_AdditivesForCultures", label = "", choices = c(loadData("tblAdditives")$Additives), selected = NULL, status = "info") )), br(), br(),br(),br(), br(),br(), br(),br(),br(), br(), actionBttn("searchPTC_Culture_Save","Save Culture", style = "fill", size = "xs", color = "primary") ) ) ) )) }) } else { shinyalert::shinyalert("", "Select at least one Identity in the table", type = "warning") } }) searchPTC_Culture_MandatoryFields <- c("searchPTC_SelectedIdentity","searchPTC_NumberOfCultures","searchPTC_CulturedBy", "searchPTC_MediaForCultures", "searchPTC_LabBookNumberForCultures", "searchPTC_PageNumberForCultures") observe({ # check if all mandatory fields have a value mandatoryFilled <- vapply(searchPTC_Culture_MandatoryFields, function(x) { !is.null(input[[x]]) && input[[x]] != "" }, logical(1)) mandatoryFilled <- all(mandatoryFilled) # enable/disable the submit button shinyjs::toggleState(id = "searchPTC_Culture_Save", "Save Culture", condition = mandatoryFilled) }) # Save observeEvent(input$searchPTC_Culture_Save,{ tb <- paste0(input$project_selected, "_tblCulturesPlantTissueCulture") dt <- data.frame( PTCIdentity = input$searchPTC_SelectedIdentity, NumberOfCultures = input$searchPTC_NumberOfCultures, DateOfCulture = input$searchPTC_DateOfCulture, CulturedBy = input$searchPTC_CulturedBy, Comments = ifelse(nchar(input$searchPTC_Comments)>0,input$searchPTC_Comments,''), MediaForCultures = input$searchPTC_MediaForCultures, AdditivesForCultures = ifelse(!is.null(input$searchPTC_AdditivesForCultures),input$searchPTC_AdditivesForCultures,''), LabBookNumberForCultures = input$searchPTC_LabBookNumberForCultures, PageNumberForCultures = input$searchPTC_PageNumberForCultures ) try(expr = dbWriteTable(conn = pool, name = tb, value = dt, overwrite = F, append = T)) #saveData(dt, tb) shinyalert("Success!", "Record Saved", type = "success") shinyjs::reset("searchPTC_form") shinyjs::hide("searchPTC_Culture_Form") shinyjs::hide("searchPTC_Table") shinyjs::hide("searchPTCDeleted_Table") shinyjs::hide("searchPTC_Culture_Table") }) # delete selected observeEvent(input$searchPTC_DeleteSelected,{ confirmSweetAlert( session = session, inputId = "searchPTC_DeleteSelected_Confirm", type = "warning", title = "", text = "Do you really want to DELETE the record?", btn_labels = c("Cancel", "Yes, Delete!"), btn_colors = c("#DD6B55", "#04B404") ) }) observeEvent(input$searchPTC_DeleteSelected_Confirm,{ if(input$searchPTC_DeleteSelected_Confirm==TRUE){ r <- input$searchPTC_Table_select$select$r c <- searchPTC_input()[r,] id <- c$PTCIdentity if(input$userName %in% isAdmin$UserName){ if(length(c>0)){ tb <- paste0(input$project_selected,"_tblPlantTissueCulture") tb2 <- paste0(input$project_selected, "_tblDeletedPlantTissueCulture") sql <- paste("DELETE FROM",tb,"WHERE PTCIdentity = ?id;") query <- sqlInterpolate(pool, sql, id = id) dbExecute(pool, query)# delete dbWriteTable(conn = pool, name = "tblDeletedPlantTissueCulture", value = c, overwrite = F, append = T)# save deleted record reset("searchPTC_form") shinyjs::hide("searchPTC_Table") shinyjs::hide("searchPTCDeleted_Table") shinyjs::hide("searchPTC_Culture_Form") } }else { shinyalert("Oops!", "You don't have permissions to delete this record.", type = "error") } } }) ## UPDATING LAST SUBCULTURE observeEvent(input$plant_tissue_culture_module,{ updateSelectInput(session, "updating_last_subculture_PTCIdentity", "Plant Tissue Culture Identity", choices = c("",culturesPTC()$PTCIdentity)) }) observeEvent(input$updating_last_subculture_PTCLoadData,{ dt <- culturesPTC() %>% dplyr::filter(trimws(PTCIdentity) == trimws(input$updating_last_subculture_PTCIdentity)) updateNumericInput(session, "updating_last_subculture_PTCNumberOfCultures", "Number of Cultures", value = dt$NumberOfCultures[1]) updateDateInput(session, "updating_last_subculture_PTCDateOfCulture", "Date of Culture", value = dt$DateOfCulture[1]) updateSelectInput(session, 'updating_last_subculture_PTCCulturedBy', "Cultured By", choices = c(dt$CulturedBy), selected = dt$CulturedBy[1]) updateSelectInput(session, "updating_last_subculture_PTCMedia", "Media", choices = c(dt$Media), selected = dt$Media[1]) updateTextInput(session, "updating_last_subculture_PTCAdditives", "Additives", value = dt$Additives[1]) updateNumericInput(session, "updating_last_subculture_PTCLabBookNumber", "Lab Book Number", value = dt$LabBookNumber[1]) updateNumericInput(session, "updating_last_subculture_PTCPageNumber", "Page Number", value = dt$PageNumber[1]) updateTextAreaInput(session, "updating_last_subculture_PTCComments", "Comments", value = dt$Comments[1]) }) # Update observeEvent(input$updating_last_subculture_PTCUpdate,{ id <- trimws(input$updating_last_subculture_PTCIdentity) NumberOfCultures = input$updating_last_subculture_PTCNumberOfCultures Comments = input$updating_last_subculture_PTCComments tb <- paste0(input$project_selected, "_tblCulturesPlantTissueCulture") sql <- paste("UPDATE ", tb, "SET NumberOfCultures = ?val1, Comments = ?val2 WHERE PTCIdentity = ?id1;") query <- sqlInterpolate(pool, sql, val1 = NumberOfCultures, val2 = Comments, id1 = id) dbExecute(pool, query) shinyalert("Success!", "Record updated", type = "success") reset("updating_last_subculture_PTCForm") }) # Clear observeEvent(input$updating_last_subculture_PTCClear,{ confirmSweetAlert( session = session, inputId = "updating_last_subculture_PTCClear_Confirm", type = "", title = "", text = "Do you really want to clear the fields?", btn_labels = c("Cancel", "Yes, Clear!"), btn_colors = c("#D3D3D3", "#DD6B55") ) }) observeEvent(input$updating_last_subculture_PTCClear_Confirm, { if(input$updating_last_subculture_PTCClear_Confirm == TRUE){ reset("updating_last_subculture_PTCForm") } }, ignoreInit = TRUE) ## NEW PLANT TISSUE CULTURE observeEvent(input$plant_tissue_culture_module, { updateSelectInput(session, "new_PTC_TPCID", "TPC ID", choices = c('', unique(PTC()$TPCIdentity))) updateSelectInput(session, "new_PTC_UPCID", "UPC ID", choices = c('', unique(PTC()$UPCIdentity))) updateSelectInput(session, "new_PTC_ExplantID", "Explant ID", choices = c('', unique(PTC()$ExplantID))) }) new_PTC_Input <- reactive({ dt <- PTC() if(input$new_PTC_TPCID != ""){ dt <- dt %>% dplyr::filter(TPCIdentity == input$new_PTC_TPCID) } if(input$new_PTC_UPCID != ""){ dt <- dt %>% dplyr::filter(TPCIdentity == input$new_PTC_UPCID) } if(input$new_PTC_ExplantID != ""){ dt <- dt %>% dplyr::filter(TPCIdentity == input$new_PTC_ExplantID) } dt }) ## update fields on data loading observeEvent(input$new_PTC_ID_LoadData,{ #updateTextInput(session, "new_PTC_VectorID", "VectorID", value = new_PTC_Input()$VectorID1) updateTextInput(session, "new_PTC_VectorID1_PromoterGene","Vector ID 1 Promoter-Gene", value = new_PTC_Input()$VectorID1) updateTextInput(session, "new_PTC_VectorID2_PromoterGene","Vector ID 2 Promoter-Gene", value = new_PTC_Input()$VectorID2) #updateTextInput(session, "new_PTC_VirusIndexed", "Virus Indexed") updateTextInput(session, "new_PTC_PTCIdentity","PTC Identity", value = new_PTC_Input()$PTCIdentity) updateTextInput(session, "new_PTC_IdentityType","Identity Type", value = new_PTC_Input()$IdentityType) updateSelectInput(session, "new_PTC_Media", "Media", choices = c(new_PTC_Input()$Media)) updatePrettyCheckboxGroup(session, "new_PTC_Additives", "Additives", choices = new_PTC_Input()$Additives, animation = "jelly") }) # update fields on New input observeEvent(input$new_PTC_source,{ tb <- input$project_selected cultivar <- tbl(pool,"tblCultivar") %>% collect() source <- tbl(pool,"tblSource") %>% collect() # virusIndexedBy <- tbl(pool, paste0(tb, "_tblVirusIndexedBy")) %>% collect() permitType <- tbl(pool, "tblPermitType") %>% collect() if(input$new_PTC_source == "New"){ updateSelectInput(session, "new_PTC_Cultivar", "Cultivar", choices = c('', cultivar$Cultivar)) updateSelectInput(session, "new_PTC_Source", "Source", choices = c('', source$Source)) updateSelectInput(session, "new_PTC_VirusIndexedBy", "Virus Indexed By", choices = input$username) updateSelectInput(session, "new_PTC_PermitType1A", "", choices = permitType$PermitType) updateSelectInput(session, "new_PTC_PermitType1B", "", choices = permitType$PermitType) updateSelectInput(session, "new_PTC_PermitType2A", "", choices = permitType$PermitType) updateSelectInput(session, "new_PTC_PermitType2B", "", choices = permitType$PermitType) updateSelectInput(session, "new_PTC_PermitType3A", "", choices = permitType$PermitType) updateSelectInput(session, "new_PTC_PermitType3B", "", choices = permitType$PermitType) } }) ## Load Data on PTC Identity Input observeEvent(input$new_PTC_LoadData,{ }) ## Update PTC Identity observeEvent(input$new_PTC_Update,{ }) ## Save record observeEvent(input$new_PTC_SaveStarterCultureAndSubCulture,{ }) ## Save Culture #observeEvent(input$,{ # tb <- IBBTV_tblCulturesPlantTissueCulture #}) ## Clear observeEvent(input$new_PTC_ClearTheForm,{ reset("new_PTC_form") }) # DEPLOYMENT observeEvent(input$plant_tissue_culture_module, { updateSelectInput(session, "PTC_deployment_TPLIdentity","TPL Identity", choices = c('', unique(PTC()$TPCIdentity))) updateSelectInput(session, "new_PTC_UPCID", "UPC ID", choices = c('', unique(PTC()$UPCIdentity))) updateSelectInput(session, "new_PTC_ExplantID", "Explant ID", choices = c('', unique(PTC()$ExplantID))) }) observeEvent(input$PTC_deployment_TPLGetVectorData,{ })

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/Code/S4_analysis_code.R

efb2ce092e5e7f8bcda3d434f1cc578bdac9edcc

[]

no_license

philipclare/missing_data_simulation

28df0089cb388ceb767ff1b04309762298fa34c4

54bba7aae4702f1d1ee1c7e1e573211b50ad948f

refs/heads/master

2020-04-29T03:18:55.805749

2019-07-22T03:21:08

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R

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64,883

r

S4_analysis_code.R

args <- commandArgs(trailingOnly = TRUE) # args <- c(1,10,"C:/Users/z3312911/Cloudstor/PhD/Katana/missingsim/") start <- as.numeric(args[1]) stop <- as.numeric(args[2]) n <- stop-start+1 nimp <- 40 packages <- paste0("/home/z3312911/RPackages/") .libPaths(packages) load(paste0(args[3],"seeds.RData")) set.seed(eval(seeds[as.numeric(stop/n)])) filepath1 <- paste0(args[3],"Data/S1/") filepath2 <- paste0(args[3],"Data/S2/") filepath3 <- paste0(args[3],"Data/S3/") ## Check required packages are installed, and if not, install them libs <- c("nnls","SuperLearner","Matrix","foreach","glmnet","ranger","lme4","geepack","parallel", "doParallel","mvtnorm","survival","TH.data","MASS","splines","boot","haven","ggplot2", "multcomp","doBy","gam","future","stats","data.table","optimx","mitml","Amelia","mice","norm") missing <- !libs %in% installed.packages() if (any(missing)) { install.packages(libs[missing],repos="https://cloud.r-project.org") } library("nnls") library("SuperLearner") library("Matrix") library("foreach") library("glmnet") library("ranger") library("lme4") library("geepack") library("parallel") library("doParallel") library("boot") library("haven") library("ggplot2") library("multcomp") library("doBy") library("gam") library("future") library("stats") library("data.table") library("optimx") library("plyr") library("ltmle") library("mitml") library("mice") library("norm") library("Amelia") library("clubSandwich") ## Define custom RF wrapper with slightly fewer trees (based on previous work, this is all that is required for this data) create.Learner("SL.ranger", params = list(num.trees = 250)) ## Program to truncate inverse probability weights ## Used to reduce variability in weights, which can cause issues checkrange <- function(v) { v <- ifelse(v<0.001,0.001,v) v <- ifelse(v>0.999,ifelse(is.na(v),v,0.999),v) } ## Complete-case analysis syntax simcomp <- function(data) { data<-data[complete.cases(data),] # Define analytic models for outcome, propensities, etc outmodel <- "y ~ a + la + l + ll + oa + ob + oc + obs" outmodel2 <- "y ~ a + la + ll + oa + ob + oc + obs" propa0model <- "la ~ ll + oa + ob + oc + obs" propa1model <- "a ~ la + l + ll + oa + ob + oc + obs" # qform/gform is essentially the same, but in the form required by the package ltmle qform <- c(l="Q.kplus1 ~ la + ll + obs + oa + ob + oc", y="Q.kplus1 ~ a + la + l + la + oa + ob + oc + obs") gform1 <- c(la="la ~ ll + oa + ob + oc + obs", a="a ~ la + l + ll + oa + ob + oc + obs") create.Learner("SL.ranger", params = list(num.trees = 250)) # same as in top-level code - replicated because sometimes goes wrong in parallel # Define libraries to be used by SuperLearner # Top set is set to be used in final analysis. Second set is used for testing. SLlib1 <- c("SL.glm","SL.glm.interaction","SL.gam","SL.ranger_1") SLlib2 <- list(g=c("SL.glm","SL.glm.interaction","SL.gam","SL.ranger_1"), Q=c("SL.glm","SL.glm.interaction","SL.gam")) SLlib1 <- c("SL.glm","SL.glm.interaction") SLlib2 <- list(g=c("SL.glm","SL.glm.interaction"), Q=c("SL.glm","SL.glm.interaction")) # GLM IPTW-based analysis GLexp0 <- glm(formula=propa0model,data=data,family=binomial) GLexp1 <- glm(formula=propa1model,data=data,family=binomial) GLexpp <- data.table(cbind(id=data$id,obs=data$obs,a=data$a,la=data$la, propa=ifelse(data$a==1,checkrange(predict(GLexp1,type="response")),checkrange(1-predict(GLexp1,type="response"))), propla=ifelse(data$la==1,checkrange(predict(GLexp0,type="response")),checkrange(1-predict(GLexp0,type="response"))))) GLexpp$p <- GLexpp$propla*GLexpp$propa GLexpp$GLwt <- 1/GLexpp$p # GLM IPT and DR-IPT analysis GLiptw <- glm(y~a+la,data=merge(data,GLexpp[,c("id","obs","GLwt")]),weight=GLwt,family=gaussian) GLdriptw <- glm(outmodel2,data=merge(data,GLexpp[,c("id","obs","GLwt")]),weight=GLwt,family=gaussian) # SuperLearner IPTW-based analysis SLexp0 <- SuperLearner(Y=as.vector(data[,]$la),X=data[,c("obs","oa","ob","oc","ll")],id=data[,1],SL.library=SLlib1,family=binomial) SLexp1 <- SuperLearner(Y=as.vector(data[,]$a),X=data[,c("obs","oa","ob","oc","ll","la","l")],id=data[,1],SL.library=SLlib1,family=binomial) SLexpp <- data.table(cbind(id=data$id,obs=data$obs,a=data$a,la=data$la, propa=ifelse(data$a==1,checkrange(predict(SLexp1)$pred),checkrange(1-predict(SLexp1)$pred)), propla=ifelse(data$la==1,checkrange(predict(SLexp0)$pred),checkrange(1-predict(SLexp0)$pred)))) SLexpp$p <- SLexpp$propla*SLexpp$propa SLexpp$SLwt <- 1/SLexpp$p # SL IPT and DR-IPT analysis SLiptw <- glm(y~a+la,data=merge(data,SLexpp[,c("id","obs","SLwt")]),weight=SLwt,family=gaussian) SLdriptw <- glm(outmodel2,data=merge(data,SLexpp[,c("id","obs","SLwt")]),weight=SLwt,family=gaussian) # GLM TMLE GLtmle <- ltmle(data[,c("obs","oa","ob","oc","ll","la","l","a","y")], id=data[,1], Anodes=c("la","a"), Lnodes=c("l"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), estimate.time = FALSE) # SuperLearner TMLE SLtmle <- ltmle(data[,c("obs","oa","ob","oc","ll","la","l","a","y")], id=data[,1], Anodes=c("la","a"), Lnodes=c("l"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), SL.library = SLlib2, estimate.time = FALSE) # Naive analysis GLM <- glm(outmodel,data=data,family=gaussian) RI <- lmer(paste0(outmodel,"+(1|id)"),data=data) GEE <- geeglm(formula(outmodel),data=data,id=data$id,waves=data$obs,family=gaussian) c(coef(summary(GLM))[2,1]+coef(summary(GLM))[3,1],sqrt(vcovCR(GLM, cluster=data$id, type = "CR3")[2,2] + vcovCR(GLM, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(GLM, cluster=data$id, type = "CR3")[2,3]), coef(summary(RI))[2,1]+coef(summary(RI))[3,1],sqrt(vcov(RI)[2,2] + vcov(RI)[3,3] + 2*vcov(RI)[2,3]), coef(summary(GEE))[2,1]+coef(summary(GEE))[3,1],sqrt(summary(GEE)$cov.scaled[2,2] + summary(GEE)$cov.scaled[3,3] + 2*summary(GEE)$cov.scaled[2,3]), coef(summary(GLiptw))[2,1]+coef(summary(GLiptw))[3,1],sqrt(vcovCR(GLiptw, cluster=data$id, type = "CR3")[2,2] + vcovCR(GLiptw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(GLiptw, cluster=data$id, type = "CR3")[2,3]), coef(summary(SLiptw))[2,1]+coef(summary(SLiptw))[3,1],sqrt(vcovCR(SLiptw, cluster=data$id, type = "CR3")[2,2] + vcovCR(SLiptw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(SLiptw, cluster=data$id, type = "CR3")[2,3]), coef(summary(GLdriptw))[2,1]+coef(summary(GLdriptw))[3,1],sqrt(vcovCR(GLdriptw, cluster=data$id, type = "CR3")[2,2] + vcovCR(GLdriptw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(GLdriptw, cluster=data$id, type = "CR3")[2,3]), coef(summary(SLdriptw))[2,1]+coef(summary(SLdriptw))[3,1],sqrt(vcovCR(SLdriptw, cluster=data$id, type = "CR3")[2,2] + vcovCR(SLdriptw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(SLdriptw, cluster=data$id, type = "CR3")[2,3]), summary(GLtmle)$effect.measures$ATE$estimate,summary(GLtmle)$effect.measures$ATE$std.dev, summary(SLtmle)$effect.measures$ATE$estimate,summary(SLtmle)$effect.measures$ATE$std.dev) } ## Missing Data analysis syntax simmiss <- function(data,nimpute,scenario) { if (scenario==1) { # Define analytic models for outcome, propensities, etc outmodel <- "y ~ a + la + l + ll + oa + ob + oc + obs" outmodel2 <- "y ~ a + la + ll + oa + ob + oc + obs" propa0model <- "la ~ ll + oa + ob + oc + obs" propa1model <- "a ~ la + l + ll + oa + ob + oc + obs" missmodel <- "c ~ a + la + l + ll + oa + ob + oc + obs" # qform/gform is essentially the same, but in the form required by the package ltmle qform <- c(l="Q.kplus1 ~ la + ll + obs + oa + ob + oc", y="Q.kplus1 ~ a + la + l + la + oa + ob + oc + obs") gform1 <- c(la="la ~ ll + oa + ob + oc + obs", a="a ~ la + l + ll + oa + ob + oc + obs") gform2 <- c(la="la ~ ll + oa + ob + oc + obs", a="a ~ la + l + ll + oa + ob + oc + obs", c="c ~ a + la + l + ll + oa + ob + oc + obs") # Define imputation formulae for jomo impute impfmlc <- y + a ~ la + l + ll + oa + ob + oc + obs impfml <- y + a ~ l + ll + oa + ob + oc + obs + (1|id) # Reshape data into wide format and drop all lagged variables except those at baseline datawide <- data datawide$id <- as.numeric(levels(datawide$id))[datawide$id] datawide <- reshape(datawide[,c(-10)], v.names=c("ll","la","l","a","y"), idvar="id", timevar="obs", direction="wide")[,c(-10,-11,-15,-16,-20,-21)] # Standard-FCS imputation via MICE # Run imputation and save as list of data frames impdatafcs <- complete(mice(datawide[,-1], m=nimpute, maxit=10), action="all") # Reshape back into long form for analysis, included re-creating lagged variables impdatafcs <- lapply(impdatafcs, function (x) { x <- cbind(id=seq.int(nrow(x)),x) x$ll.2 <- x$l.1 x$ll.3 <- x$l.2 x$ll.4 <- x$l.3 x$la.2 <- x$a.1 x$la.3 <- x$a.2 x$la.4 <- x$a.3 x <- data.frame(x[,c(1:9,19,22,10:12,20,23,13:15,21,24,16:18)]) x <- reshape(x,varying=list(c(5,10,15,20), c(6,11,16,21), c(7,12,17,22), c(8,13,18,23), c(9,14,19,24)), v.names=c("ll","la","l","a","y"), times=c(1,2,3,4), sep=".", idvar="id", timevar="obs", direction="long") x <- data.frame(x) }) # JM imputation via MVN # Run imputation and save as list of data frames s <- prelim.norm(as.matrix(datawide[,-1])) thetahat <- em.norm(s) rngseed(269012) theta <- da.norm(s, thetahat, steps=100, showits=TRUE) ximp <- lapply(vector("list", length=nimpute),function (x) {data.frame(imp.norm(s,theta,as.matrix(datawide[,-1])))}) # Reshape back into long form for analysis, included re-creating lagged variables impdatamvn <- lapply(ximp, function (x) { x <- cbind(id=seq.int(nrow(x)),x) x$ll.2 <- x$l.1 x$ll.3 <- x$l.2 x$ll.4 <- x$l.3 x$la.2 <- x$a.1 x$la.3 <- x$a.2 x$la.4 <- x$a.3 x <- data.frame(x[,c(1:9,19,22,10:12,20,23,13:15,21,24,16:18)]) x <- reshape(x,varying=list(c(5,10,15,20), c(6,11,16,21), c(7,12,17,22), c(8,13,18,23), c(9,14,19,24)), v.names=c("ll","la","l","a","y"), times=c(1,2,3,4), sep=".", idvar="id", timevar="obs", direction="long") x <- data.frame(x) }) # Random-intercept imputation via jomo # Transform binary variables into factors (required for JOMO to run) data$ll <- factor(data$ll) data$la <- factor(data$la) data$l <- factor(data$l) data$a <- factor(data$a) # Run imputation and save as list of data frames impdatajomo <- mitmlComplete(jomoImpute(data=data.frame(data[,!(names(data) %in% "c")]),formula=impfml,m=nimpute,n.burn=100,n.iter=10),print="all") data$ll <- as.numeric(levels(data$ll))[data$ll] data$la <- as.numeric(levels(data$la))[data$la] data$l <- as.numeric(levels(data$l))[data$l] data$a <- as.numeric(levels(data$a))[data$a] } else { # Define analytic models for outcome, propensities, etc outmodel <- "y ~ a + la + l + ll + oa + ob + oc + obs" outmodel2 <- "y ~ a + la + ll + oa + ob + oc + obs" propa0model <- "la ~ ll + oa + ob + oc + obs" propa1model <- "a ~ la + l + ll + oa + ob + oc + obs" missmodel <- "c ~ a + la + w + lw + oa + ob + oc + obs" # qform/gform is essentially the same, but in the form required by the package ltmle qform <- c(l="Q.kplus1 ~ la + ll + obs + oa + ob + oc", y="Q.kplus1 ~ a + la + l + la + oa + ob + oc + obs") gform1 <- c(la="la ~ ll + oa + ob + oc + obs", a="a ~ la + l + ll + oa + ob + oc + obs") gform2 <- c(la="la ~ ll + oa + ob + oc + obs", a="a ~ la + l + ll + oa + ob + oc + obs", c="c ~ a + la + w + lw + oa + ob + oc + obs") # Define imputation formulae for jomo impute impfmlc <- y + a ~ la + l + ll + w + lw + oa + ob + oc + obs impfml <- y + a ~ l + ll + w + lw + oa + ob + oc + obs + (1|id) # Reshape data into wide format and drop all lagged variables except those at baseline datawide <- data datawide$id <- as.numeric(levels(datawide$id))[datawide$id] datawide <- reshape(datawide[,c(-12)], v.names=c("ll","lw","la","l","w","a","y"), idvar="id", timevar="obs", direction="wide")[,c(-12,-13,-14,-19,-20,-21,-26,-27,-28)] # Standard-FCS imputation via MICE # Run imputation and save as list of data frames impdatafcs <- complete(mice(datawide[,-1], m=nimpute, maxit=10), action="all") # Reshape back into long form for analysis, included re-creating lagged variables impdatafcs <- lapply(impdatafcs, function (x) { x <- cbind(id=seq.int(nrow(x)),x) x$ll.2 <- x$l.1 x$ll.3 <- x$l.2 x$ll.4 <- x$l.3 x$lw.2 <- x$w.1 x$lw.3 <- x$w.2 x$lw.4 <- x$w.3 x$la.2 <- x$a.1 x$la.3 <- x$a.2 x$la.4 <- x$a.3 x <- data.frame(x[,c(1:11,24,27,30,12:15,25,28,31,16:19,26,29,32,20:23)]) x <- reshape(x,varying=list(c(5,12,19,26), c(6,13,20,27), c(7,14,21,28), c(8,15,22,29), c(9,16,23,30), c(10,17,24,31), c(11,18,25,32)), v.names=c("ll","lw","la","l","w","a","y"), times=c(1,2,3,4), sep=".", idvar="id", timevar="obs", direction="long") x <- data.frame(x) }) # Random-intercept imputation via MVN # Run imputation and save as list of data frames s <- prelim.norm(as.matrix(datawide[,-1])) thetahat <- em.norm(s) rngseed(269012) theta <- da.norm(s, thetahat, steps=100, showits=TRUE) ximp <- lapply(vector("list", length=nimpute),function (x) {data.frame(imp.norm(s,theta,as.matrix(datawide[,-1])))}) # Reshape back into long form for analysis, included re-creating lagged variables impdatamvn <- lapply(ximp, function (x) { x <- cbind(id=seq.int(nrow(x)),x) x$ll.2 <- x$l.1 x$ll.3 <- x$l.2 x$ll.4 <- x$l.3 x$lw.2 <- x$w.1 x$lw.3 <- x$w.2 x$lw.4 <- x$w.3 x$la.2 <- x$a.1 x$la.3 <- x$a.2 x$la.4 <- x$a.3 x <- data.frame(x[,c(1:11,24,27,30,12:15,25,28,31,16:19,26,29,32,20:23)]) x <- reshape(x,varying=list(c(5,12,19,26), c(6,13,20,27), c(7,14,21,28), c(8,15,22,29), c(9,16,23,30), c(10,17,24,31), c(11,18,25,32)), v.names=c("ll","lw","la","l","w","a","y"), times=c(1,2,3,4), sep=".", idvar="id", timevar="obs", direction="long") x <- data.frame(x) }) # Random-intercept imputation via jomo # Transform binary variables into factors (required for JOMO to run) data$ll <- factor(data$ll) data$la <- factor(data$la) data$lw <- factor(data$la) data$l <- factor(data$l) data$a <- factor(data$a) data$w <- factor(data$a) # Run imputation and save as list of data frames impdatajomo <- mitmlComplete(jomoImpute(data=data.frame(data[,!(names(data) %in% "c")]),formula=impfml,m=nimpute,n.burn=100,n.iter=10),print="all") data$ll <- as.numeric(levels(data$ll))[data$ll] data$la <- as.numeric(levels(data$la))[data$la] data$l <- as.numeric(levels(data$l))[data$l] data$a <- as.numeric(levels(data$a))[data$a] } create.Learner("SL.ranger", params = list(num.trees = 250)) # same as in top-level code - replicated because sometimes goes wrong in parallel # Define libraries to be used by SuperLearner # Top set is set to be used in final analysis. Second set is used for testing. SLlib1 <- c("SL.glm","SL.glm.interaction","SL.gam","SL.ranger_1") SLlib2 <- list(g=c("SL.glm","SL.glm.interaction","SL.gam","SL.ranger_1"), Q=c("SL.glm","SL.glm.interaction","SL.gam")) SLlib1 <- c("SL.glm","SL.glm.interaction") SLlib2 <- list(g=c("SL.glm","SL.glm.interaction"), Q=c("SL.glm","SL.glm.interaction")) # GLM IPTW-based analysis GLexp0 <- glm(formula=propa0model,data=data,family=binomial) GLexp1 <- glm(formula=propa1model,data=data,family=binomial) GLexpc <- glm(formula=missmodel,data=data,family=binomial) GLexpp <- data.table(cbind(id=data$id,obs=data$obs,a=data$a,la=data$la, propa=ifelse(data$a==1,checkrange(predict(GLexp1,type="response")),checkrange(1-predict(GLexp1,type="response"))), propla=ifelse(data$la==1,checkrange(predict(GLexp0,type="response")),checkrange(1-predict(GLexp0,type="response"))), propc=checkrange(1-predict(GLexpc,type="response")))) GLexpp$p <- GLexpp$propla*GLexpp$propa GLexpp$pc <- GLexpp$propla*GLexpp$propa*GLexpp$propc GLexpp$GLwt <- 1/GLexpp$p GLexpp$GLwtc <- 1/GLexpp$pc GLexpp$GLwc <- 1/GLexpp$propc # IPC-weighted GLM IPT and DR-IPT analysis GLiptcw <- glm(y~a+la,data=merge(data,GLexpp[,c("id","obs","GLwtc")]),weight=GLwtc,family=gaussian) GLdriptcw <- glm(outmodel2,data=merge(data,GLexpp[,c("id","obs","GLwtc")]),weight=GLwtc,family=gaussian) # SuperLearner IPTW-based analysis SLexp0 <- SuperLearner(Y=as.vector(data[,]$la),X=data[,c("obs","oa","ob","oc","ll")],id=data[,1],SL.library=SLlib1,family=binomial) SLexp1 <- SuperLearner(Y=as.vector(data[,]$a),X=data[,c("obs","oa","ob","oc","ll","la","l")],id=data[,1],SL.library=SLlib1,family=binomial) SLexpc <- SuperLearner(Y=as.vector(data$c),X=data[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a") else c("obs","oa","ob","oc","lw","la","w","a")],id=data[,1],SL.library=SLlib1,family=binomial) SLexpp <- data.table(cbind(id=data$id,obs=data$obs,a=data$a,la=data$la, propa=ifelse(data$a==1,checkrange(predict(SLexp1)$pred),checkrange(1-predict(SLexp1)$pred)), propla=ifelse(data$la==1,checkrange(predict(SLexp0)$pred),checkrange(1-predict(SLexp0)$pred)), propc=as.vector(checkrange(1-predict(SLexpc)$pred)))) SLexpp$p <- SLexpp$propla*SLexpp$propa SLexpp$pc <- SLexpp$propla*SLexpp$propa*SLexpp$propc SLexpp$SLwt <- 1/SLexpp$p SLexpp$SLwtc <- 1/SLexpp$pc SLexpp$SLwc <- 1/SLexpp$propc # IPC-weighted SL IPT and DR-IPT analysis SLiptcw <- glm(y~a+la,data=merge(data,SLexpp[,c("id","obs","SLwtc")]),weight=SLwtc,family=gaussian) SLdriptcw <- glm(outmodel2,data=merge(data,SLexpp[,c("id","obs","SLwtc")]),weight=SLwtc,family=gaussian) data$c <- BinaryToCensoring(is.censored=data$c) # IPC-weighted GLM TMLE GLtmlec <- ltmle(data[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","c","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","c","y")], id=data[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Cnodes="c", Qform=qform, gform=gform2, abar=list(c(1,1),c(0,0)), estimate.time = FALSE) # IPC-weighted SuperLearner TMLE SLtmlec <- ltmle(data[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","c","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","c","y")], id=data[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Cnodes="c", Qform=qform, gform=gform2, abar=list(c(1,1),c(0,0)), SL.library = SLlib2, estimate.time = FALSE) # GLM IPC-weighted naive analysis GLGLMc <- glm(outmodel,data=merge(data,GLexpp[,c("id","obs","GLwc")]),weight=GLwc,family=gaussian) GLRIc <- lmer(paste0(outmodel,"+(1|id)"),data=merge(data,GLexpp[,c("id","obs","GLwc")]),weight=GLwc) GLGEEc <- geeglm(formula(outmodel),data=merge(data,GLexpp[,c("id","obs","GLwc")]),id=data$id,waves=data$obs,weight=GLwc,family=gaussian) # SL IPC-weighted naive analysis SLGLMc <- glm(outmodel,data=merge(data,SLexpp[,c("id","obs","SLwc")]),weight=SLwc,family=gaussian) SLRIc <- lmer(paste0(outmodel,"+(1|id)"),data=merge(data,SLexpp[,c("id","obs","SLwc")]),weight=SLwc) SLGEEc <- geeglm(formula(outmodel),data=merge(data,SLexpp[,c("id","obs","SLwc")]),id=data$id,waves=data$obs,weight=SLwc,family=gaussian) # FCS Multiple Imputation Analyses # Imputed naive analysis # GLM GLMimpfcs <- lapply(impdatafcs, function (x) {glm(formula=formula(outmodel),data=x,family=gaussian)}) GLMimpcofcs <- matrix(unlist(lapply(GLMimpfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLMimpsefcs <- matrix(unlist(lapply(GLMimpfcs, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLMfcs <- matrix(unlist(mi.meld(q=GLMimpcofcs, se=GLMimpsefcs)),nrow=1,ncol=2) # Random intercept RIimpfcs <- lapply(impdatafcs, function (x) {lmer(paste0(outmodel,"+(1|id)"),data=x)}) RIimpcofcs <- matrix(unlist(lapply(RIimpfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) RIimpsefcs <- matrix(unlist(lapply(RIimpfcs, function (x) {sqrt(vcov(x)[2,2] + vcov(x)[3,3] + 2*vcov(x)[2,3])})),nrow=nimpute,ncol=1) RIfcs <- matrix(unlist(mi.meld(q=RIimpcofcs, se=RIimpsefcs)),nrow=1,ncol=2) # GEE GEEimpfcs <- lapply(impdatafcs, function (x) {geeglm(formula(outmodel),data=x,id=x$id,waves=x$obs,family=gaussian)}) GEEimpcofcs <- matrix(unlist(lapply(GEEimpfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GEEimpsefcs <- matrix(unlist(lapply(GEEimpfcs, function (x) {sqrt(summary(x)$cov.scaled[2,2] + summary(x)$cov.scaled[3,3] + 2*summary(x)$cov.scaled[2,3])})),nrow=nimpute,ncol=1) GEEfcs <- matrix(unlist(mi.meld(q=GEEimpcofcs, se=GEEimpsefcs)),nrow=1,ncol=2) # GLM-based IPTW and DR-IPTW GLiptwimpfcs <- lapply(impdatafcs, function (x) { GLexp0 <- glm(formula=propa0model,data=x,family=binomial) GLexp1 <- glm(formula=propa1model,data=x,family=binomial) GLexpp <- data.table(cbind(id=x$id,obs=x$obs,a=x$a,la=x$la, propa=ifelse(x$a==1,checkrange(predict(GLexp1,type="response")),checkrange(1-predict(GLexp1,type="response"))), propla=ifelse(x$la==1,checkrange(predict(GLexp0,type="response")),checkrange(1-predict(GLexp0,type="response"))))) GLexpp$p <- GLexpp$propla*GLexpp$propa GLexpp$GLwt <- 1/GLexpp$p D <- merge(x,GLexpp[,c("id","obs","GLwt")]) D }) GLiptwimpsumfcs <- lapply(GLiptwimpfcs, function (x) {glm(y~a+la,data=x,weight=GLwt,family=gaussian)}) GLiptwimpcofcs <- matrix(unlist(lapply(GLiptwimpsumfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLiptwimpsefcs <- matrix(unlist(lapply(GLiptwimpsumfcs, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLiptwfcs <- matrix(unlist(mi.meld(q=GLiptwimpcofcs, se=GLiptwimpsefcs)),nrow=1,ncol=2) GLdriptwimpsumfcs <- lapply(GLiptwimpfcs, function (x) {glm(outmodel2,data=x,weight=GLwt,family=gaussian)}) GLdriptwimpcofcs <- matrix(unlist(lapply(GLdriptwimpsumfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLdriptwimpsefcs <- matrix(unlist(lapply(GLdriptwimpsumfcs, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLdriptwfcs <- matrix(unlist(mi.meld(q=GLdriptwimpcofcs, se=GLdriptwimpsefcs)),nrow=1,ncol=2) # SuperLearner-based IPTW and DR-IPTW SLiptwimpfcs <- lapply(impdatafcs, function (x) { SLexp0 <- SuperLearner(Y=as.numeric(as.vector(x$la)),X=x[,c("obs","oa","ob","oc","ll")],id=x[,"id"],SL.library=SLlib1,family=binomial) SLexp1 <- SuperLearner(Y=as.numeric(as.vector(x$a)),X=x[,c("obs","oa","ob","oc","ll","la","l")],id=x[,"id"],SL.library=SLlib1,family=binomial) SLexpp <- data.table(cbind(id=x$id,obs=x$obs,a=x$a,la=x$la, propa=ifelse(x$a==1,checkrange(predict(SLexp1)$pred),checkrange(1-predict(SLexp1)$pred)), propla=ifelse(x$la==1,checkrange(predict(SLexp0)$pred),checkrange(1-predict(SLexp0)$pred)))) SLexpp$p <- SLexpp$propla*SLexpp$propa SLexpp$SLwt <- 1/SLexpp$p D <- merge(x,SLexpp[,c("id","obs","SLwt")]) D }) SLiptwimpsumfcs <- lapply(SLiptwimpfcs, function (x) {glm(y~a+la,data=x,weight=SLwt,family=gaussian)}) SLiptwimpcofcs <- matrix(unlist(lapply(SLiptwimpsumfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) SLiptwimpsefcs <- matrix(unlist(lapply(SLiptwimpsumfcs, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) SLiptwfcs <- matrix(unlist(mi.meld(q=SLiptwimpcofcs, se=SLiptwimpsefcs)),nrow=1,ncol=2) SLdriptwimpsumfcs <- lapply(SLiptwimpfcs, function (x) {glm(outmodel2,data=x,weight=SLwt,family=gaussian)}) SLdriptwimpcofcs <- matrix(unlist(lapply(SLdriptwimpsumfcs, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) SLdriptwimpsefcs <- matrix(unlist(lapply(SLdriptwimpsumfcs, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) SLdriptwfcs <- matrix(unlist(mi.meld(q=SLdriptwimpcofcs, se=SLdriptwimpsefcs)),nrow=1,ncol=2) # GLM-based TMLE GLtmleimpfcs <- lapply(impdatafcs, function (x) { ltmle(x[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","y")], id=x[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), estimate.time = FALSE)}) GLtmleimpsumfcs <- lapply(GLtmleimpfcs, function (x) {summary(x)}) GLtmleimpcofcs <- matrix(unlist(lapply(GLtmleimpsumfcs, function (x) {x$effect.measures$ATE$estimate})),nrow=nimpute,ncol=1) GLtmleimpsefcs <- matrix(unlist(lapply(GLtmleimpsumfcs, function (x) {x$effect.measures$ATE$std.dev})),nrow=nimpute,ncol=1) GLtmlefcs <- matrix(unlist(mi.meld(q=GLtmleimpcofcs, se=GLtmleimpsefcs)),nrow=1,ncol=2) # SuperLearner based TMLE SLtmleimpfcs <- lapply(impdatafcs, function (x) { ltmle(x[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","y")], id=x[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), SL.library = SLlib2, estimate.time = FALSE)}) SLtmleimpsumfcs <- lapply(SLtmleimpfcs, function (x) {summary(x)}) SLtmleimpcofcs <- matrix(unlist(lapply(SLtmleimpsumfcs, function (x) {x$effect.measures$ATE$estimate})),nrow=nimpute,ncol=1) SLtmleimpsefcs <- matrix(unlist(lapply(SLtmleimpsumfcs, function (x) {x$effect.measures$ATE$std.dev})),nrow=nimpute,ncol=1) SLtmlefcs <- matrix(unlist(mi.meld(q=SLtmleimpcofcs, se=SLtmleimpsefcs)),nrow=1,ncol=2) # MVN Multiple Imputation Analyses # Imputed naive analysis # GLM GLMimpmvn <- lapply(impdatamvn, function (x) {glm(formula=formula(outmodel),data=x,family=gaussian)}) GLMimpcomvn <- matrix(unlist(lapply(GLMimpmvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLMimpsemvn <- matrix(unlist(lapply(GLMimpmvn, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLMmvn <- matrix(unlist(mi.meld(q=GLMimpcomvn, se=GLMimpsemvn)),nrow=1,ncol=2) # Random intercept RIimpmvn <- lapply(impdatamvn, function (x) {lmer(paste0(outmodel,"+(1|id)"),data=x)}) RIimpcomvn <- matrix(unlist(lapply(RIimpmvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) RIimpsemvn <- matrix(unlist(lapply(RIimpmvn, function (x) {sqrt(vcov(x)[2,2] + vcov(x)[3,3] + 2*vcov(x)[2,3])})),nrow=nimpute,ncol=1) RImvn <- matrix(unlist(mi.meld(q=RIimpcomvn, se=RIimpsemvn)),nrow=1,ncol=2) # GEE GEEimpmvn <- lapply(impdatamvn, function (x) {geeglm(formula(outmodel),data=x,id=x$id,waves=x$obs,family=gaussian)}) GEEimpcomvn <- matrix(unlist(lapply(GEEimpmvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GEEimpsemvn <- matrix(unlist(lapply(GEEimpmvn, function (x) {sqrt(summary(x)$cov.scaled[2,2] + summary(x)$cov.scaled[3,3] + 2*summary(x)$cov.scaled[2,3])})),nrow=nimpute,ncol=1) GEEmvn <- matrix(unlist(mi.meld(q=GEEimpcomvn, se=GEEimpsemvn)),nrow=1,ncol=2) # GLM-based IPTW and DR-IPTW GLiptwimpmvn <- lapply(impdatamvn, function (x) { GLexp0 <- glm(formula=propa0model,data=x,family=binomial) GLexp1 <- glm(formula=propa1model,data=x,family=binomial) GLexpp <- data.table(cbind(id=x$id,obs=x$obs,a=x$a,la=x$la, propa=ifelse(x$a==1,checkrange(predict(GLexp1,type="response")),checkrange(1-predict(GLexp1,type="response"))), propla=ifelse(x$la==1,checkrange(predict(GLexp0,type="response")),checkrange(1-predict(GLexp0,type="response"))))) GLexpp$p <- GLexpp$propla*GLexpp$propa GLexpp$GLwt <- 1/GLexpp$p D <- merge(x,GLexpp[,c("id","obs","GLwt")]) D }) GLiptwimpsummvn <- lapply(GLiptwimpmvn, function (x) {glm(y~a+la,data=x,weight=GLwt,family=gaussian)}) GLiptwimpcomvn <- matrix(unlist(lapply(GLiptwimpsummvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLiptwimpsemvn <- matrix(unlist(lapply(GLiptwimpsummvn, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLiptwmvn <- matrix(unlist(mi.meld(q=GLiptwimpcomvn, se=GLiptwimpsemvn)),nrow=1,ncol=2) GLdriptwimpsummvn <- lapply(GLiptwimpmvn, function (x) {glm(outmodel2,data=x,weight=GLwt,family=gaussian)}) GLdriptwimpcomvn <- matrix(unlist(lapply(GLdriptwimpsummvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLdriptwimpsemvn <- matrix(unlist(lapply(GLdriptwimpsummvn, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLdriptwmvn <- matrix(unlist(mi.meld(q=GLdriptwimpcomvn, se=GLdriptwimpsemvn)),nrow=1,ncol=2) # SuperLearner-based IPTW and DR-IPTW SLiptwimpmvn <- lapply(impdatamvn, function (x) { SLexp0 <- SuperLearner(Y=as.numeric(as.vector(x$la)),X=x[,c("obs","oa","ob","oc","ll")],id=x[,"id"],SL.library=SLlib1,family=binomial) SLexp1 <- SuperLearner(Y=as.numeric(as.vector(x$a)),X=x[,c("obs","oa","ob","oc","ll","la","l")],id=x[,"id"],SL.library=SLlib1,family=binomial) SLexpp <- data.table(cbind(id=x$id,obs=x$obs,a=x$a,la=x$la, propa=ifelse(x$a==1,checkrange(predict(SLexp1)$pred),checkrange(1-predict(SLexp1)$pred)), propla=ifelse(x$la==1,checkrange(predict(SLexp0)$pred),checkrange(1-predict(SLexp0)$pred)))) SLexpp$p <- SLexpp$propla*SLexpp$propa SLexpp$SLwt <- 1/SLexpp$p D <- merge(x,SLexpp[,c("id","obs","SLwt")]) D }) SLiptwimpsummvn <- lapply(SLiptwimpmvn, function (x) {glm(y~a+la,data=x,weight=SLwt,family=gaussian)}) SLiptwimpcomvn <- matrix(unlist(lapply(SLiptwimpsummvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) SLiptwimpsemvn <- matrix(unlist(lapply(SLiptwimpsummvn, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) SLiptwmvn <- matrix(unlist(mi.meld(q=SLiptwimpcomvn, se=SLiptwimpsemvn)),nrow=1,ncol=2) SLdriptwimpsummvn <- lapply(SLiptwimpmvn, function (x) {glm(outmodel2,data=x,weight=SLwt,family=gaussian)}) SLdriptwimpcomvn <- matrix(unlist(lapply(SLdriptwimpsummvn, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) SLdriptwimpsemvn <- matrix(unlist(lapply(SLdriptwimpsummvn, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) SLdriptwmvn <- matrix(unlist(mi.meld(q=SLdriptwimpcomvn, se=SLdriptwimpsemvn)),nrow=1,ncol=2) # GLM-based TMLE GLtmleimpmvn <- lapply(impdatamvn, function (x) { ltmle(x[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","y")], id=x[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), estimate.time = FALSE)}) GLtmleimpsummvn <- lapply(GLtmleimpmvn, function (x) {summary(x)}) GLtmleimpcomvn <- matrix(unlist(lapply(GLtmleimpsummvn, function (x) {x$effect.measures$ATE$estimate})),nrow=nimpute,ncol=1) GLtmleimpsemvn <- matrix(unlist(lapply(GLtmleimpsummvn, function (x) {x$effect.measures$ATE$std.dev})),nrow=nimpute,ncol=1) GLtmlemvn <- matrix(unlist(mi.meld(q=GLtmleimpcomvn, se=GLtmleimpsemvn)),nrow=1,ncol=2) # SuperLearner-based TMLE SLtmleimpmvn <- lapply(impdatamvn, function (x) { ltmle(x[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","y")], id=x[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), SL.library = SLlib2, estimate.time = FALSE)}) SLtmleimpsummvn <- lapply(SLtmleimpmvn, function (x) {summary(x)}) SLtmleimpcomvn <- matrix(unlist(lapply(SLtmleimpsummvn, function (x) {x$effect.measures$ATE$estimate})),nrow=nimpute,ncol=1) SLtmleimpsemvn <- matrix(unlist(lapply(SLtmleimpsummvn, function (x) {x$effect.measures$ATE$std.dev})),nrow=nimpute,ncol=1) SLtmlemvn <- matrix(unlist(mi.meld(q=SLtmleimpcomvn, se=SLtmleimpsemvn)),nrow=1,ncol=2) # JOMO Multiple Imputation Analyses # Imputed naive analysis #GLM GLMimpjomo <- lapply(impdatajomo, function (x) {glm(formula=formula(outmodel),data=x,family=gaussian)}) GLMimpcojomo <- matrix(unlist(lapply(GLMimpjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLMimpsejomo <- matrix(unlist(lapply(GLMimpjomo, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLMjomo <- matrix(unlist(mi.meld(q=GLMimpcojomo, se=GLMimpsejomo)),nrow=1,ncol=2) # Random intercept RIimpjomo <- lapply(impdatajomo, function (x) {lmer(paste0(outmodel,"+(1|id)"),data=x)}) RIimpcojomo <- matrix(unlist(lapply(RIimpjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) RIimpsejomo <- matrix(unlist(lapply(RIimpjomo, function (x) {sqrt(vcov(x)[2,2] + vcov(x)[3,3] + 2*vcov(x)[2,3])})),nrow=nimpute,ncol=1) RIjomo <- matrix(unlist(mi.meld(q=RIimpcojomo, se=RIimpsejomo)),nrow=1,ncol=2) # GEE GEEimpjomo <- lapply(impdatajomo, function (x) {geeglm(formula(outmodel),data=x,id=x$id,waves=x$obs,family=gaussian)}) GEEimpcojomo <- matrix(unlist(lapply(GEEimpjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GEEimpsejomo <- matrix(unlist(lapply(GEEimpjomo, function (x) {sqrt(summary(x)$cov.scaled[2,2] + summary(x)$cov.scaled[3,3] + 2*summary(x)$cov.scaled[2,3])})),nrow=nimpute,ncol=1) GEEjomo <- matrix(unlist(mi.meld(q=GEEimpcojomo, se=GEEimpsejomo)),nrow=1,ncol=2) # GLM-based IPTW and DR-IPTW GLiptwimpjomo <- lapply(impdatajomo, function (x) { GLexp0 <- glm(formula=propa0model,data=x,family=binomial) GLexp1 <- glm(formula=propa1model,data=x,family=binomial) GLexpp <- data.table(cbind(id=x$id,obs=x$obs,a=x$a,la=x$la, propa=ifelse(x$a==1,checkrange(predict(GLexp1,type="response")),checkrange(1-predict(GLexp1,type="response"))), propla=ifelse(x$la==1,checkrange(predict(GLexp0,type="response")),checkrange(1-predict(GLexp0,type="response"))))) GLexpp$p <- GLexpp$propla*GLexpp$propa GLexpp$GLwt <- 1/GLexpp$p D <- merge(x,GLexpp[,c("id","obs","GLwt")]) D }) GLiptwimpsumjomo <- lapply(GLiptwimpjomo, function (x) {glm(y~a+la,data=x,weight=GLwt,family=gaussian)}) GLiptwimpcojomo <- matrix(unlist(lapply(GLiptwimpsumjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLiptwimpsejomo <- matrix(unlist(lapply(GLiptwimpsumjomo, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLiptwjomo <- matrix(unlist(mi.meld(q=GLiptwimpcojomo, se=GLiptwimpsejomo)),nrow=1,ncol=2) GLdriptwimpsumjomo <- lapply(GLiptwimpjomo, function (x) {glm(outmodel2,data=x,weight=GLwt,family=gaussian)}) GLdriptwimpcojomo <- matrix(unlist(lapply(GLdriptwimpsumjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) GLdriptwimpsejomo <- matrix(unlist(lapply(GLdriptwimpsumjomo, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) GLdriptwjomo <- matrix(unlist(mi.meld(q=GLdriptwimpcojomo, se=GLdriptwimpsejomo)),nrow=1,ncol=2) # SuperLearner-based IPTW and DR-IPTW SLiptwimpjomo <- lapply(impdatajomo, function (x) { SLexp0 <- SuperLearner(Y=as.numeric(levels(x$la))[x$la],X=x[,c("obs","oa","ob","oc","ll")],id=x[,"id"],SL.library=SLlib1,family=binomial) SLexp1 <- SuperLearner(Y=as.numeric(levels(x$a))[x$a],X=x[,c("obs","oa","ob","oc","ll","la","l")],id=x[,"id"],SL.library=SLlib1,family=binomial) SLexpp <- data.table(cbind(id=x$id,obs=x$obs,a=x$a,la=x$la, propa=ifelse(x$a==1,checkrange(predict(SLexp1)$pred),checkrange(1-predict(SLexp1)$pred)), propla=ifelse(x$la==1,checkrange(predict(SLexp0)$pred),checkrange(1-predict(SLexp0)$pred)))) SLexpp$p <- SLexpp$propla*SLexpp$propa SLexpp$SLwt <- 1/SLexpp$p D <- merge(x,SLexpp[,c("id","obs","SLwt")]) D }) SLiptwimpsumjomo <- lapply(SLiptwimpjomo, function (x) {glm(y~a+la,data=x,weight=SLwt,family=gaussian)}) SLiptwimpcojomo <- matrix(unlist(lapply(SLiptwimpsumjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) SLiptwimpsejomo <- matrix(unlist(lapply(SLiptwimpsumjomo, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) SLiptwjomo <- matrix(unlist(mi.meld(q=SLiptwimpcojomo, se=SLiptwimpsejomo)),nrow=1,ncol=2) SLdriptwimpsumjomo <- lapply(SLiptwimpjomo, function (x) {glm(outmodel2,data=x,weight=SLwt,family=gaussian)}) SLdriptwimpcojomo <- matrix(unlist(lapply(SLdriptwimpsumjomo, function (x) {coef(summary(x))[2,1]+coef(summary(x))[3,1]})),nrow=nimpute,ncol=1) SLdriptwimpsejomo <- matrix(unlist(lapply(SLdriptwimpsumjomo, function (x) {sqrt(vcovCR(x, cluster=data$id, type = "CR3")[2,2] + vcovCR(x, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(x, cluster=data$id, type = "CR3")[2,3])})),nrow=nimpute,ncol=1) SLdriptwjomo <- matrix(unlist(mi.meld(q=SLdriptwimpcojomo, se=SLdriptwimpsejomo)),nrow=1,ncol=2) # GLM-based TMLE GLtmleimpjomo <- lapply(impdatajomo, function (x) { x$ll <- as.numeric(levels(x$ll))[x$ll] x$la <- as.numeric(levels(x$la))[x$la] x$l <- as.numeric(levels(x$l))[x$l] x$a <- as.numeric(levels(x$a))[x$a] ltmle(x[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","y")], id=x[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), estimate.time = FALSE)}) GLtmleimpsumjomo <- lapply(GLtmleimpjomo, function (x) {summary(x)}) GLtmleimpcojomo <- matrix(unlist(lapply(GLtmleimpsumjomo, function (x) {x$effect.measures$ATE$estimate})),nrow=nimpute,ncol=1) GLtmleimpsejomo <- matrix(unlist(lapply(GLtmleimpsumjomo, function (x) {x$effect.measures$ATE$std.dev})),nrow=nimpute,ncol=1) GLtmlejomo <- matrix(unlist(mi.meld(q=GLtmleimpcojomo, se=GLtmleimpsejomo)),nrow=1,ncol=2) # SuperLearner-based TMLE SLtmleimpjomo <- lapply(impdatajomo, function (x) { x$ll <- as.numeric(levels(x$ll))[x$ll] x$la <- as.numeric(levels(x$la))[x$la] x$l <- as.numeric(levels(x$l))[x$l] x$a <- as.numeric(levels(x$a))[x$a] ltmle(x[,if (scenario==1) c("obs","oa","ob","oc","ll","la","l","a","y") else c("obs","oa","ob","oc","ll","lw","la","l","w","a","y")], id=x[,"id"], Anodes=c("la","a"), Lnodes=if (scenario==1) c("l") else c("l","w"), Ynodes="y", Qform=qform, gform=gform1, abar=list(c(1,1),c(0,0)), SL.library = SLlib2, estimate.time = FALSE)}) SLtmleimpsumjomo <- lapply(SLtmleimpjomo, function (x) {summary(x)}) SLtmleimpcojomo <- matrix(unlist(lapply(SLtmleimpsumjomo, function (x) {x$effect.measures$ATE$estimate})),nrow=nimpute,ncol=1) SLtmleimpsejomo <- matrix(unlist(lapply(SLtmleimpsumjomo, function (x) {x$effect.measures$ATE$std.dev})),nrow=nimpute,ncol=1) SLtmlejomo <- matrix(unlist(mi.meld(q=SLtmleimpcojomo, se=SLtmleimpsejomo)),nrow=1,ncol=2) # Combine results into vector c(coef(summary(GLGLMc))[2,1]+coef(summary(GLGLMc))[3,1],sqrt(vcovCR(GLGLMc, cluster=data$id, type = "CR3")[2,2] + vcovCR(GLGLMc, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(GLGLMc, cluster=data$id, type = "CR3")[2,3]), coef(summary(GLRIc))[2,1]+coef(summary(GLRIc))[3,1],sqrt(vcov(GLRIc)[2,2] + vcov(GLRIc)[3,3] + 2*vcov(GLRIc)[2,3]), coef(summary(GLGEEc))[2,1]+coef(summary(GLGEEc))[3,1],sqrt(summary(GLGEEc)$cov.scaled[2,2] + summary(GLGEEc)$cov.scaled[3,3] + 2*summary(GLGEEc)$cov.scaled[2,3]), coef(summary(GLiptcw))[2,1]+coef(summary(GLiptcw))[3,1],sqrt(vcovCR(GLiptcw, cluster=data$id, type = "CR3")[2,2] + vcovCR(GLiptcw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(GLiptcw, cluster=data$id, type = "CR3")[2,3]), coef(summary(GLdriptcw))[2,1]+coef(summary(GLdriptcw))[3,1],sqrt(vcovCR(GLdriptcw, cluster=data$id, type = "CR3")[2,2] + vcovCR(GLdriptcw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(GLdriptcw, cluster=data$id, type = "CR3")[2,3]), summary(GLtmlec)$effect.measures$ATE$estimate,summary(GLtmlec)$effect.measures$ATE$std.dev, coef(summary(SLGLMc))[2,1]+coef(summary(SLGLMc))[3,1],sqrt(vcovCR(SLGLMc, cluster=data$id, type = "CR3")[2,2] + vcovCR(SLGLMc, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(SLGLMc, cluster=data$id, type = "CR3")[2,3]), coef(summary(SLRIc))[2,1]+coef(summary(SLRIc))[3,1],sqrt(vcov(SLRIc)[2,2] + vcov(SLRIc)[3,3] + 2*vcov(SLRIc)[2,3]), coef(summary(SLGEEc))[2,1]+coef(summary(SLGEEc))[3,1],sqrt(summary(SLGEEc)$cov.scaled[2,2] + summary(SLGEEc)$cov.scaled[3,3] + 2*summary(SLGEEc)$cov.scaled[2,3]), coef(summary(SLiptcw))[2,1]+coef(summary(SLiptcw))[3,1],sqrt(vcovCR(SLiptcw, cluster=data$id, type = "CR3")[2,2] + vcovCR(SLiptcw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(SLiptcw, cluster=data$id, type = "CR3")[2,3]), coef(summary(SLdriptcw))[2,1]+coef(summary(SLdriptcw))[3,1],sqrt(vcovCR(SLdriptcw, cluster=data$id, type = "CR3")[2,2] + vcovCR(SLdriptcw, cluster=data$id, type = "CR3")[3,3] + 2*vcovCR(SLdriptcw, cluster=data$id, type = "CR3")[2,3]), summary(SLtmlec)$effect.measures$ATE$estimate,summary(SLtmlec)$effect.measures$ATE$std.dev, GLMfcs[1],GLMfcs[2], RIfcs[1],RIfcs[2], GEEfcs[1],GEEfcs[2], GLiptwfcs[1],GLiptwfcs[2], GLdriptwfcs[1],GLdriptwfcs[2], GLtmlefcs[1],GLtmlefcs[2], SLiptwfcs[1],SLiptwfcs[2], SLdriptwfcs[1],SLdriptwfcs[2], SLtmlefcs[1],SLtmlefcs[2], GLMmvn[1],GLMmvn[2], RImvn[1],RImvn[2], GEEmvn[1],GEEmvn[2], GLiptwmvn[1],GLiptwmvn[2], GLdriptwmvn[1],GLdriptwmvn[2], GLtmlemvn[1],GLtmlemvn[2], SLiptwmvn[1],SLiptwmvn[2], SLdriptwmvn[1],SLdriptwmvn[2], SLtmlemvn[1],SLtmlemvn[2], GLMjomo[1],GLMjomo[2], RIjomo[1],RIjomo[2], GEEjomo[1],GEEjomo[2], GLiptwjomo[1],GLiptwjomo[2], GLdriptwjomo[1],GLdriptwjomo[2], GLtmlejomo[1],GLtmlejomo[2], SLiptwjomo[1],SLiptwjomo[2], SLdriptwjomo[1],SLdriptwjomo[2], SLtmlejomo[1],SLtmlejomo[2]) } col.names1 <- c("GLMjtco","GLMjtse","RIjtco","RIjtse","GEEjtco","GEEjtse", "GLMiptwjtco","GLMiptwjtse","SLiptwjtco","SLiptwjtse", "GLMdriptwjtco","GLMdriptwjtse","SLdriptwjtco","SLdriptwjtse", "GLMtmlejtco","GLMtmlejtse","SLtmlejtco","SLtmlejtse") col.names2 <- c("GLMjtco","GLMjtse","RIjtco","RIjtse","GEEjtco","GEEjtse", "GLMiptwjtco","GLMiptwjtse","SLiptwjtco","SLiptwjtse", "GLMdriptwjtco","GLMdriptwjtse","SLdriptwjtco","SLdriptwjtse", "GLMtmlejtco","GLMtmlejtse","SLtmlejtco","SLtmlejtse", "GLMIPC_GLMjtco","GLMIPC_GLMjtse","GLMIPC_RIjtco","GLMIPC_RIjtse","GLMIPC_GEEjtco","GLMIPC_GEEjtse", "GLMIPC_GLMiptwjtco","GLMIPC_GLMiptwjtse","GLMIPC_GLMdriptwjtco","GLMIPC_GLMdriptwjtse","GLMIPC_GLMtmlejtco","GLMIPC_GLMtmlejtse", "SLIPC_GLMjtco","SLIPC_GLMjtse","SLIPC_RIjtco","SLIPC_RIjtse","SLIPC_GEEjtco","SLIPC_GEEjtse", "SLIPC_SLiptwjtco","SLIPC_SLiptwjtse","SLIPC_SLdriptwjtco","SLIPC_SLdriptwjtse","SLIPC_SLtmlejtco","SLIPC_SLtmlejtse", "FCS_GLMjtco","FCS_GLMjtse","FCS_RIjtco","FCS_RIjtse","FCS_GEEjtco","FCS_GEEjtse", "FCS_GLMiptwjtco","FCS_GLMiptwjtse","FCS_SLiptwjtco","FCS_SLiptwjtse", "FCS_GLMdriptwjtco","FCS_GLMdriptwjtse","FCS_SLdriptwjtco","FCS_SLdriptwjtse", "FCS_GLMtmlejtco","FCS_GLMtmlejtse","FCS_SLtmlejtco","FCS_SLtmlejtse", "MVN_GLMjtco","MVN_GLMjtse","MVN_RIjtco","MVN_RIjtse","MVN_GEEjtco","MVN_GEEjtse", "MVN_GLMiptwjtco","MVN_GLMiptwjtse","MVN_SLiptwjtco","MVN_SLiptwjtse", "MVN_GLMdriptwjtco","MVN_GLMdriptwjtse","MVN_SLdriptwjtco","MVN_SLdriptwjtse", "MVN_GLMtmlejtco","MVN_GLMtmlejtse","MVN_SLtmlejtco","MVN_SLtmlejtse", "JOMO_GLMjtco","JOMO_GLMjtse","JOMO_RIjtco","JOMO_RIjtse","JOMO_GEEjtco","JOMO_GEEjtse", "JOMO_GLMiptwjtco","JOMO_GLMiptwjtse","JOMO_SLiptwjtco","JOMO_SLiptwjtse", "JOMO_GLMdriptwjtco","JOMO_GLMdriptwjtse","JOMO_SLdriptwjtco","JOMO_SLdriptwjtse", "JOMO_GLMtmlejtco","JOMO_GLMtmlejtse","JOMO_SLtmlejtco","JOMO_SLtmlejtse") # ############################ # ## Scenario 1 ## # ############################ # # # Complete data - no missing data # datacomp1 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data <- data.frame(data[,c(-11,-12,-13,-14,-15,-16)]) # data # }) # rescomp1 <- matrix(unlist(lapply(datacomp1, function (x) {simcomp(x)})),nrow=n,ncol=18,byrow=TRUE) # colnames(rescomp1) <- col.names1 # write_dta(data.frame(rescomp1),path=paste0(paste0(paste0(args[3],"Results/S1comp-"),start),".dta")) # rm(datacomp1) # # # 25% Missing data scenario # # MCAR - only complete case analysis run (because it is unbiased) # datamcara1 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data$y <- ifelse(data$missmcarmod==1,NA,data$y) # data <- data.frame(cbind(data[,1:9],c=data[,"missmcarmod"],y=data[,"y"])) # data # }) # resmcara1 <- matrix(unlist(lapply(datamcara1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) # colnames(resmcara1) <- col.names1 # write_dta(data.frame(resmcara1),path=paste0(paste0(paste0(args[3],"Results/S1mod-mcar-"),start),".dta")) # rm(datamcara1) # # # MAR A - Missingness only related to L # datamara1 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data$y <- ifelse(data$missmarmod1==1,NA,data$y) # data <- data.frame(cbind(data[,1:9],c=data[,"missmarmod1"],y=data[,"y"])) # data # }) # resmara1a <- matrix(unlist(lapply(datamara1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) # resmara1b <- matrix(unlist(lapply(datamara1, function (x) {simmiss(x,nimpute=nimp,scenario=1)})),nrow=n,ncol=78,byrow=TRUE) # resmara1 <- cbind(resmara1a,resmara1b) # colnames(resmara1) <- col.names2 # write_dta(data.frame(resmara1),path=paste0(paste0(paste0(args[3],"Results/S1mod-mar1-"),start),".dta")) # rm(datamara1) # # # MAR B - Missingness related to L and A # datamarb1 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data$y <- ifelse(data$missmarmod2==1,NA,data$y) # data <- data.frame(cbind(data[,1:9],c=data[,"missmarmod2"],y=data[,"y"])) # data # }) # resmarb1a <- matrix(unlist(lapply(datamarb1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) # resmarb1b <- matrix(unlist(lapply(datamarb1, function (x) {simmiss(x,nimpute=nimp,scenario=1)})),nrow=n,ncol=78,byrow=TRUE) # resmarb1 <- cbind(resmarb1a,resmarb1b) # colnames(resmarb1) <- col.names2 # write_dta(data.frame(resmarb1),path=paste0(paste0(paste0(args[3],"Results/S1mod-mar2-"),start),".dta")) # rm(datamarb1) # # # 50% Missing data scenario # # MCAR - only complete case analysis run (because it is unbiased) # datamcara2 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data$y <- ifelse(data$missmcarsev==1,NA,data$y) # data <- data.frame(cbind(data[,1:9],c=data[,"missmcarsev"],y=data[,"y"])) # data # }) # resmcara2 <- matrix(unlist(lapply(datamcara2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) # colnames(resmcara2) <- col.names1 # write_dta(data.frame(resmcara2),path=paste0(paste0(paste0(args[3],"Results/S1sev-mcar-"),start),".dta")) # rm(datamcara2) # # # MAR A - Missingness only related to L # datamara2 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data$y <- ifelse(data$missmarsev1==1,NA,data$y) # data <- data.frame(cbind(data[,1:9],c=data[,"missmarsev1"],y=data[,"y"])) # data # }) # resmara2a <- matrix(unlist(lapply(datamara2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) # resmara2b <- matrix(unlist(lapply(datamara2, function (x) {simmiss(x,nimpute=nimp,scenario=1)})),nrow=n,ncol=78,byrow=TRUE) # resmara2 <- cbind(resmara2a,resmara2b) # colnames(resmara2) <- col.names2 # write_dta(data.frame(resmara2),path=paste0(paste0(paste0(args[3],"Results/S1sev-mar1-"),start),".dta")) # rm(datamara2) # # # MAR B - Missingness related to L and A # datamarb2 <- lapply(paste0(filepath1,list.files(path=filepath1))[start:stop],function (x) { # data <- data.frame(read_dta(x)) # data$id <- factor(data$id) # data$y <- ifelse(data$missmarsev2==1,NA,data$y) # data <- data.frame(cbind(data[,1:9],c=data[,"missmarsev2"],y=data[,"y"])) # data # }) # resmarb2a <- matrix(unlist(lapply(datamarb2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) # resmarb2b <- matrix(unlist(lapply(datamarb2, function (x) {simmiss(x,nimpute=nimp,scenario=1)})),nrow=n,ncol=78,byrow=TRUE) # resmarb2 <- cbind(resmarb2a,resmarb2b) # colnames(resmarb2) <- col.names2 # write_dta(data.frame(resmarb2),path=paste0(paste0(paste0(args[3],"Results/S1sev-mar2-"),start),".dta")) # rm(datamarb2) ############################ ## Scenario 2 ## ############################ # Complete data - no missing data datacomp2 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data <- data.frame(data[,c(-13,-14,-15,-16,-17,-18)]) data }) rescomp2 <- matrix(unlist(lapply(datacomp2, function (x) {simcomp(x)})),nrow=n,ncol=18,byrow=TRUE) colnames(rescomp2) <- col.names1 write_dta(data.frame(rescomp2),path=paste0(paste0(paste0(args[3],"Results/S2comp-"),start),".dta")) rm(datacomp2) # 25% Missing data scenario # MCAR - only complete case analysis run (because it is unbiased) datamcarb1 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmcarmod==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmcarmod"],y=data[,"y"])) data }) resmcarb1 <- matrix(unlist(lapply(datamcarb1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) colnames(resmcarb1) <- col.names1 write_dta(data.frame(resmcarb1),path=paste0(paste0(paste0(args[3],"Results/S2mod-mcar-"),start),".dta")) rm(datamcarb1) # MAR C - Missingness only related to W datamarc1 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarmod1==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarmod1"],y=data[,"y"])) data }) resmarc1a <- matrix(unlist(lapply(datamarc1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmarc1b <- matrix(unlist(lapply(datamarc1, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmarc1 <- cbind(resmarc1a,resmarc1b) colnames(resmarc1) <- col.names2 write_dta(data.frame(resmarc1),path=paste0(paste0(paste0(args[3],"Results/S2mod-mar1-"),start),".dta")) rm(datamarc1) # MAR D - Missingness related to W and A datamard1 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarmod2==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarmod2"],y=data[,"y"])) data }) resmard1a <- matrix(unlist(lapply(datamard1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmard1b <- matrix(unlist(lapply(datamard1, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmard1 <- cbind(resmard1a,resmard1b) colnames(resmard1) <- col.names2 write_dta(data.frame(resmard1),path=paste0(paste0(paste0(args[3],"Results/S2mod-mar2-"),start),".dta")) rm(datamard1) # 50% Missing data scenario # MCAR - only complete case analysis run (because it is unbiased) datamcarb2 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmcarsev==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmcarsev"],y=data[,"y"])) data }) resmcarb2 <- matrix(unlist(lapply(datamcarb2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) colnames(resmcarb2) <- col.names1 write_dta(data.frame(resmcarb2),path=paste0(paste0(paste0(args[3],"Results/S2sev-mcar-"),start),".dta")) rm(datamcarb2) # MAR C - Missingness only related to W datamarc2 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarsev1==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarsev1"],y=data[,"y"])) data }) resmarc2a <- matrix(unlist(lapply(datamarc2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmarc2b <- matrix(unlist(lapply(datamarc2, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmarc2 <- cbind(resmarc2a,resmarc2b) colnames(resmarc2) <- col.names2 write_dta(data.frame(resmarc2),path=paste0(paste0(paste0(args[3],"Results/S2sev-mar1-"),start),".dta")) rm(datamarc2) # MAR D - Missingness related to W and A datamard2 <- lapply(paste0(filepath2,list.files(path=filepath2))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarsev2==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarsev2"],y=data[,"y"])) data }) resmard2a <- matrix(unlist(lapply(datamard2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmard2b <- matrix(unlist(lapply(datamard2, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmard2 <- cbind(resmard2a,resmard2b) colnames(resmard2) <- col.names2 write_dta(data.frame(resmard2),path=paste0(paste0(paste0(args[3],"Results/S2sev-mar2-"),start),".dta")) rm(datamard2) ############################ ## Scenario 3 ## ############################ # Complete data - no missing data datacomp3 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data <- data.frame(data[,c(-13,-14,-15,-16,-17,-18)]) data }) rescomp3 <- matrix(unlist(lapply(datacomp3, function (x) {simcomp(x)})),nrow=n,ncol=18,byrow=TRUE) colnames(rescomp3) <- col.names1 write_dta(data.frame(rescomp3),path=paste0(paste0(paste0(args[3],"Results/S3comp-"),start),".dta")) rm(datacomp3) # 25% Missing data scenario # MCAR - only complete case analysis run (because it is unbiased) datamcarc1 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmcarmod==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmcarmod"],y=data[,"y"])) data }) resmcarc1 <- matrix(unlist(lapply(datamcarc1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) colnames(resmcarc1) <- col.names1 write_dta(data.frame(resmcarc1),path=paste0(paste0(paste0(args[3],"Results/S3mod-mcar-"),start),".dta")) rm(datamcarc1) # MAR E - Missingness only related to W datamare1 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarmod1==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarmod1"],y=data[,"y"])) data }) resmare1a <- matrix(unlist(lapply(datamare1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmare1b <- matrix(unlist(lapply(datamare1, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmare1 <- cbind(resmare1a,resmare1b) colnames(resmare1) <- col.names2 write_dta(data.frame(resmare1),path=paste0(paste0(paste0(args[3],"Results/S3mod-mar1-"),start),".dta")) rm(datamare1) # MAR F - Missingness related to W and A datamarf1 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarmod2==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarmod2"],y=data[,"y"])) data }) resmarf1a <- matrix(unlist(lapply(datamarf1, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmarf1b <- matrix(unlist(lapply(datamarf1, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmarf1 <- cbind(resmarf1a,resmarf1b) colnames(resmarf1) <- col.names2 write_dta(data.frame(resmarf1),path=paste0(paste0(paste0(args[3],"Results/S3mod-mar2-"),start),".dta")) rm(datamarf1) # 50% Missing data scenario # MCAR - only complete case analysis run (because it is unbiased) datamcarc2 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmcarsev==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmcarsev"],y=data[,"y"])) data }) resmcarc2 <- matrix(unlist(lapply(datamcarc2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) colnames(resmcarc2) <- col.names1 write_dta(data.frame(resmcarc2),path=paste0(paste0(paste0(args[3],"Results/S3sev-mcar-"),start),".dta")) rm(datamcarc2) # MAR E - Missingness only related to W datamare2 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarsev1==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarsev1"],y=data[,"y"])) data }) resmare2a <- matrix(unlist(lapply(datamare2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmare2b <- matrix(unlist(lapply(datamare2, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmare2 <- cbind(resmare2a,resmare2b) colnames(resmare2) <- col.names2 write_dta(data.frame(resmare2),path=paste0(paste0(paste0(args[3],"Results/S3sev-mar1-"),start),".dta")) rm(datamare2) # MAR F - Missingness related to W and A datamarf2 <- lapply(paste0(filepath3,list.files(path=filepath3))[start:stop],function (x) { data <- data.frame(read_dta(x)) data$id <- factor(data$id) data$y <- ifelse(data$missmarsev2==1,NA,data$y) data <- data.frame(cbind(data[,1:11],c=data[,"missmarsev2"],y=data[,"y"])) data }) resmarf2a <- matrix(unlist(lapply(datamarf2, function (x) {simcomp(x[,!(names(x) %in% "c")])})),nrow=n,ncol=18,byrow=TRUE) resmarf2b <- matrix(unlist(lapply(datamarf2, function (x) {simmiss(x,nimpute=nimp,scenario=2)})),nrow=n,ncol=78,byrow=TRUE) resmarf2 <- cbind(resmarf2a,resmarf2b) colnames(resmarf2) <- col.names2 write_dta(data.frame(resmarf2),path=paste0(paste0(paste0(args[3],"Results/S3sev-mar2-"),start),".dta")) rm(datamarf2)

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calimaborges-drafts/the-archive

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source("readfiles.R") source("complete.R") corr <- function(directory, threshold = 0) { ## 'directory' is a character vector of length 1 indicating ## the location of the CSV files working_data <- complete(directory) working_data <- subset(working_data, nobs > threshold) if (nrow(working_data) == 0) return(numeric()) files <- readfiles(directory, working_data$id) tables <- lapply(files, read.csv) corr <- c() for (table in tables) { table <- subset(table, !is.na(table["sulfate"])) table <- subset(table, !is.na(table["nitrate"])) corr <- c(corr, cor(table$nitrate, table$sulfate)) } corr #print(cor(tables[1,]$sulfate, tables[1,]$nitrate)) ## 'threshold' is a numeric vector of length 1 indicating the ## number of completely observed observations (on all ## variables) required to compute the correlation between ## nitrate and sulfate; the default is 0 ## Return a numeric vector of correlations ## NOTE: Do not round the result! }

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/tests/testthat/test-msn.R

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knausb/poppr

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test-msn.R

options(warn = -1) ucl <- function(x){ unclass(x$graph)[-10] } set.seed(9005) gend <- new("genind" , tab = structure(c(1L, 1L, 0L, 0L, 1L, 1L, 2L, 2L, 0L, 1L, 1L, 0L, 2L, 1L, 1L, 2L), .Dim = c(4L, 4L), .Dimnames = list(c("1", "2", "3", "4"), c("loc-1.1", "loc-1.2", "loc-2.1", "loc-2.2"))) , loc.names = structure(c("loc-1", "loc-2"), .Names = c("loc-1", "loc-2")) , loc.fac = structure(c(1L, 1L, 2L, 2L), .Label = c("loc-1", "loc-2"), class = "factor") , loc.nall = structure(c(2L, 2L), .Names = c("loc-1", "loc-2")) , all.names = structure(list(`loc-1` = structure(c("3", "4"), .Names = c("1", "2" )), `loc-2` = structure(c("3", "4"), .Names = c("1", "2"))), .Names = c("loc-1", "loc-2")) , call = NULL , ind.names = structure(c("", "", "", ""), .Names = c("1", "2", "3", "4")) , pop = structure(1:4, .Label = c("1", "2", "3", "4"), class = "factor") , pop.names = structure(c("1", "2", "3", "4")) , ploidy = 2L , type = "codom" , other = NULL ) gend_gc <- as.genclone(gend) gend_bruvo <- bruvo.dist(gend, replen = c(1, 1)) set.seed(9005) gend_single <- gend pop(gend_single) <- rep(1, 4) no_ties <- structure(list(graph = structure(list(4, FALSE, c(3, 2, 3), c(0, 1, 2), c(1, 0, 2), c(0, 1, 2), c(0, 0, 0, 1, 3), c(0, 1, 2, 3, 3), list(c(1, 0, 1), structure(list(), .Names = character(0)), structure(list(name = c("1", "2", "3", "4"), size = c(1L, 1L, 1L, 1L), shape = c("pie", "pie", "pie", "pie"), pie = list( structure(1L, .Names = "1"), structure(1L, .Names = "2"), structure(1L, .Names = "3"), structure(1L, .Names = "4")), pie.color = list(structure("#4C00FFFF", .Names = "1"), structure("#00E5FFFF", .Names = "2"), structure("#00FF4DFF", .Names = "3"), structure("#FFFF00FF", .Names = "4")), label = c("MLG.3", "MLG.4", "MLG.2", "MLG.1")), .Names = c("name", "size", "shape", "pie", "pie.color", "label")), structure(list(weight = c(0.125, 0.125, 0.125), color = c("#434343", "#434343", "#434343"), width = c(8, 8, 8)), .Names = c("weight", "color", "width" )))), class = "igraph"), populations = structure(c("1", "2", "3", "4")), colors = structure(c("#4C00FFFF", "#00E5FFFF", "#00FF4DFF", "#FFFF00FF"), .Names = c("1","2","3","4"))), .Names = c("graph", "populations", "colors")) no_ties_single <- structure(list(graph = structure(list(4, FALSE, c(3, 2, 3), c(0, 1, 2), c(1, 0, 2), c(0, 1, 2), c(0, 0, 0, 1, 3), c(0, 1, 2, 3, 3), list(c(1, 0, 1), structure(list(), .Names = character(0)), structure(list(name = c("1", "2", "3", "4"), size = c(1L, 1L, 1L, 1L), color = c("#4C00FFFF", "#4C00FFFF", "#4C00FFFF", "#4C00FFFF"), label = c("MLG.3", "MLG.4", "MLG.2", "MLG.1" )), .Names = c("name", "size", "color", "label")), structure(list( weight = c(0.125, 0.125, 0.125), color = c("#434343", "#434343", "#434343"), width = c(8, 8, 8)), .Names = c("weight", "color", "width")))), class = "igraph"), populations = "1", colors = "#4C00FFFF"), .Names = c("graph", "populations", "colors")) ties <- structure(list(graph = structure(list(4, FALSE, c(3, 2, 3, 1), c(0, 1, 2, 0), c(3, 1, 0, 2), c(3, 0, 1, 2), c(0, 0, 1, 2, 4), c(0, 2, 3, 4, 4), list(c(1, 0, 1), structure(list(), .Names = character(0)), structure(list(name = c("1", "2", "3", "4"), size = c(1L, 1L, 1L, 1L), shape = c("pie", "pie", "pie", "pie"), pie = list( structure(1L, .Names = "1"), structure(1L, .Names = "2"), structure(1L, .Names = "3"), structure(1L, .Names = "4")), pie.color = list(structure("#4C00FFFF", .Names = "1"), structure("#00E5FFFF", .Names = "2"), structure("#00FF4DFF", .Names = "3"), structure("#FFFF00FF", .Names = "4")), label = c("MLG.3", "MLG.4", "MLG.2", "MLG.1")), .Names = c("name", "size", "shape", "pie", "pie.color", "label")), structure(list(weight = c(0.125, 0.125, 0.125, 0.125), color = c("#434343", "#434343", "#434343", "#434343"), width = c(8, 8, 8, 8)), .Names = c("weight", "color", "width")))), class = "igraph"), populations = structure(c("1", "2", "3", "4")), colors = structure(c("#4C00FFFF", "#00E5FFFF", "#00FF4DFF", "#FFFF00FF"), .Names = c("1","2","3","4"))), .Names = c("graph", "populations", "colors")) ties_single <- structure(list(graph = structure(list(4, FALSE, c(3, 2, 3, 1), c(0, 1, 2, 0), c(3, 1, 0, 2), c(3, 0, 1, 2), c(0, 0, 1, 2, 4), c(0, 2, 3, 4, 4), list(c(1, 0, 1), structure(list(), .Names = character(0)), structure(list(name = c("1", "2", "3", "4"), size = c(1L, 1L, 1L, 1L), color = c("#4C00FFFF", "#4C00FFFF", "#4C00FFFF", "#4C00FFFF"), label = c("MLG.3", "MLG.4", "MLG.2", "MLG.1" )), .Names = c("name", "size", "color", "label")), structure(list( weight = c(0.125, 0.125, 0.125, 0.125), color = c("#434343", "#434343", "#434343", "#434343"), width = c(8, 8, 8, 8)), .Names = c("weight", "color", "width")))), class = "igraph"), populations = "1", colors = "#4C00FFFF"), .Names = c("graph", "populations", "colors")) if (packageVersion("igraph") >= package_version("1.0.0")){ no_ties$graph <- igraph::upgrade_graph(no_ties$graph) no_ties_single$graph <- igraph::upgrade_graph(no_ties_single$graph) ties$graph <- igraph::upgrade_graph(ties$graph) ties_single$graph <- igraph::upgrade_graph(ties_single$graph) } options(warn = 0) context("Tied MSN edge tests") test_that("bruvo.msn can properly account for tied edges", { # Test Bruvo.msn set.seed(9005) expect_equal(ucl(bruvo.msn(gend, replen=c(1,1))), ucl(no_ties)) set.seed(9005) expect_equal(ucl(bruvo.msn(gend, replen=c(1,1), include.ties = TRUE)), ucl(ties)) }) test_that("poppr.msn can properly account for tied edges", { # Test poppr.msn set.seed(9005) expect_equal(ucl(poppr.msn(gend, distmat=bruvo.dist(gend,replen=c(1,1)))), ucl(no_ties)) set.seed(9005) expect_equal(ucl(poppr.msn(gend, distmat=bruvo.dist(gend,replen=c(1,1)), include.ties = TRUE)), ucl(ties)) }) test_that("bruvo.msn can work with single populations", { # Test both for single populations sets set.seed(9005) expect_equal(ucl(bruvo.msn(gend_single, replen=c(1,1))), ucl(no_ties_single)) set.seed(9005) expect_equal(ucl(bruvo.msn(gend_single, replen=c(1,1), include.ties = TRUE)), ucl(ties_single)) }) test_that("poppr.msn can work with single populations", { gs.bruvo <- bruvo.dist(gend_single,replen=c(1,1)) set.seed(9005) expect_equal(ucl(poppr.msn(gend_single, distmat = gs.bruvo)), ucl(no_ties_single)) set.seed(9005) expect_equal(ucl(poppr.msn(gend_single, distmat = gs.bruvo, include.ties = TRUE)), ucl(ties_single)) }) context("MSN and collapsed MLG tests") gmsnt <- bruvo.msn(gend, replen = c(1, 1), threshold = 0.15) test_that("Minimum spanning networks also collapse MLGs", { skip_on_cran() gend <- as.genclone(gend) gend_single <- as.genclone(gend_single) # Adding the filter for testing mlg.filter(gend, dist = bruvo.dist, replen = c(1, 1)) <- 0.15 mll(gend) <- "original" expect_equal(igraph::vcount(gmsnt$graph), 2) pgmsnt <- poppr.msn(gend, distmat = gend_bruvo, threshold = 0.15) mll(gend) <- "contracted" gmsnot <- bruvo.msn(gend, replen = c(1, 1)) # no threshold supplied gmsnone <- bruvo.msn(gend, replen = c(1, 1), threshold = 0.3) expect_equal(igraph::vcount(gmsnone$graph), 1) gmsnall <- bruvo.msn(gend, replen = c(1, 1), threshold = 0) expect_equal(igraph::vcount(gmsnall$graph), 4) expect_identical(igraph::V(gmsnt$graph)$pie, igraph::V(pgmsnt$graph)$pie) expect_identical(igraph::V(gmsnot$graph)$pie, igraph::V(pgmsnt$graph)$pie) expect_identical(igraph::V(gmsnt$graph)$name, igraph::V(pgmsnt$graph)$name) expect_identical(igraph::V(gmsnot$graph)$name, igraph::V(pgmsnt$graph)$name) expect_identical(igraph::E(gmsnt$graph)$weight, igraph::E(pgmsnt$graph)$weight) expect_identical(igraph::E(gmsnot$graph)$weight, igraph::E(pgmsnt$graph)$weight) mll(gend) <- "original" gmsn <- bruvo.msn(gend, replen = c(1, 1), showplot = FALSE) expect_equal(igraph::vcount(gmsn$graph), 4) pgmsn <- poppr.msn(gend, distmat = gend_bruvo, showplot = FALSE) expect_identical(igraph::V(gmsn$graph)$pie, igraph::V(pgmsn$graph)$pie) expect_identical(igraph::V(gmsn$graph)$pie, igraph::V(gmsnall$graph)$pie) expect_identical(igraph::V(gmsn$graph)$name, igraph::V(pgmsn$graph)$name) expect_identical(igraph::V(gmsn$graph)$name, igraph::V(gmsnall$graph)$name) expect_identical(igraph::E(gmsn$graph)$weight, igraph::E(pgmsn$graph)$weight) expect_identical(igraph::E(gmsn$graph)$weight, igraph::E(gmsnall$graph)$weight) }) test_that("Minimum spanning networks can collapse MLGs with single populations", { skip_on_cran() sgmsnt <- bruvo.msn(gend_single, replen = c(1, 1), threshold = 0.15) psgmsnt <- poppr.msn(gend_single, distmat = gend_bruvo, threshold = 0.15) expect_identical(igraph::V(sgmsnt$graph)$pie, igraph::V(psgmsnt$graph)$pie) expect_identical(igraph::V(sgmsnt$graph)$name, igraph::V(psgmsnt$graph)$name) expect_identical(igraph::E(sgmsnt$graph)$weight, igraph::E(psgmsnt$graph)$weight) sgmsn <- bruvo.msn(gend_single, replen = c(1, 1), showplot = FALSE) psgmsn <- poppr.msn(gend_single, distmat = gend_bruvo, showplot = FALSE) expect_identical(igraph::V(sgmsn$graph)$pie, igraph::V(psgmsn$graph)$pie) expect_identical(igraph::V(sgmsn$graph)$name, igraph::V(psgmsn$graph)$name) expect_identical(igraph::E(sgmsn$graph)$weight, igraph::E(psgmsn$graph)$weight) expect_equal(igraph::vcount(sgmsnt$graph), 2) expect_equal(igraph::vcount(sgmsn$graph), 4) expect_output(plot_poppr_msn(gend, gmsnt, palette = "cm.colors"), NA) expect_output(plot_poppr_msn(gend_single, sgmsnt, palette = "cm.colors"), NA) }) test_that("Filtered minimum spanning networks retain original names", { skip_on_cran() # setup ---------------------------------------------------- grid_example <- matrix(c(1, 4, 1, 1, 5, 1, 9, 1, 9, 4), ncol = 2, byrow = TRUE) rownames(grid_example) <- LETTERS[1:5] colnames(grid_example) <- c("x", "y") grid_new <- rbind(grid_example, new = c(5, NA), mut = c(5, 2) ) x <- as.genclone(df2genind(grid_new, ploidy = 1)) indNames(x) ## [1] "A" "B" "C" "D" "E" "new" "mut" raw_dist <- function(x){ dist(genind2df(x, usepop = FALSE)) } (xdis <- raw_dist(x)) # normal --------------------------------------------------- set.seed(9001) g1 <- poppr.msn(x, xdis, include.ties = TRUE, showplot = FALSE, vertex.label.color = "firebrick", vertex.label.font = 2) all_names <- igraph::V(g1$graph)$name ## [1] "A" "B" "C" "D" "E" "new" "mut" # filtered --------------------------------------------------- set.seed(9001) g1.1 <- poppr.msn(x, xdis, threshold = 1, include.ties = TRUE, showplot = FALSE, vertex.label.color = "firebrick", vertex.label.font = 2) cc_names <- igraph::V(g1.1$graph)$name ## [1] "A" "B" "C" "D" "E" "new" expect_identical(cc_names, head(all_names, -1)) }) context("minimum spanning network subset populations") data("partial_clone") pc <- as.genclone(partial_clone) test_that("Minimum spanning networks can subset populations", { bpc <- bruvo.dist(pc, replen = rep(1, 10)) bmsn <- bruvo.msn(pc, replen = rep(1, 10), showplot = FALSE) pmsn <- poppr.msn(pc, bpc, showplot = FALSE) expect_identical(ucl(bmsn), ucl(pmsn)) bmsn12 <- bruvo.msn(pc, replen = rep(1, 10), sublist = 1:2, showplot = FALSE) pmsn12 <- poppr.msn(pc, bpc, sublist = 1:2, showplot = FALSE) expect_identical(ucl(bmsn12), ucl(pmsn12)) bmsn1 <- bruvo.msn(pc, replen = rep(1, 10), sublist = 1, showplot = FALSE) pmsn1 <- poppr.msn(pc, bpc, sublist = 1, showplot = FALSE) expect_identical(ucl(bmsn1), ucl(pmsn1)) }) context("custom MLLs and minimum spanning networks") mll.custom(pc) <- LETTERS[mll(pc)] mll.levels(pc)[mll.levels(pc) == "Q"] <- "M" mll(pc) <- "custom" test_that("msn works with custom MLLs", { skip_on_cran() expect_error(pcmsn <- bruvo.msn(pc, replen = rep(1, 10)), NA) expect_equivalent(sort(unique(igraph::V(pcmsn$graph)$label)), sort(mll.levels(pc))) expect_error(plot_poppr_msn(pc, pcmsn), NA) expect_error(plot_poppr_msn(pc, pcmsn, mlg = TRUE), NA) }) context("Minimum spanning network aesthetics") test_that("vectors can be used to color graphs", { skip_on_cran() data(Aeut) A.dist <- diss.dist(Aeut) # Graph it. A.msn <- poppr.msn(Aeut, A.dist, gadj=15, vertex.label=NA, showplot = FALSE) unpal <- c("black", "orange") fpal <- function(x) unpal npal <- setNames(unpal, c("Athena", "Mt. Vernon")) xpal <- c(npal, JoMo = "awesome") # Using palette without names uname_pal <- plot_poppr_msn(Aeut, A.msn, palette = unpal)$colors # Using palette with function fun_pal <- plot_poppr_msn(Aeut, A.msn, palette = fpal)$colors # Using palette with names name_pal <- plot_poppr_msn(Aeut, A.msn, palette = npal[2:1])$colors # Using palette with extra names xname_pal <- plot_poppr_msn(Aeut, A.msn, palette = xpal)$colors expect_identical(uname_pal, npal) expect_identical(fun_pal, npal) expect_identical(name_pal, npal) expect_identical(xname_pal, npal) })

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Fedzzy/Diameter-Class-Growth-and-Yield---Thesis

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Model 1 Analysis_SzPt20_Final.R

#======================Regression Analysis of Different Varibles - Model 1 - SzPt20======================= #Loading in Sourced Functions source(file="Compiled Analysis Functions.R") #Load in Plot Level Parameter Data.Rdata load(file="Plot Level Parameter Data.RData") #========================================Nonlinear Regression for AMD - Model 1 AMD_Mod1<-nls(AMDat~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))+b3*Age), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10, b3=.006)) summary(AMD_Mod1) #Prediction SzPt20ParamDat$AMD_Mod1<-predict(AMD_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$AMDat, SzPt20ParamDat$AMD_Mod1, 4) #========================================Nonlinear Regression for QMD - Model 1 QMD_Mod1<-nls(QMDat~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))+b3*Age), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10, b3=.006)) summary(QMD_Mod1) #Prediction SzPt20ParamDat$QMD_Mod1<-predict(QMD_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$QMDat, SzPt20ParamDat$QMD_Mod1, 4) #========================================Nonlinear Regression for D0 - Model 1 D0_Mod1<-nls(D0~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))+b3*Age), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10, b3=.006)) #Obtaining Summary of Results summary(D0_Mod1) #RMSE was 1.899 #Prediction SzPt20ParamDat$D0_Mod1<-predict(D0_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$D0, SzPt20ParamDat$D0_Mod1, 4) #<><><><><><><><><><><><><><><><><><<><><><><><><><<Running alternative models for dbhMin<><><><><><><> #Running alternative model 1 for dbhMin prediciton D0_E1<-nls(D0~b0*AMDat^b1*exp(b2*(1/Age)), data=(SzPt20ParamDat), start=list(b0=.035, b1=1.826, b2=.774)) #Obtaining summary of results summary(D0_E1) #RMSE found to be 1.723 #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< #Running alternative model 2 for dbhMin prediction D0_E2<-nls(D0~b0*QMDat^b1*exp(b2*(1/Age)), data=(SzPt20ParamDat), start=list(b0=.035, b1=1.826, b2=.416)) #Obtaining summary of results summary(D0_E2) #RMSE was found to be 1.849 #Alternative 1(AMD) showed to have the lowest RMSE of 1.723 #========================================Nonlinear Regression for D25 - Model 1 D25_Mod1<-nls(D25~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))+b3*Age), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10, b3=.006)) summary(D25_Mod1) #Prediction SzPt20ParamDat$D25_Mod1<-predict(D25_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$D25, SzPt20ParamDat$D25_Mod1, 4) #========================================Nonlinear Regression for D50 - Model 1 D50_Mod1<-nls(D50~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))+b3*Age), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10, b3=.006)) summary(D50_Mod1) #Prediction SzPt20ParamDat$D50_Mod1<-predict(D50_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$D50, SzPt20ParamDat$D50_Mod1, 4) #========================================Nonlinear Regression for D93 - Model 1 D93_Mod1<-nls(D93~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))+b3*Age), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10, b3=.006)) summary(D93_Mod1) #Prediction SzPt20ParamDat$D93_Mod1<-predict(D93_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$D93, SzPt20ParamDat$D93_Mod1, 4) #========================================Nonlinear Regression for D95 - Model 1 D95_Mod1<-nls(D95~b0*(TPH^b1)*exp((b2/sqrt(DomHtM))), data=(SzPt20ParamDat), start=list(b0=105, b1=-0.2, b2=-10)) summary(D95_Mod1) #Prediction SzPt20ParamDat$D95_Mod1<-predict(D95_Mod1) #Calculating RMSE RMSE(SzPt20ParamDat, SzPt20ParamDat$D95, SzPt20ParamDat$D95_Mod1, 3) #$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ #======================================================================================== #Separating Plot Data for Further Analysis #Get column subsets PlotSz20<-SzPt20ParamDat[,c("MeasmtObs","Age","DomHtM","TPH","RS", "AMD_Mod1", "QMD_Mod1","D0_Mod1","D25_Mod1","D50_Mod1","D93_Mod1", "D95_Mod1")] save(PlotSz20,file="K:/csabatia/GraduateStudentWork/JoshBankston/Data Analysis/Distribution Recovery Analyses/Model 1_Cao2004/PlotLevelAnalysis20.RData")

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# <-- encoding UTF-8 --> # Portal for the code of: Socioeconomic Status Inequality and Morbidity Rate Differences in China: Based on NHSS and CHARLS Data # Author: Y. Jiang, H. Zheng, T. Zhao # ------------------------------------------- ## DOC STRING # # The script is the portal of all programs of our submtted paper; # you may run this file, or follow the documentation, README, and comments to specific source files. # Please refer to README.md for more information. # and, all scripts & file I/O are encoded in UTF-8 (because Chinese charaters, in data and/or scripts, may be included & used) # # Tianhao Zhao (GitHub: Clpr) # Dec 2018 # ------------------------------------------- ## DEPENDENCY # # 1. readxl: IO of xlsx files # 2. plm: panel data models # 3. nlme: GLS estimation of linear models # 4. car: VIFs, qqplots # 5. ggplot2, plyr, maptools, mapproj, Cairo: map drawing # 6. sqldf: api of firendly sql enqury # 7. nortest: normality tests # 8. purrr: std lib, to do function operations # 9. openxlsx: easy xlsx I/O, no dependency on rJava # 10. psych: easier APIs for PCA # ------------------------------------------- rm(list=ls()) # clear setwd("C:/Users/Lenovo/OneDrive/GitHubProj/HealthInequality2018Dec") # working directory # ------------------------------------------- # Section 1: NHSS data processing & variable selection cat("\nScript 1: NHSS data processing & variable selection") # MISSION: # 1. NHSS data I/O # 2. NHSS data processing, missing values # 3. collinearity between income & edu: resid(income ~ edu) --> income # 4. NHSS (control) variable selection I: through single-variable regressions # 5. NHSS (control) variable selection II: PCA # 6. VIFs of final specifications source("./scripts/proc_1_NHSS.r") # LEGACY IN WORKSPACE: # 1. df_NHSSraw: raw NHSS dataset, consisting of all potential vars & useless vars; as backup # 2. df_NHSS: processed NHSS dataset # 3. li_Dat_NHSS: datasets for every specification # 3. li_DatPlm_NHSS: panel-data for every specification # 4. li_PCAres_NHSS: PCA results # 5. li_PCAk_NHSS: numebr of components used in every specification # 5. li_VIF_NHSS: VIF results of final specifications # 6. li_Eq_NHSS: a list consisting of formula objects of final specifications # 7. li_Xnames_NHSS: a list of namelists of independents of every specification # 7. envNHSS: environment variables of NHSS dataset # NOTE: pls go to ./output/proc_1_NHSS/ for output results (if applicable) # --------------------------------------------- # --------------------------------------------- # Section 2: NHSS data, descriptive statistics (basic and advanced) cat("\nScript 2: NHSS data descriptive statistics") # DEPENDENCY: using legacy of proc_1_NHSS.r # MISSION: # 1. NHSS data, general descriptive stats (mean, sd etc) # 2. county-level (individual) Lorenz Curve & Gini coef of health outcomes # 3. county-level (individual) Theil-I, Theil-II # 4. county-level (individual) C.V., coefficient of variance # 5. county-level (individual) Variance # 6. NHSS data, the existence of the inequalities of health outcomes among areas in China (colored map) # 7. NHSS data, the difference of (income & edu) among areas in china (colored map) source("./scripts/proc_2_NHSS.r") # LEGACY IN WORKSPACE: # 1. li_Descript_NHSS: tables of the descriptive statistics of NHSS data (only income & edu, we do not do this on normalized+centered principle components!) # 2. df_InequalIdx_NHSS: a table of different kinds of inequality indices of NHSS data # 3. MapShapeCH: a dataset of Chinese GIS data; will be used later # 4. func_DescrStat: a function to do descriptive statistics # 5. func_MapProv: draws colored map based on Chinese GIS data; province level # 6. func_SaveMap2PDF: easy output method of the figures created by func_MapProv() # 7. LorenzCurve: easy function to compute Lorenz curve & Gini coef # 8. Theil: easy function to compute Theil-I/II index # 9. TrapeInt: easy function to compute trapezium integral # NOTE: pls go to ./output/proc_2_NHSS/ for output results (applicable) # --------------------------------------------- # --------------------------------------------- # Section 3: NHSS data, econometric analysis (pool, fix, random, Hausman) cat("\nScript 3: NHSS data, econometric analysis (pool, fix, random, Hausman)") # DEPENDENCY: using legacy of proc_1_NHSS.r # # IMPORTANT NOTE: please refer to PanelAnalysisLogic.md under the ./docs directory to help understand how we designed this section !!! :) # # MISSION: # 1. one-way fixed individual effect model & random individual effect model (FGLS estimators) # 2. Hausman test & robust Hausman test (Wooldridge, 2010) # 3. Residual QQplots & normality tests, one-way fixed individual effect model (only) # 4. ROBUST: two-ways fixed effect model & Haussman test (to see if one-way & two-ways are consistent) # 5. ROBUST: pooling, OLS # 6. ROBUST: pooling, FGLS # # 5. ROBUST: add possible independent: source("./scripts/proc_3_NHSS.r") # LEGACY IN WORKSPACE: # NOTE: pls go to ./output/proc_3_NHSS/ for output results (applicable) # ------------------------------------------- # ------------------------------------------- # Section 4: CHARLS data processing & variable selection cat("\nScript 4: CHARLS data processing & variable selection") # MISSION: # 1. CHARLS data I/O # 2. CHARLS data processing, missing values # 3. collinearity between income & edu: resid(income ~ edu) --> income # 4. CHARLS (control) variable selection I: through single-variable regressions # 5. CHARLS (control) variable selection II: AIC stepwise to FURTHER select control variables into final specifications # 6. VIFs of final specifications source("./scripts/proc_1_CHARLS.r") # LEGACY IN WORKSPACE: # 1. df_CHARLS_backup: raw CHARLS dataset, consisting of all potential vars & useless vars; as backup # 2. df_CHARLS: processed CHARLS dataset # 3. dfp_CHARLS: a panel-data-type dataframe (converted from df_NHSS, used in package plm) # 4. df_FinalSpecif_CHARLS: a dataframe marking which variables in the final specifications # 5. df_VIF_CHARLS: VIF results of final specifications # 6. li_Eq_CHARLS: a list consisting of formula objects of final specifications # 7. envCHARLS: environment variables of CHARLS dataset # NOTE: pls go to ./output/proc_1_CHARLS/ for output results (if applicable) # --------------------------------------------- # --------------------------------------------- # Section 5: CHARLS data, descriptive statistics (basic and advanced) cat("\nScript 5: CHARLS data, descriptive statistics (basic and advanced)") # DEPENDENCY: using legacy of proc_1_NHSS.r # MISSION: # 1. CHARLS data, general descriptive stats (mean, sd etc) # 2. county-level (individual) Lorenz Curve & Gini coef of health outcomes # 3. county-level (individual) Theil-I, Theil-II # 4. county-level (individual) C.V., coefficient of variance # 5. county-level (individual) Variance # 6. CHARLS data, the existence of the inequalities of health outcomes among areas in China (colored map) # 7. CHARLS data, the difference of (income & edu) among areas in china (colored map) source("./scripts/proc_2_CHARLS.r") # LEGACY IN WORKSPACE: # 1. df_Descript_CHARLS: a table of the descriptive statistics of NHSS data (final specification) # 2. df_InequalIdx_CHARLS: a table of different kinds of inequality indices of NHSS data # 3. MapShapeCH: a dataset of Chinese GIS data; will be used later # 4. func_DescrStat: a function to do descriptive statistics # 5. func_MapProv: draws colored map based on Chinese GIS data; province level # 6. func_SaveMap2PDF: easy output method of the figures created by func_MapProv() # 7. LorenzCurve: easy function to compute Lorenz curve & Gini coef # 8. Theil: easy function to compute Theil-I/II index # 9. TrapeInt: easy function to compute trapezium integral # NOTE: pls go to ./output/proc_2_CHARLS/ for output results (applicable) # --------------------------------------------- # --------------------------------------------- # Section 6: CHARLS data, econometric analysis (pool, fix, random, Hausman) cat("\nScript 3: CHARLS data, econometric analysis (pool, fix, random, Hausman)") # DEPENDENCY: using legacy of proc_1_CHARLS.r # # IMPORTANT NOTE: please refer to PanelAnalysisLogic.md under the ./docs directory to help understand how we designed this section !!! :) # # MISSION: # 1. one-way fixed individual effect model & random individual effect model (FGLS estimators) # 2. Hausman test & robust Hausman test (Wooldridge, 2010) # 3. Residual QQplots & normality tests, one-way fixed individual effect model (only) # 4. ROBUST: two-ways fixed effect model & Haussman test (to see if one-way & two-ways are consistent) # 5. ROBUST: pooling, OLS # 6. ROBUST: pooling, FGLS # # 5. ROBUST: add possible independent: source("./scripts/proc_3_CHARLS.r") # LEGACY IN WORKSPACE: # NOTE: pls go to ./output/proc_3_CHARLS/ for output results (applicable) # -------------------------------------------

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/rd_regress.R

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cjs-quant/rd-simulation

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rd_regress.R

rd_regress = function(data, order, bw_l, bw_r, kernel, discontinuity) { # DESCRIPTION: # takes RD data and computes an RD regression with # provided order and bandwidths. returns betas, variances, # t-statistics, p-values, and the data used in the RD # ARGUMENTS: # data: the input data in the RD - RV in col 1, observations in col2 # order: order of RD regression # bw_l: left-bandwidth # bw_r: right-bandwidth # kernel: triangular or uniform computation of kernel weights # discontinuity: selected discontinuity # keep observations if within bandwidths data <- data[which(data$x > -1*bw_l & data$x < bw_r),] # kernel weights if triangular if (kernel == "triangular") { w = 0 w[data$x < 0 & data$x > -1*bw_l] = 1 - abs(data$x[data$x < 0 & data$x > -1*bw_l]/bw_l) w[data$x >= 0 & data$x < bw_r] = 1 - abs(data$x[data$x >= 0 & data$x < bw_r]/bw_r) w = diag(w) } # kernel weights if uniform if (kernel == "uniform") { w = diag(1, nrow(data), nrow(data)) } # dummy for over-cutoff (> 0) data$over_cutoff = ifelse(data$x > 0, 1, 0) # reates variables to be used in RD regression and cbinds together for (i in 1:order) { data[paste(colnames(data[1]),toString(i),sep ="_")] = data[1]^(i) data[paste(colnames(data[1]),"over_cutoff",toString(i),sep ="_")] = data[1]^(i) * data$over_cutoff } # define matrices for OLS y = data$y data$y = NULL data$x = NULL x = data x$constant = 1 x = data.matrix(x) # run OLS beta = solve(t(x) %*% w %*% x) %*% t(x) %*% w %*% y # compute variance of each estimator r = y - x %*% beta sigma2_hat = (t(r) %*% r) / (nrow(x) - ncol(x)) # compute standard errors vcov_beta_hat = c(sigma2_hat) * solve(t(x) %*% x) se = sqrt(diag(vcov_beta_hat)) # compute t-statistic t = beta/se # compute p-values p = 2*pt(abs(t), nrow(x) - 1, lower=FALSE) p = p[1,1] # collect estimates estimate = cbind(discontinuity, beta) colnames(estimate) = c("Discontinuity", "Beta") # create output table out_table = cbind(discontinuity, beta[1], p, beta[nrow(beta)]) colnames(out_table) = c("True Discontinuity", "Estimated Discontinuity", "P-Value", "Control Mean") # store coefficients for regression equation coeffs = list() coeffs[1] = paste(round(beta[nrow(beta)], 2)) coeffs[2] = paste(ifelse(round(beta[1], 2) >= 0, "+", ""), round(beta[1], 2), " \\times \\mathbb{D}_{X > 0}") for (i in 1:order) { if (i == 1) { coeffs[2*i+1] = paste(ifelse(round(beta[2*i+1], 2) >= 0, "+", ""), round(beta[2*i+1], 2), " \\times X") coeffs[2*i+2] = paste(ifelse(round(beta[2*i+2], 2) >= 0, "+", ""), round(beta[2*i+2], 2), " \\times X \\times \\mathbb{D}_{X > 0}") } if (i > 1) { coeffs[2*i+1] = paste(ifelse(round(beta[2*i+1], 2) >= 0, "+", ""), round(beta[2*i+1], 2), " \\times X^", i) coeffs[2*i+2] = paste(ifelse(round(beta[2*i+2], 2) >= 0, "+", ""), round(beta[2*i+2], 2), " \\times X^", i, " \\times \\mathbb{D}_{X > 0}") } } # format regression equation equation = paste0(coeffs) # report estimates with underlying data results = list("estimate" = estimate, "data" = x, "equation" = equation, "out_table" = out_table) return(results) }

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WeiAkaneDeng/SPAC2

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loglk.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PPCA_profile_log_likelihood.R \name{loglk} \alias{loglk} \title{Profile log-likelihood of the PPCA model with sample eigenvalues.} \usage{ loglk(lam, n, tau = 1e-05) } \arguments{ \item{lam}{a numerical vector of positive sample eigenvalues} \item{n}{the full dimension} \item{tau}{a tolerance threshold for the smallest eigenvalue, the default value is 0.001.} } \value{ profile log-likelihood of length \eqn{n-1}. } \description{ The function returns the profile log-likelihood of the PPCA model for each possible choice of \eqn{K (1, 2, \dots, n-1)} at their respective MLEs. The maximum choice was set at \eqn{n-1} because when \eqn{K=n}, the profile log-likelihood is equal to that at \eqn{K=n-1}. }

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linpower.r

linpower <- function(n=10,m=3,k=2,NL=30,beta=c(1,.5,-.5),sigma=2,alpha=.05,nSim=10000,st=3) { set.seed(st) C=matrix(c(0,1,1,0,-1,0),nrow=k);a=C%*%beta X=cbind(rep(1,n),1:n,(1:n)^2) XX=t(X)%*%X;iXX=solve(XX);ix=iXX%*%t(X) cx=C%*%iXX%*%t(C) icx=solve(cx) d=runif(m,min=-1,max=1) Cd=C%*%d qff=qf(1-alpha,df1=k,df2=n-m) Fobs=rep(NA,nSim) lambda=powEMP=powT=seq(from=0,to=30,length=NL) for(i in 1:NL) { nu=as.numeric(sigma*sqrt(lambda[i]/(t(Cd)%*%icx%*%Cd))) beta.alt=beta+nu*d for(isim in 1:nSim) { y=X%*%beta.alt+rnorm(n,sd=sigma) bh=ix%*%y br=bh+iXX%*%t(C)%*%icx%*%(a-C%*%bh) S0=sum((y-X%*%br)^2) Smin=sum((y-X%*%bh)^2) Fobs[isim]=(S0-Smin)/k/Smin*(n-m) } powEMP[i]=mean(Fobs>qff) powT[i]=1-pf(qff,df1=k,df2=n-m,ncp=lambda[i]) } par(mfrow=c(1,1),mar=c(4,4,1,1)) plot(lambda,powT,type="l",ylim=c(0,1),xlab="",ylab="") mtext(side=1,"l",font=5,cex=2,line=2.75) mtext(side=2,"Power",cex=1.5,line=2.5) segments(-1,alpha,lambda[NL],alpha,lty=2) text(20,alpha+.03,paste("a =",alpha),cex=1.5,font=5) points(lambda,powEMP) }

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/Scripts/iii.LineageAnalyses/Use/OrderBiomeDiversity.R

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KerkhoffLab/Bryophytes

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OrderBiomeDiversity.R

#Plotting alpha diversity of BRYOPHYTE orders by biome and continent #Adapted from BiomeDiversity.R #Kathryn Dawdy and Hailey Napier, July 2020 # 0.0 FIRST ---------------------------------------------------------------- #Run DataProcessing.R, Continents.R, BiomeContinents.R, BiomeDiversity.R, #ORange.R, OrdBiomeBP.R, TotalAlpha.R, #then OrderBiomeDF.R - unless you can just load the DF (takes a while) #then MossPlotData.R - or just load the DF OrderBiomeDF <- readRDS("Data/OrderBiomeDF.rds") OrderBiomeContDF <- readRDS("Data/OrderBiomeContDF.rds") #Run OrderRichness.R # 0.1 Load Packages -------------------------------------------------------- require(BIEN) require(maps) require(dplyr) require(maptools) require(raster) require(sp) require(rgdal) require(mapdata) require(mapproj) require(wesanderson) require(ggplot2) require(rasterVis) require(knitr) require(latexpdf) require(vegan) require(gridExtra) require(sf) require(rgeos) require(rworldmap) require(filesstrings) require(forcats) require(tidyverse) require(tmap) # 0.2 Load data ------------------------------------------------------------ OrderBiomeDF <- readRDS("Data/OrderBiomeDF.rds") OrdRichAbove100 <- readRDS("Data/OrdRichAbove100.rds") OrdRich25to100 <- readRDS("Data/OrdRich25to100.rds") OrdRich10to25 <- readRDS("Data/OrdRich10to25.rds") OrdRichBelow10 <-readRDS("Data/OrdRichBelow10.rds") OrderBiomeHemDF <- readRDS("Data/OrderBiomeHemDF.rds") # 0.3 Colors --------------------------------------------------------------- #From wes_palette() hex numbers on GitHub: karthik/wesanderson #Color scheme for biomes (in order of BiomeNames (BiomeProcessing.R)) cols7 <- c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#C7B19C", "#798E87", "#C27D38") #Colors used for plots (# corresponds to # of boxplots/length of data) biome_cols_11 <- c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#C7B19C", "#798E87", "#C27D38") biome_cols_22 <- c(biome_cols_11, biome_cols_11) biome_cols_87 <- c(biome_cols_22, biome_cols_11, "#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38", biome_cols_22, biome_cols_22) biome_cols_66 <- c(biome_cols_22, biome_cols_22, biome_cols_22) biome_cols_166 <- c(biome_cols_22, c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38"), biome_cols_11, c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#C27D38"), c("#D8B70A", "#972D15", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#C7B19C", "#798E87", "#C27D38"), c("#81A88D", "#FDD262", "#D3DDDC"), c("#D8B70A", "#972D15", "#81A88D", "#446455", "#D3DDDC", "#C7B19C", "#798E87"), c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38"), "#81A88D", c("#972D15", "#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38"), c("#D8B70A", "#446455", "#FDD262", "#D3DDDC"), biome_cols_22, c("#D8B70A", "#972D15", "#81A88D", "#D3DDDC", "#C7B19C", "#C27D38"), c("#D8B70A", "#446455", "#FDD262", "#D3DDDC", "#798E87", "#C27D38"), c("#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C"), c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38"), biome_cols_11, c("#D8B70A", "#C27D38"), biome_cols_11) biome_cols_29 <- c(biome_cols_11, c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#798E87", "#C27D38"), c("#972D15", "#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38")) biome_cols_18 <- c(c("#D8B70A", "#972D15", "#A2A475", "#81A88D", "#02401B", "#446455", "#FDD262", "#D3DDDC", "#798E87", "#C27D38"), c("#972D15", "#A2A475", "#81A88D", "#02401B", "#FDD262", "#D3DDDC", "#C7B19C", "#C27D38")) # 1.0 BIOME RICHNESS BY ORDER ---------------------------------------------- #Hailey's function: source("Functions/OrdBiomeBP.R") ###Run OrdBiomeBP.R #Use OrdBiomeBP function for box, violin, or layered violin on box plot #Enter any order; box, violin, or boxyviolin; cont = "Southern", "Northern", or "both" OrdBiomeBP("Hypnales", "box") OrdBiomeBP("Hypnales", "violin") OrdBiomeBP("Hypnales", "boxyviolin") OrdBiomeBP("Hypnales", "boxyviolin", cont="South America") OrdBiomeBP("Hypnales", "boxyviolin", cont="North America") OrdBiomeBP("Hypnales", "boxyviolin", cont="both") # 2.0 RICHNESS FACET PLOTS in biomes by order ------------------------------ # 2.1 Facet of richness in biomes by all orders ---------------------------- FacetOrdBiomeRich <- ggplot(OrderBiomeDF, aes(x=Biome, y=Alpha)) + geom_boxplot(show.legend = FALSE) + theme_minimal() + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=20), axis.text.x = element_text(angle = 90, hjust = 1, size = 8))+ facet_wrap(~Order) FacetOrdBiomeRich png("Figures/AlphaOrderBiomeFacet.png", width = 1500, height = 1000, pointsize = 20) FacetOrdBiomeRich dev.off() # 2.2 Subset order by maximum alpha diversity value ------------------------ ###Run OrderRichness.R # max α > 100 OrdRichAbove100 <- readRDS("Data/OrdRichAbove100.rds") OrdRichAbove100 OBRAbove100DF <- subset(OrderBiomeDF, OrderBiomeDF$Order=="Hypnales"| OrderBiomeDF$Order=="Dicranales") # max α 25-100 OrdRich25to100 <- readRDS("Data/OrdRich25to100.rds") OrdRich25to100 OBR25to100DF <- subset(OrderBiomeDF, OrderBiomeDF$Order=="Bartramiales"| OrderBiomeDF$Order=="Bryales"| OrderBiomeDF$Order=="Grimmiales"| OrderBiomeDF$Order=="Hookeriales"| OrderBiomeDF$Order=="Jungermanniales"| OrderBiomeDF$Order=="Orthotrichales"| OrderBiomeDF$Order=="Porellales"| OrderBiomeDF$Order=="Pottiales") # max α 10-25 OrdRich10to25 <- readRDS("Data/OrdRich10to25.rds") OrdRich10to25 OBR10to25DF <- subset(OrderBiomeDF, OrderBiomeDF$Order=="Funariales"| OrderBiomeDF$Order=="Hedwigiales"| OrderBiomeDF$Order=="Marchantiales"| OrderBiomeDF$Order=="Metzgeriales"| OrderBiomeDF$Order=="Polytrichales"| OrderBiomeDF$Order=="Sphagnales") # max α < 10 OrdRichBelow10 <-readRDS("Data/OrdRichBelow10.rds") OrdRichBelow10 OBRBelow10DF <- subset(OrderBiomeDF, OrderBiomeDF$Order!="Hypnales"& OrderBiomeDF$Order!="Dicranales"& OrderBiomeDF$Order!="Bartramiales"& OrderBiomeDF$Order!="Bryales"& OrderBiomeDF$Order!="Grimmiales"& OrderBiomeDF$Order!="Hookeriales"& OrderBiomeDF$Order!="Jungermanniales"& OrderBiomeDF$Order!="Orthotrichales"& OrderBiomeDF$Order!="Porellales"& OrderBiomeDF$Order!="Pottiales"& OrderBiomeDF$Order!="Funariales"& OrderBiomeDF$Order!="Hedwigiales"& OrderBiomeDF$Order!="Marchantiales"& OrderBiomeDF$Order!="Metzgeriales"& OrderBiomeDF$Order!="Polytrichales"& OrderBiomeDF$Order!="Sphagnales") # max α < 25 #We ended up not using the <25 grouping, but keeping this just in case... #OrdRichBelow25 #OBRBelow25DF <- subset(OrderBiomeDF, #OrderBiomeDF$Order!="Hypnales"& #OrderBiomeDF$Order!="Dicranales"& #OrderBiomeDF$Order!="Bartramiales"& #OrderBiomeDF$Order!="Bryales"& #OrderBiomeDF$Order!="Grimmiales"& #OrderBiomeDF$Order!="Hookeriales"& #OrderBiomeDF$Order!="Jungermanniales"& #OrderBiomeDF$Order!="Orthotrichales"& #OrderBiomeDF$Order!="Porellales"& #OrderBiomeDF$Order!="Pottiales") saveRDS(OBRAbove100DF, "Data/OBRAbove100DF.rds") saveRDS(OBR25to100DF, "Data/OBR25to100DF.rds") saveRDS(OBR10to25DF, "Data/OBR10to25DF.rds") saveRDS(OBRBelow10DF, "Data/OBRBelow10DF.rds") # 2.2.5 Subset for cells that don't have centers covered # max α > 100 OrdRichAbove100 <- readRDS("Data/OrdRichAbove100.rds") OrdRichAbove100 OBRAbove100DF <- subset(CleanOrderBiomeDF, CleanOrderBiomeDF$Order=="Hypnales"| CleanOrderBiomeDF$Order=="Dicranales") # max α 25-100 OrdRich25to100 <- readRDS("Data/OrdRich25to100.rds") OrdRich25to100 OBR25to100DF <- subset(CleanOrderBiomeDF, CleanOrderBiomeDF$Order=="Bartramiales"| CleanOrderBiomeDF$Order=="Bryales"| CleanOrderBiomeDF$Order=="Grimmiales"| CleanOrderBiomeDF$Order=="Hookeriales"| CleanOrderBiomeDF$Order=="Jungermanniales"| CleanOrderBiomeDF$Order=="Orthotrichales"| CleanOrderBiomeDF$Order=="Porellales"| CleanOrderBiomeDF$Order=="Pottiales") # max α 10-25 OrdRich10to25 <- readRDS("Data/OrdRich10to25.rds") OrdRich10to25 OBR10to25DF <- subset(CleanOrderBiomeDF, CleanOrderBiomeDF$Order=="Funariales"| CleanOrderBiomeDF$Order=="Hedwigiales"| CleanOrderBiomeDF$Order=="Marchantiales"| CleanOrderBiomeDF$Order=="Metzgeriales"| CleanOrderBiomeDF$Order=="Polytrichales"| CleanOrderBiomeDF$Order=="Sphagnales") # max α < 10 OrdRichBelow10 <-readRDS("Data/OrdRichBelow10.rds") OrdRichBelow10 OBRBelow10DF <- subset(CleanOrderBiomeDF, CleanOrderBiomeDF$Order!="Hypnales"& CleanOrderBiomeDF$Order!="Dicranales"& CleanOrderBiomeDF$Order!="Bartramiales"& CleanOrderBiomeDF$Order!="Bryales"& CleanOrderBiomeDF$Order!="Grimmiales"& CleanOrderBiomeDF$Order!="Hookeriales"& CleanOrderBiomeDF$Order!="Jungermanniales"& CleanOrderBiomeDF$Order!="Orthotrichales"& CleanOrderBiomeDF$Order!="Porellales"& CleanOrderBiomeDF$Order!="Pottiales"& CleanOrderBiomeDF$Order!="Funariales"& CleanOrderBiomeDF$Order!="Hedwigiales"& CleanOrderBiomeDF$Order!="Marchantiales"& CleanOrderBiomeDF$Order!="Metzgeriales"& CleanOrderBiomeDF$Order!="Polytrichales"& CleanOrderBiomeDF$Order!="Sphagnales") saveRDS(OBRAbove100DF, "Data/CleanOBRAbove100DF.rds") saveRDS(OBR25to100DF, "Data/CleanOBR25to100DF.rds") saveRDS(OBR10to25DF, "Data/CleanOBR10to25DF.rds") saveRDS(OBRBelow10DF, "Data/CleanOBRBelow10DF.rds") # 2.3 Load max richness value groupings ------------------------------------ OBRAbove100DF <- readRDS("Data/OBRAbove100DF.rds") OBR25to100DF <- readRDS("Data/OBR25to100DF.rds") OBR10to25DF <- readRDS("Data/OBR10to25DF.rds") OBRBelow10DF <- readRDS("Data/OBRBelow10DF.rds") # 2.4 Facets of richness in biomes by orders grouped by max α -------------- # 2.4.1 Max α >100 --------------------------------------------------------- FacetOBRAbove100 <- ggplot(OBRAbove100DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_22, color="black", outlier.size=1) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Order , #ncol=1 #un-comment # of rows you want ncol=2 ) FacetOBRAbove100 png("Figures/AlphaOrderBiomeAbove100.png", width = 1500, height = 1000, pointsize = 20) FacetOBRAbove100 dev.off() # 2.4.2 Max α 25-100 ------------------------------------------------------- FacetOBR25to100 <- ggplot(OBR25to100DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_87, color="black", outlier.size=0.7) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Order , #ncol=2 #un-comment # of rows you want ncol=4 ) FacetOBR25to100 png("Figures/AlphaOrderBiome25to100.png", width = 1500, height = 1000, pointsize = 20) FacetOBR25to100 dev.off() # 2.4.3 Max α 10-25 -------------------------------------------------------- FacetOBR10to25 <- ggplot(OBR10to25DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome )) + geom_boxplot(show.legend = FALSE, fill=biome_cols_66, color="black", outlier.size=0.7) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Order , ncol=3 #un-comment # of rows you want #ncol=2 ) FacetOBR10to25 png("Figures/AlphaOrderBiome10to25.png", width = 1500, height = 1000, pointsize = 20) FacetOBR10to25 dev.off() # 2.4.4 Max α <10 ---------------------------------------------------------- FacetOBRBelow10 <- ggplot(OBRBelow10DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_166, color="black", outlier.size=0.7) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + #geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 90, hjust = 1, size = 8))+ facet_wrap(~Order , ncol=6 #un-comment # of rows you want #ncol=5 ) FacetOBRBelow10 png("Figures/AlphaOrderBiomeBelow10.png", width = 1500, height = 1000, pointsize = 20) FacetOBRBelow10 dev.off() # 2.5 Plots with weighted cell biome count # 2.5.1 Max α >100 --------------------------------------------------------- CleanFacetOBRAbove100 <- ggplot(CleanOBRAbove100DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_22, color="black", outlier.size=1) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Order , #ncol=1 #un-comment # of rows you want ncol=2 ) CleanFacetOBRAbove100 png("Figures/CleanAlphaOrderBiomeAbove100.png", width = 1500, height = 1000, pointsize = 20) CleanFacetOBRAbove100 dev.off() # 2.4.2 Max α 25-100 ------------------------------------------------------- CleanFacetOBR25to100 <- ggplot(CleanOBR25to100DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_87, color="black", outlier.size=0.7) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Order , #ncol=2 #un-comment # of rows you want ncol=4 ) CleanFacetOBR25to100 png("Figures/CleanAlphaOrderBiome25to100.png", width = 1500, height = 1000, pointsize = 20) CleanFacetOBR25to100 dev.off() # 2.4.3 Max α 10-25 -------------------------------------------------------- CleanFacetOBR10to25 <- ggplot(CleanOBR10to25DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome )) + geom_boxplot(show.legend = FALSE, fill=biome_cols_66, color="black", outlier.size=0.7) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Order , ncol=3 #un-comment # of rows you want #ncol=2 ) CleanFacetOBR10to25 png("Figures/CleanAlphaOrderBiome10to25.png", width = 1500, height = 1000, pointsize = 20) CleanFacetOBR10to25 dev.off() # 2.4.4 Max α <10 ---------------------------------------------------------- CleanFacetOBRBelow10 <- ggplot(CleanOBRBelow10DF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_166, color="black", outlier.size=0.7) + #theme_minimal() + #un-comment whichever theme you want theme_gray() + #theme_light() + #theme_bw() + #theme_dark() + #theme_linedraw() + #geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 90, hjust = 1, size = 8))+ facet_wrap(~Order , ncol=6 #un-comment # of rows you want #ncol=5 ) CleanFacetOBRBelow10 png("Figures/CleanAlphaOrderBiomeBelow10.png", width = 1500, height = 1000, pointsize = 20) CleanFacetOBRBelow10 dev.off() # 3.0 CONTINENT FACET PLOTS ----------------------------------------------- FacetContBiomeRich <- ggplot(OrderBiomeContDF, aes(x=Biome, y=Alpha, fill=Biome, color=Biome)) + geom_boxplot(show.legend = FALSE, fill=biome_cols_18, color="black") + guides(x = guide_axis(angle=30)) + theme_gray() + geom_violin(scale="count", show.legend=FALSE, fill="gray", alpha=0.35, color="gray25") + xlab("Biome") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=15), axis.text.x = element_text(angle = 30, hjust = 1, size = 8))+ facet_wrap(~Cont) CleanFacetContBiomeRich png("Figures/AlphaBiomeContinents.png", width = 1500, height = 1000, pointsize = 20) FacetContBiomeRich dev.off() # 4.0 Orders on x axis, facet of biomes boxplots --------------------------- FacetRichOrder <- ggplot(OrderBiomeDF, aes(x=Order, y=Alpha)) + geom_boxplot(show.legend = FALSE) + theme_minimal() + xlab("Order") + ylab("Richness") + theme(axis.title.y = element_text(size=32), axis.title.x = element_text(size=32), axis.text.y = element_text(size=20), axis.text.x = element_text(angle = 90, hjust = 1, size = 6))+ facet_wrap(~Biome) FacetRichOrder png("Figures/AlphaOrderBiomeSwapped.png", width = 1500, height = 1000, pointsize = 20) FacetRichOrder dev.off()

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library(RSDA) ### Name: lynne2 ### Title: Symbolic interval data example. ### Aliases: lynne2 ### Keywords: datasets ### ** Examples data(lynne2) display.sym.table(lynne2)

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vg<- read.csv("~/Desktop/vgsales.csv") vg$Platform<-as.factor(vg$Platform) vg<-vg[complete.cases(vg),] vg$Genre<-as.factor(vg$Genre) vg$Year<-as.numeric(vg$Year) vg$Publisher<-as.factor(vg$Publisher) #to check for repeated observations library(dplyr) dim(distinct(vg)) dim(vg) #EDA library(ggplot2) #From 1980-2020 which.max(table(vg$Publisher))#EA has most games plot(table(vg$Platform),las=2)#DS,PS2 most platforms plot(table(vg$Genre),las=2)#ACTION games made more cor(vg$Global_Sales,vg$EU_Sales) cor(vg$Global_Sales,vg$JP_Sales) cor(vg$Global_Sales,vg$NA_Sales) cor(vg$Global_Sales,vg$Other_Sales) g<-ggplot(vg,aes(Platform))+geom_bar(aes(color=Global_Sales)) #global sales is more in DS,PS2,wii,xbox360 a<-ggplot(vg,aes(Genre))+geom_bar(aes(color=Global_Sales)) #action sales is more followed by sports plot(table(vg$Year)) #more games releases during the 2000s,2007-8 #top 10 publishers b<-sort(table(vg$Publisher),decreasing = TRUE) plot(b[1:10],las=2) #best genre every year e<-group_by(vg,Year,Genre) f<-summarise(e,sum(Global_Sales)) j<-top(f,1) ggplot(data =j,aes(x = Year, y = j$`sum(Global_Sales)`,fill=Genre))+geom_bar(stat = "identity") #best platform every year h<-group_by(vg,Year,Platform) i<-summarise(h,sum(Global_Sales)) ggplot(data =i,aes(x = Year, y = i$`sum(Global_Sales)`,fill=Platform))+geom_line(stat = "identity",aes(color= Platform)) #best game every year k<-group_by(vg,Year) m<-top_n(k,1) ggplot(m,aes(m$Global_Sales,m$Name,fill=Year))+geom_bar(stat = "identity")+ theme(axis.text.x = element_text(angle=45,hjust=1), plot.title = element_text(hjust=.5)) #z<-group_by(vg,Genre) #z<-summarise(z,count=n()) #z<-top_n(z,1) #ggplot(z,aes(z$Genre,z$count))+geom_bar(stat ="identity")

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library('ggplot2') load("duin.RData") load("guo.RData") duin_summary_q30 <- subset(duin_results$summary, q == 30) duin_summary_q50 <- subset(duin_results$summary, q == 50) duin_summary_q100 <- subset(duin_results$summary, q == 100) guo_summary_q30 <- subset(guo_results$summary, q == 30) guo_summary_q50 <- subset(guo_results$summary, q == 50) guo_summary_q100 <- subset(guo_results$summary, q == 100) p <- ggplot(duin_summary_q50, aes(x = n, y = error, group = method, color = method)) p <- p + geom_path(aes(color = method, linetype = method)) + ylim(c(0, .45)) p <- p + geom_point(aes(shape = method)) p <- p + xlab("Training Data Size") + ylab("Expected Error Rate") + opts(title = plot_title) p + theme_set(theme_bw()) eer_plots <- function(results, plot_title = "TODO: Add plot title", ymax = 0.5, save = F, file) { p <- ggplot(results, aes(x = n, y = error, group = method, color = method)) p <- p + geom_path(aes(color = method, linetype = method)) + ylim(c(0, ymax)) p <- p + geom_point(aes(shape = method)) p <- p + xlab(expression(n[k])) + ylab("Expected Error Rate") + opts(title = plot_title) p <- p + theme_set(theme_bw()) if(save) ggsave(filename = file, plot = p) p } cer_boxplot <- function(results, nk = 100, q = 100, plot_title = "TODO: Add plot title", ymax = 0.5, save = F, file) { plot_title <- paste(plot_title, "(q = ", q, ", ", expression(n_k), " = ", nk, ")", sep = "") p <- ggplot(subset(results, n == nk), aes(x = method, y = error)) p <- p + ylim(c(0, ymax)) + geom_boxplot(color = I("#3366FF")) p <- p + xlab("") + ylab("Conditional Error Rate") + opts(title = plot_title) p <- p + theme_set(theme_bw()) if(save) ggsave(filename = file, plot = p) p } # Duin Plots duin_q30 <- subset(duin_results$results, q == 30) duin_q50 <- subset(duin_results$results, q == 50) duin_q100 <- subset(duin_results$results, q == 100) cer_boxplot(duin_q50, nk = 10, q = 50, plot_title = "Duin Simulation Configuration", ymax = 0.6, save = T, file = "duin-box-10.eps") cer_boxplot(duin_q50, nk = 30, q = 50, plot_title = "Duin Simulation Configuration", ymax = 0.6, save = T, file = "duin-box-30.eps") cer_boxplot(duin_q50, nk = 50, q = 50, plot_title = "Duin Simulation Configuration", ymax = 0.6, save = T, file = "duin-box-50.eps") eer_plots(duin_summary_q30, "Duin Simulation Configuration (q = 30)", 0.45) eer_plots(duin_summary_q50, "Duin Simulation Configuration(q = 50)", 0.45, save = T, file = "duin50.eps") eer_plots(duin_summary_q100, "Duin Simulation Configuration (q = 100)", 0.5)) # Guo Plots guo_q30 <- subset(guo_results$results, q == 30) guo_q50 <- subset(guo_results$results, q == 50) guo_q100 <- subset(guo_results$results, q == 100) cer_boxplot(guo_q100, nk = 10, q = 100, plot_title = "Guo Simulation Configuration", ymax = 0.6, save = T, file = "guo-box-10.eps") cer_boxplot(guo_q100, nk = 50, q = 100, plot_title = "Guo Simulation Configuration", ymax = 0.6, save = T, file = "guo-box-50.eps") cer_boxplot(guo_q100, nk = 70, q = 100, plot_title = "Guo Simulation Configuration", ymax = 0.6, save = T, file = "guo-box-70.eps") eer_plots(guo_summary_q30, "Guo Simulation Configuration (q = 30)", 0.35) eer_plots(guo_summary_q50, "Guo Simulation Configuration (q = 50)", 0.35) eer_plots(guo_summary_q100, "Guo Simulation Configuration (q = 100)", 0.40, save = T, file = "guo100.eps")

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CIAT-DAPA/aeps_Grupo_Santa_Maria_DM

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r

Cluster_DTW_embolse_events_filtrados.R

# Analisis cluster de patrones de embolse # Hugo Andres Dorado B # 11-10-2018 library(ggplot2) library(dtw) library(dtwclust) library(plyr) library(zoo) rm(list=ls()) source('SCRIPTS/funciones_cluster_temporal.R') source('SCRIPTS/Cluster_DTW_embolse_FUN.R') # Leer y alistar datos embolse <- read.csv('DATOS/consolidado_embolse_removiendo_interv.csv') embolse <- embolse[!is.na(embolse$Embolse),] embolse_lst <- split( embolse , paste(embolse$finca,embolse$anio,embolse$Lote,sep='_') ) conteoFilas <- do.call(rbind,lapply(embolse_lst,dim)) summary(conteoFilas) boxplot(conteoFilas[,1]) conteoFilas <- conteoFilas[order(conteoFilas[,1],decreasing = F),] ct <- conteoFilas[conteoFilas[,1] > 51,] emls_50_51 <- embolse_lst[row.names(ct)] emls_50_51 <- lapply(emls_50_51, function(w){ w[order(w$Semana),] }) time_series <- lapply( emls_50_51,function(x){x['Embolse']} ) time_series <- lapply( time_series,function(x){ data.frame(Embolse = rollmean(x$Embolse ,4) ) } ) time_series <- lapply(time_series,ts) plot(time_series[[1]],las=2) # time_series <- time_series[1:30] # lapply(time_series,function(x){plot(x,type='l')}) ##----------------------------------------------------------------------------## # -------------------Calcular matriz de distancia dtw--------------------------- distAllMatrix <- distDtwMV(time_series) save(distAllMatrix,file=here::here('DATOS','distAllMatrix_media_movil_sin_interve.RDATA')) #load(here::here('DATOS','distAllMatrix_media_movil_sin_interve.RDATA')) # Cluster a (25 CLUSTER) hClustEvents <- hirarCluster(distAllMatrix) dfResults <- data.frame(Nam_time_series = names(time_series), clust = hClustEvents) spl_time_series <- split(time_series,hClustEvents) dirSave <- 'CLUSTER_25_SIN_INTER' if(!dir.exists(dirSave)){dir.create(dirSave)} write.csv(dfResults,paste(dirSave,'/',dirSave,'.csv',sep=''),row.names = F) tablResults <- ddply(dfResults,~clust,summarise,conteo = length(clust)) write.csv(tablResults,paste(dirSave,'/conteo_',dirSave,'.csv',sep=''),row.names = F) graphics_cluster(ts= time_series,ts_per_cluster=spl_time_series,dirSave,limites_fijos=F) #------------------------------------------------------------------------------ #--------------- Indetificar los centroides de las curvas---------------------- library(gtools) # Agrupar cada curva en una matriz groupsTable <- lapply(spl_time_series, function(w){ ltx <- lapply(w,function(q){ g <- as.numeric(q[,1]) names(g) <- 1:length(q[,1]) g }) do.call(smartbind,ltx) } ) centroids <- lapply(groupsTable,function(w){ ts(data.frame(Embolse = apply(w,2,median,na.rm=T)))} ) names(spl_time_series) names(centroids) centroids_obs <- sapply( names(spl_time_series) ,function(w){ names(which.min(sapply(spl_time_series[[w]],function(x){dtw(centroids[[w]],x)$distance}))) } ) centerPatterns <- lapply(time_series[centroids_obs],function(w){data.frame(week=1:length(w),Embolse = w[,1])}) names(centerPatterns) <- names(centroids_obs) namCenPatt <- names(centerPatterns) patterCluster <- do.call(rbind, lapply(seq(length(centerPatterns)), function(g){dfa <- data.frame(Group=as.character(namCenPatt[g]),centerPatterns[[g]]) dfa$Group <- as.character(dfa$Group ) dfa$week <- as.numeric(dfa$week) dfa$Embolse <- as.numeric(dfa$Embolse) dfa } ) ) patterCluster$Group <- as.numeric(patterCluster$Group) tablResults$clust mg_DS <- merge(patterCluster,tablResults,by.x= 'Group',by.y='clust',all.x = T ,all.y =F,sort=F) mg_DS$Group_Count <- paste('G',mg_DS$Group,'_C',mg_DS$conteo,sep='') mg_DS_1 <- mg_DS[mg_DS$conteo > 6,] g1 <- ggplot(mg_DS,aes(x=week,y=Embolse))+geom_point(aes(colour=factor(Group_Count)),size = 0.3)+ geom_line(aes(colour=factor(Group_Count)),size = 0.3)+theme_bw() g2 <- ggplot(mg_DS,aes(x=week,y=Embolse))+geom_point(aes(colour=factor(Group_Count)))+ geom_line(aes(colour=factor(Group_Count)))+facet_wrap(~Group_Count,scales = 'free')+theme_bw() g3 <- ggplot(mg_DS_1,aes(x=week,y=Embolse))+geom_point(aes(colour=factor(Group_Count)),size = 0.3)+ geom_line(aes(colour=factor(Group_Count)),size = 0.3)+theme_bw() ggsave('CLUSTER_25_SIN_INTER/ghrap_lin_sin_interve.png',g1) ggsave('CLUSTER_25_SIN_INTER/ghra_wrap_sin_interve.png',g2) ggsave('CLUSTER_25_SIN_INTER/ghrap_lin_mas_5_sin_interve.png',g3) write.csv(mg_DS_1,'CLUSTER_25_SIN_INTER/datos_grafico_centro_cluster_sin_interve.csv') write.csv(mg_DS_1,'CLUSTER_22_MA/datos_grafico_centro_cluster_sin_interve.csv')

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/plot3.R

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dse-arvind/Peer-graded-Assignment-Course-Project-2

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refs/heads/master

2022-11-05T23:45:57.064556

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plot3.R

g<-ggplot(aes(x = year, y = Emissions, fill=type), data=NEIdataBaltimore) g+geom_bar(stat="identity")+ facet_grid(.~type)+ labs(x="year", y=expression("Total PM"[2.5]*" Emission (Tons)")) + labs(title=expression("PM"[2.5]*" Emissions, Baltimore City 1999-2008 by Source Type"))+ guides(fill=FALSE)

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/Effect of Coupons grocery Retail/marketing_project.R

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as75999/Projects

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f5b8e5891e0ceda2b8c6364d1e3ef2be516acaf3

refs/heads/master

2016-08-12T03:31:50.743275

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marketing_project.R

library(sqldf) #loading files data=read.csv('D:/Fall/MA/Project/grocery_data.csv') # getting the column names colnames(data) #penetration by commodity sqldf('SELECT commodity,count(distinct household) household from data group by 1') #Penetration by brand #pasta sqldf('SELECT brand,count(distinct household) household from data where commodity="pasta" group by 1 order by 2 desc') #pancakes sqldf('SELECT brand,count(distinct household) household from data where commodity="pancake mixes" group by 1 order by 2 desc') #pasta sauce sqldf('SELECT brand,count(distinct household) household from data where commodity="pasta sauce" group by 1 order by 2 desc') #syrups sqldf('SELECT brand,count(distinct household) household from data where commodity="syrups" group by 1 order by 2 desc') #Penetration by combinations of brand of different complimentary commodity ########################################################################## Commodity_by_Trip=sqldf(' select basket, brand, max(case when commodity = "pasta" then 1 else 0 end) as pasta, max(case when commodity = "pasta sauce" then 1 else 0 end) as pasta_sauce, max(case when commodity = "pancake mixes" then 1 else 0 end) as pancake_mixes, max(case when commodity = "syrups" then 1 else 0 end) as syrups from data group by basket,brand ') Pancake_and_Syrup =sqldf(' select basket, pancake_mixes, syrups, brand from Commodity_by_Trip where pancake_mixes = 1 or syrups =1 ') Pancake_and_Syrup_1 =sqldf(' select basket, sum(pancake_mixes) pancake_mixes, sum(syrups) syrups from Pancake_and_Syrup group by basket having sum(pancake_mixes) > 0 and sum(syrups) > 0 ') Pancake_and_Syrup_2=sqldf(' SELECT a.* FROM Pancake_and_Syrup a join Pancake_and_Syrup_1 b ON a.basket=b.basket ') Pancake =sqldf(' select basket, brand from Pancake_and_Syrup_2 where pancake_mixes >= 1 order by basket ') Syrup =sqldf(' select basket, brand from Pancake_and_Syrup_2 where syrups >=1 order by basket ') Pancake_and_Syrup_3 =sqldf(' select b.basket, a.brand as pancake_brand, b.brand as syrup_brand from Pancake a, Syrup b where a.basket = b.basket order by b.basket, a.brand, b.brand ') ############## results for pan cakes and syrup sqldf(' select pancake_brand, syrup_brand, count(basket) from Pancake_and_Syrup_3 group by pancake_brand, syrup_brand ') ############## pasta_sauce_and_pasta =sqldf(' select basket, pasta_sauce, pasta, brand from Commodity_by_Trip where pasta_sauce = 1 or pasta =1 ') pasta_sauce_and_pasta_1 =sqldf(' select basket, sum(pasta_sauce) pasta_sauce, sum(pasta) pasta from pasta_sauce_and_pasta group by basket having sum(pasta_sauce) > 0 and sum(pasta) > 0 ') pasta_sauce_and_pasta_2=sqldf(' SELECT a.* FROM pasta_sauce_and_pasta a join pasta_sauce_and_pasta_1 b ON a.basket=b.basket ') pasta_sauce =sqldf(' select basket, brand from pasta_sauce_and_pasta_2 where pasta_sauce >= 1 order by basket ') pasta =sqldf(' select basket, brand from pasta_sauce_and_pasta_2 where pasta >=1 order by basket ') pasta_sauce_and_pasta_3 =sqldf(' select b.basket, a.brand as pasta_sauce_brand, b.brand as pasta_brand from pasta_sauce a, pasta b where a.basket = b.basket order by b.basket, a.brand, b.brand ') ############## results for pasta and pasta sauce sqldf(' select pasta_sauce_brand, pasta_brand, count(basket) num_basket from pasta_sauce_and_pasta_3 group by pasta_sauce_brand, pasta_brand order by count(basket) desc ') ############### ################################################################################ ##Brand loyality INDEX BL=sqldf(' select household, commodity, brand, sum(units) as quantity from data group by household, commodity, brand' ) BL1=sqldf(' select household, commodity, sum(units) as total_quantity from data group by household, commodity') BL2=sqldf(' select a.household, a.commodity, a.brand, a.quantity/b.total_quantity as bli from BL a, BL1 b where a.household = b.household and a.commodity = b.commodity group by a.household, a.commodity, a.brand ') ###output for perceptual maps###### #Pasta pasta_bli=sqldf('select brand , avg(bli) pasta_bli,count(household) pasta_hd from BL2 where commodity="pasta" group by 1 order by count(household) desc ' ) #Patsa Sauce pasta_sauce_bli=sqldf('select brand , avg(bli) pasta_Sauce_bli,count(household) pasta_Sauce_hd from BL2 where commodity="pasta sauce" group by 1 order by count(household) desc ' ) pasta_past_sauce_bli=sqldf('select a.brand, a.pasta_bli, a.pasta_hd,b.pasta_Sauce_bli, b.pasta_Sauce_hd from pasta_bli a ,pasta_sauce_bli b where a.brand=b.brand') View(pasta_past_sauce_bli) #Pancakes pancake_mixes_bli=sqldf('select brand , avg(bli) pancake_mixes_bli,count(household) pancake_mixes_hd from BL2 where commodity="pancake mixes" group by 1 order by count(household) desc ' ) #Syrup syrups_bli=sqldf('select brand , avg(bli) syrups_bli,count(household) syrups_hd from BL2 where commodity="syrups" group by 1 order by count(household) desc ' ) pancake_syrups_bli=sqldf('select a.brand,a.pancake_mixes_bli, a.pancake_mixes_hd,b.syrups_bli, b.syrups_hd from pancake_mixes_bli a ,syrups_bli b where a.brand=b.brand') View(pancake_syrups_bli) ########################################################################## #first coupon usage coupon=sqldf('select brand,commodity,household,min(day) min_day from data where coupon =1 group by 1,2,3'); non_coupon=sqldf('select brand,commodity,household,min(day) min_day from data where coupon =0 group by 1,2,3'); first_household=sqldf('select a.brand,a.commodity,count(distinct a.household) num_household FROM coupon a,non_coupon b where a.brand=b.brand and a.commodity=b.commodity and a.household=b.household and a.min_day<b.min_day group by 1,2') coupon_household=sqldf('select brand,commodity, count(distinct(household)) num_household FROM coupon group by 1,2'); #output to find for which branch coupon has been effective coupon =sqldf('select a.brand, a.commodity, a.num_household total_h, b.num_household from first_household a,coupon_household b where a.brand=b.brand and a.commodity=b.commodity and b.num_household>a.num_household group by 1,2') coupon$household_onboarded=round((coupon$total_h/coupon$num_household)*100,1) coupon_pasta=sqldf('select * from coupon where commodity="pasta"') coupon_pasta_sauce=sqldf('select * from coupon where commodity="pasta sauce"') coupon_pancake_mixes=sqldf('select * from coupon where commodity="pancake mixes"') coupon_syrups=sqldf('select * from coupon where commodity="syrups"') library(ggplot2) A=ggplot(coupon_pasta,aes(x=brand,y=household_onboarded))+geom_bar(stat="identity", fill='red')+ ggtitle("Coupon Effect On Pasta Brand Adoption") A=A + theme(plot.title=element_text(size=20,face='bold',color="dark gray")) A=A +theme(axis.title.x=element_blank()) A=A +theme(axis.title.y=element_blank()) A=A+geom_text(aes(label=round(household_onboarded,3)),vjust=-0.2,color="dark blue",size=8) A=A+theme(axis.text=element_text(size=16)) A A=ggplot(coupon_pasta_sauce,aes(x=brand,y=household_onboarded))+geom_bar(stat="identity", fill='salmon2')+ ggtitle("Coupon Effect On Pasta Sauce Brand Adoption") A=A + theme(plot.title=element_text(size=20,face='bold',color="dark gray")) A=A +theme(axis.title.x=element_blank()) A=A +theme(axis.title.y=element_blank()) A=A+geom_text(aes(label=round(household_onboarded,3)),vjust=-0.2,color="dark blue",size=8) A=A+theme(axis.text=element_text(size=16)) A A=ggplot(coupon_pancake_mixes,aes(x=brand,y=household_onboarded))+geom_bar(stat="identity", fill='tan3')+ ggtitle("Coupon Effect On Pankcake Mixes Brand Adoption") A=A + theme(plot.title=element_text(size=20,face='bold',color="dark gray")) A=A +theme(axis.title.x=element_blank()) A=A +theme(axis.title.y=element_blank()) A=A+geom_text(aes(label=round(household_onboarded,3)),vjust=-0.2,color="dark blue",size=8) A=A+theme(axis.text=element_text(size=16)) A A=ggplot(coupon_syrups,aes(x=brand,y=household_onboarded))+geom_bar(stat="identity", fill='gold1')+ ggtitle("Coupon Effect On Syrup Brand Adoption") A=A + theme(plot.title=element_text(size=20,face='bold',color="dark gray")) A=A +theme(axis.title.x=element_blank()) A=A +theme(axis.title.y=element_blank()) A=A+geom_text(aes(label=round(household_onboarded,3)),vjust=-0.2,color="dark blue",size=8) A=A+theme(axis.text=element_text(size=16)) A ggplot(coupon_pasta_sauce,aes(x=brand,y=household_onboarded))+ geom_bar(stat="identity", fill='salmon2')+xlab("Brand")+ ylab("% household onboarded using coupon")+ ggtitle("Pasta Sauce Brand adoption") ggplot(coupon_pancake_mixes,aes(x=brand,y=household_onboarded))+ geom_bar(stat="identity", fill='tan3')+xlab("Brand")+ ylab("% household onboarded using coupon")+ ggtitle("Pancake Mixes Brand adoption") ggplot(coupon_syrups,aes(x=brand,y=household_onboarded))+ geom_bar(stat="identity", fill='gold1')+xlab("Brand")+ ylab("% household onboarded using coupon")+ ggtitle("Syrups Brand adoption") ##################################################################################### #Modelling price elasticity agg_data=sqldf(' select week, sum(coupon) num_coupon, sum(Case when commodity ="pasta" then units else 0 end) pasta_sales, avg(Case when commodity ="pasta" then price else 0 end) pasta_price, sum(Case when commodity ="pancake mixes" then units else 0 end) pancake_mixes_sales, avg(Case when commodity ="pancake mixes" then price else 0 end) pancake_mixes_price, sum(Case when commodity ="pasta sauce" then units else 0 end) pasta_sauce_sales, avg(Case when commodity ="pasta sauce" then price else 0 end) pasta_sauce_price, sum(Case when commodity ="syrups" then dollar_sales else 0 end) syrups_sales, avg(Case when commodity ="syrups" then price else 0 end) syrups_price, sum(Case when commodity ="pasta" then coupon else 0 end) pasta_coupon, sum(Case when commodity ="pancake mixes" then coupon else 0 end) pancake_mixes_coupon, sum(Case when commodity ="pasta sauce" then coupon else 0 end) pasta_sauce_coupon, sum(Case when commodity ="syrups" then coupon else 0 end) syrups_coupon from data group by week ') agg_data=as.data.frame(agg_data) row=nrow(agg_data) agg_data$pasta_sales_lag[1]=0; agg_data$pasta_sales_lag[2:row]=agg_data$pasta_sales[1:row-1] colnames(agg_data) ##models for price elasticty and cross price ealsticity agg_data$pasta_sales_lag[1]=0; agg_data$pasta_sales_lag[2:row]=agg_data$pasta_sales[1:row-1] agg_data$pancake_mixes_sales_lag[1]=0; agg_data$pancake_mixes_sales_lag[2:row]=agg_data$pancake_mixes_sales[1:row-1] agg_data$pasta_sauce_sales_lag[1]=0; agg_data$pasta_sauce_sales_lag[2:row]=agg_data$pasta_sauce_sales[1:row-1] agg_data$syrups_sales_lag[1]=0; agg_data$syrups_sales_lag[2:row]=agg_data$syrups_sales[1:row-1] agg_data$pasta_coupon_lag[1]=0; agg_data$pasta_coupon_lag[2:row]=agg_data$pasta_coupon[1:row-1] agg_data$pancake_mixes_coupon_lag[1]=0; agg_data$pancake_mixes_coupon_lag[2:row]=agg_data$pancake_mixes_coupon[1:row-1] agg_data$pasta_sauce_coupon_lag[1]=0; agg_data$pasta_sauce_coupon_lag[2:row]=agg_data$pasta_sauce_coupon[1:row-1] agg_data$syrups_coupon_lag[1]=0; agg_data$syrups_coupon_lag[2:row]=agg_data$syrups_coupon[1:row-1] str(agg_data) model1=lm(log(pasta_sales)~week+log(pasta_price)+log(pasta_sauce_price)+log(pasta_sales_lag+1)+log(pasta_coupon+1), data=agg_data) summary(model1) model2=lm(log(pasta_sauce_sales)~week+log(pasta_sauce_price)+log(pasta_price)+log(pasta_sauce_sales_lag+1)+log(pasta_sauce_coupon+1), data=agg_data) summary(model2) model3=lm(log(pancake_mixes_sales)~log(pancake_mixes_price)+log(syrups_price)+log(pancake_mixes_sales_lag+1)+log(pancake_mixes_coupon+1), data=agg_data) summary(model3) model4=lm(log(syrups_sales)~log(pancake_mixes_price)+log(syrups_price)+log(syrups_sales_lag+1)+log(syrups_coupon+1), data=agg_data) summary(model4) ####### instrument variance library(systemfit) library(AER) model5=ivreg(log(pasta_sales)~log(pasta_price)+log(pasta_sauce_price)+ log(pasta_coupon+1)|log(pasta_sales_lag+1)+log(pasta_sauce_price)+ log(pasta_coupon+1), data=agg_data) summary(model5) model5=ivreg(log(pasta_sauce_sales)~log(pasta_sauce_price)+log(pasta_sauce_sauce_price)+log(pasta_sauce_coupon+1)|log(pasta_sauce_sauce_price)+log(pasta_sauce_sales_lag+1) +log(pasta_sauce_coupon+1), data=agg_data) summary(model5) library(dlpyr)

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/IDESSA/BushEncroachment/AerialImages_MODIS/ProcessAerialImages/unsupervised.R

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environmentalinformatics-marburg/magic

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refs/heads/master

2022-05-27T06:40:23.443801

2022-05-05T12:55:28

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unsupervised.R

################################################################################ ### UNSUPERVIDED CLASSIFICATION FOR SOUTH AFRICA AERIAL IMAGES # script calculates predictor variables from aerial images and applys a k-means # clustering algorithm ################################################################################ rm(list=ls()) library(Rsenal) library(rgdal) library(cluster) library(fuerHanna) # for fortran implementation of vvi, hsi, simpletexture require(vegan) ################################################################################ ### Do adjustments here ################################################################################ pathToSampleImages <- "/media/hanna/data/IDESSA_Bush/AERIALIMAGERY/samples/" tmpdir <- "/media/hanna/data/IDESSA_Bush/AERIALIMAGERY/samples/tmpdir" outdir <- "/media/hanna/data/IDESSA_Bush/AERIALIMAGERY/samples/clusterResults/" fname <- list.files(pathToSampleImages,pattern=".tif$")[2] # file to be clustered sizeOfImage <- 300 # in m method <- "elbow" # or"cascadeKM" method to determine optimal nr of clusters includeTexture <- FALSE applyShadowMask <- FALSE #might make more sense to let shadow be a separate class #if it can clearly be distinguished from other objects ################################################################################ ### Load and crop ################################################################################ setwd(pathToSampleImages) tmpdir <- paste0(tmpdir,"/001") dir.create(tmpdir) rasterOptions(tmpdir=tmpdir) rgb_img <- brick(fname) center <- c((extent(rgb_img)@xmin+extent(rgb_img)@xmax)/2, (extent(rgb_img)@ymin+extent(rgb_img)@ymax)/2) rgb_img <- crop(rgb_img,c(center[1]-sizeOfImage/2,center[1]+sizeOfImage/2, center[2]-sizeOfImage/2,center[2]+sizeOfImage/2)) names(rgb_img) <- c("R","G","B") ################################################################################ ### Calculate further variables ################################################################################ rgb_hsi <- hsi(red = raster(rgb_img, layer = 1), green = raster(rgb_img, layer = 2), blue = raster(rgb_img, layer = 3)) vvindex <- fuerHanna::vvi(red = raster(rgb_img, layer = 1), green = raster(rgb_img, layer = 2), blue = raster(rgb_img, layer = 3)) names(vvindex) <- "VVI" result <- brick(c(rgb_img,rgb_hsi,vvindex)) if(includeTexture){ txt <- simpletexture(result,3) result <- stack(result,txt) } result <- stack(result) ################################################################################ ### Shadow detection ################################################################################ ## shadow detection if(applyShadowMask){ shadow <- rgbShadowMask(rgb_img) # modal filter shadow <- focal(shadow, w = matrix(c(1, 1, 1, 1, 1, 1, 1, 1, 1), nc = 3), fun = modal, na.rm = TRUE, pad = TRUE) } ################################################################################ ### Determine Number of clusters ################################################################################ image.df <- as.data.frame(result) ############################# Elbow method if (method=="elbow"){ cluster.image<-list() pExp <- c() for (i in 1:10){ cluster.image[[i]] <- kmeans(na.omit(image.df), i, iter.max = 10, nstart = 25) pExp[i] = 1- cluster.image[[i]]$tot.withinss / cluster.image[[i]]$totss print (paste0(i," processed...")) } pExpFunc <-splinefun(1:10,pExp) optNrCluster <- min(which(round(pExpFunc(1:10,deriv=2),1)==0)) pdf(paste0(outdir,"/Nclust_elbow_",fname,".pdf")) plot(1:10, pExp, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") points(optNrCluster,pExp[optNrCluster],col="red",pch=16) dev.off() } ############################# cascadeKM method if (method=="cascadeKM"){ fit <- cascadeKM(na.omit(image.df), 2, 8, iter = 50) optNrCluster <- as.numeric(which.max(fit$results[2,])) } ################################################################################ ### Cluster image with optimal nr of clusters ################################################################################ cluster.image <- kmeans(na.omit(image.df), optNrCluster, iter.max = 50, nstart = 25) ### Create raster output from clusering image.df.factor <- rep(NA, length(image.df[,1])) image.df.factor[!is.na(image.df[,1])] <- cluster.image$cluster clustered <- raster(result) clustered <- setValues(clustered, image.df.factor) if(applyShadowMask){ clustered[shadow==0] <- NA } ################################################################################ ### Save clustered image and clean tmpdir ################################################################################ writeRaster(clustered,paste0(outdir,"/clustered_",fname,".tif"),overwrite=TRUE) unlink(tmpdir, recursive=TRUE)

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/Heri Folder/rscript_plots.R

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LizzieChung/Covid_19_Project

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2023-05-29T22:09:59.121164

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rscript_plots.R

library(tidycensus) library(tidyverse) library(tigris) library(lubridate) options(tigris_use_cache=TRUE) census_api_key("8ce6395e8ec025d0c06764b840e7e5fe70221625", overwrite = TRUE, install=TRUE) data("county_laea") #Dataset with county geometry for use when shifting Alaska and Hawaii class(county_laea) #fips_codes is a built-in daataset for smart state and county lookup. To access # directly use: #data(fips_codes) get_decennial() us_components <- get_estimates(geography = "state", product = "components") unique(us_components$variable) us_pop <- get_estimates(geography = "county", product = "population", year = 2019, geometry = TRUE, resolution = "20m") %>% shift_geometry() unique(us_pop$variable) us_pop <- us_pop %>% filter(variable == "POP") order = c("10,000,000 +", "1,000,000 to 10,000,000", "100,000 to 1,000,000", "10,000 to 100,000", "10,000 and below") us_pop <- us_pop %>% mutate(groups = case_when( value > 10000000 ~ "10,000,000 +", value > 1000000 ~ "1,000,000 to 10,000,000", value > 100000 ~ "100,000 to 1,000,000", value > 10000 ~ "10,000 to 100,000", value > 0 ~ "10,000 and below" )) %>% mutate(groups = factor(groups, levels = order)) state_overlay <- states( cb = TRUE, resolution = "20m" ) %>% filter(GEOID != "72") %>% shift_geometry() ggplot() + geom_sf(data = us_pop, aes(fill = groups, color = groups), size = 0.1) + geom_sf(data = state_overlay, fill = NA, color = "black", size = 0.1) + scale_fill_brewer(palette = "PuOr", direction = -1) + scale_color_brewer(palette = "PuOr", direction = -1, guide = FALSE) + coord_sf(datum = NA) + theme_minimal(base_family = "Roboto") + labs(title = "US Population", subtitle = "US Census Bureau 2019 Population Estimates", fill = "Population Grouping" ) # Attempt at Making a plot for cases per day covid.dt <- read_csv("C:/Users/hlop5/Downloads/owid-covid-data.csv") head(covid.dt) dim(covid.dt) str(covid.dt) names(covid.dt) # selecting our varibales of interest asia.covid.df <- covid.dt %>% select(iso_code, continent, location, date, new_deaths, total_deaths, new_cases, total_cases, icu_patients, hosp_patients, new_tests, total_tests, new_vaccinations, total_vaccinations, population) # Editing the format of the data variable asia.covid.df$date = as.Date(covid.dt$date, format = "%Y-%m-%d") # Filtering out the continent of Asia asia.covid.df <- covid.dt %>% filter(continent == "Asia") #Creating a month variable asia.covid.df <- asia.covid.df %>% mutate( month = month(date, label = T), wday = wday(date) ) #Creating a csv file to use for the shiny web app write.csv(asia.covid.df, "~/HBSP/Covid_19_Project/Heri Folder/asia.covid.df", row.names = FALSE) #variable.order = c("hosp_patients", "icu_patients", #"new_tests","new_vaccinations", #"new_cases","new_deaths") #unique(asia.covid.df$location) #function(location){ # } #CleanCOVIDdataUS = # subset(COVIDdataUS, select = c(date, population, # total_cases, people_vaccinated, # total_deaths)) #asia.covid.df %>% # filter(!is.na(total_cases) | !is.na(population) | is.na(total_vaccinations)) #CleanCOVIDdataUS[is.na(CleanCOVIDdataUS)] = 0 #CleanCOVIDdataUS$date = as.Date(CleanCOVIDdataUS$date, format = "%Y-%m-%d") #USCOVID_Plot = ggplot(data = CleanCOVIDdataUS) + # geom_line(mapping = aes(x = date, y = total_cases, color = "Total Cases")) + # geom_line(mapping = aes(x = date, y = population, color = "Population")) + # geom_line(mapping = aes(x = date, y = people_vaccinated, color = "Total Vaccinations")) + # geom_line(mapping = aes(x = date, y = total_deaths, color = "Total Deaths")) + # xlab("Date") + # ylab("Number of People") + # ggtitle("COVID Pandemic in the US") + theme_bw() + labs(color = "") + theme(legend.position = "top") #USCOVID_Plot #covid.mn.tbl %>% # pivot_longer(c(pcr.cases:hosp.cases, deaths)) %>% # mutate(name = factor(name, levels=variable.order)) %>% # ggplot(aes(x=date, y=value, color=month))+ # geom_point()+ # labs(x="Date", y="Logarithmic scale")+ # facet_grid(.~name)+ # scale_y_log10()

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/2. Модуль 2/2.3. Работа с Яндекс Директ API/attributions_report.R

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selesnow/r_for_marketing

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attributions_report.R

# отчёт по конверсиям с моделью аттрибуции за статичный период attribution_report <- yadirGetReport(DateFrom = "2018-11-15", DateTo = "2018-11-20", FieldNames = c("Date", "Conversions"), Goals = c(27475434, 38234732), AttributionModels = c("LC", "LSC", "FC"), Login = "irina.netpeak", TokenPath = "C:\\r_for_marketing_course\\tokens")

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/plot3.R

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hestiles/ExData_Project2

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refs/heads/master

2020-05-07T10:37:48.116799

2019-04-10T17:57:55

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plot3.R

download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", destfile="emissions.zip") unzip("emissions.zip") NEI<-readRDS("summarySCC_PM25.rds") SCC<-readRDS("Source_Classification_Code.r") library(dplyr) ##Filter to Baltimore Bal<- filter(NEI, fips=="24510") ##sum by year & type Bal3<-aggregate(Emissions~year+type,Bal, sum) library(ggplot2) ##Create PNG file png(file="plot3.png", width=480, height=480) qplot(year, Emissions, data=Bal3, facets=.~type)+geom_smooth() dev.off()

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/Main Simulation/2DataGeneration.R

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AndresFPA/DependSim

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refs/heads/main

2023-06-01T22:54:50.019409

2021-06-07T11:41:20

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2DataGeneration.R

#Comparison of dependence tests # Variable x is randomly generated # Variable y is generated based on one type of relationship with y (linear, quedratic, etc.) # noise is the error introduced while creating the relationship (high, medium, low) # samp is the sample size (10, 20, 35, 50, 75, 100, 150, 500) DataGeneration <- function(samp, noise, relationship){ x <- rnorm(samp) if(relationship == "linear"){ y <- linear(x, noise) } else if(relationship == "quadratic"){ y <- quadratic(x, noise) } else if(relationship == "exponential"){ y <- exponential(x, noise) } else if(relationship == "sinwave"){ y <- sinwave(x, noise) } else if(relationship == "cross"){ y <- cross(x, noise) } else if(relationship == "no_relationship"){ y <- no_relationship(x, noise) } XY <- cbind(x, y) dat <- data.frame(XY) return(dat) }

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/Advanced Programming in R /Lab Bonus/awesomebonus/R/airport_delays.R

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aydinardalan/MSc_Statistics-and-Machine-Learning

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refs/heads/master

2023-03-17T04:01:48.076902

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airport_delays.R

#' Function for generating plot of mean arrival delays at different airports. #' #' \code{visualize_airport_delays} generates a map plot with color scale for mean arrival delay at different airports.Dataset used is the nycflights13. #' #' #' @return \code{visualize_airport_delays} returns a plot. #' #' @examples #' visualize_airport_delays() #' #' @references \url{https://www.rdocumentation.org/packages/nycflights13/versions/1.0.1} #' #' @importFrom stats na.omit #' @importFrom rlang .data #' @import maps #' @import ggplot2 #' @import dplyr #' #' @export #' visualize_airport_delays <- function(){ flights_data <- na.omit(nycflights13::flights) airport_data <- na.omit(nycflights13::airports) # Join data sets flights_data <- dplyr::rename(flights_data, "faa" = "dest") joined_data <- inner_join(flights_data, airport_data, by="faa") # Discard data mismatches using inner join # Create dataframe for plotting (different structure) plot_data <- data.frame(unique(joined_data[c('faa', 'name', 'lat', 'lon')])) # Group by airports, then get mean for each category of airport over arrival delay #calculated_mean <- joined_data %>% group_by(faa) %>% summarise(mean_delay = mean(arr_delay)) grouped_data <- group_by(joined_data, .data$faa) calculated_mean <- summarise(grouped_data, mean_delay = mean(.data$arr_delay)) # Join mean to plot data. plot_data <- left_join(plot_data, calculated_mean, by = "faa") # Create map and plot using mean value as color scale used_map <- map_data("usa") ggplot() + geom_polygon(data = used_map, aes_string(x="long", y = "lat", group = "group"), fill = "#636e72") + coord_fixed(1.3) + geom_point(data = plot_data, aes_string(x = "lon", y = "lat", color = "mean_delay"), size = 5) + scale_colour_viridis_c() }

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/plot3.R

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tlaytongoogle/ExData_Plotting1

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2021-01-21T09:47:06.457037

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plot3.R

# This script must be run in a working directory which contains the electric power consumption dataset # from the UC Irvine Machine Learning Repository, named "exdata-data-household_power_consumption.zip" unzip("exdata-data-household_power_consumption.zip", "household_power_consumption.txt") data <- read.csv( "household_power_consumption.txt", sep = ";", colClasses = c(NA, NA, "NULL", "NULL", "NULL", "NULL", NA, NA, NA), na.strings = c("?")) data$Date <- as.Date(data$Date, format = "%d/%m/%Y") data <- subset(data, Date %in% as.Date(c("2007-02-01", "2007-02-02"))) datetime <- strptime(paste(data$Date, data$Time), "%Y-%m-%d %H:%M:%S") png(file = "plot3.png") with(data, { plot( datetime, Sub_metering_1, type = "l", col = "black", main = NA, xlab = NA, ylab = "Energy sub metering") lines( datetime, Sub_metering_2, col = "red") lines( datetime, Sub_metering_3, col = "blue") legend( "topright", legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty = c(1, 1, 1), col = c("black", "red", "blue")) }) dev.off()

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/R/pb.bay.R

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cran/altmeta

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refs/heads/master

2022-09-18T11:48:51.398907

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pb.bay.R

pb.bay <- function(y, s2 = NA, sig.level = 0.1, n00, n01, n10, n11, het = "mul", sd.prior = "unif", n.adapt = 1000, n.chains = 3, n.burnin = 5000, n.iter = 10000, thin = 2, upp.het = 2, phi = 0.5, coda = FALSE, traceplot = FALSE){ out <- NULL if(is.element("mul", het)){ if(is.element("unif", sd.prior)){ modelstring <- function(o){ out <- " model{ for(i in 1:N){ y[i] ~ dnorm(alpha + beta*se[i], prec[i]) prec[i] <- pow(kappa*se[i], -2) se[i] <- sqrt(1/(n0[i]*p01[i]*(1-p01[i])) + 1/(n1[i]*p11[i]*(1-p11[i]))) n01[i] ~ dbin(p01[i], n0[i]) n11[i] ~ dbin(p11[i], n1[i]) logit(p01[i]) <- mu[i] logit(p11[i]) <- mu[i] + delta[i] delta[i] ~ dnorm(d, inv.tau2) mu[i] ~ dnorm(0, 0.0001) } alpha ~ dnorm(0, 0.0001) beta ~ dnorm(0, 0.0001) kappa ~ dunif(0, upp.kappa) d ~ dnorm(0, 0.0001) inv.tau2 <- pow(tau, -2) tau ~ dunif(0, upp.tau) }" return(out) } jags.dat <- list(N = length(y), y = y, n0 = n00 + n01, n1 = n10 + n11, n01 = n01, n11 = n11, upp.kappa = upp.het, upp.tau = upp.het) inits <- NULL rng.seeds <- sample(1000000, n.chains) for(i in 1:n.chains){ inits[[i]] <- list(alpha = rnorm(1), beta = rnorm(1), d = rnorm(1), kappa = runif(1, 0.1, 1.9), tau = runif(1, 0.1, 1.9), .RNG.name = "base::Wichmann-Hill", .RNG.seed = rng.seeds[i]) } } if(is.element("hn", sd.prior)){ modelstring <- function(o){ out <- " model{ for(i in 1:N){ y[i] ~ dnorm(alpha + beta*se[i], prec[i]) prec[i] <- pow(kappa*se[i], -2) se[i] <- sqrt(1/(n0[i]*p01[i]*(1-p01[i])) + 1/(n1[i]*p11[i]*(1-p11[i]))) n01[i] ~ dbin(p01[i], n0[i]) n11[i] ~ dbin(p11[i], n1[i]) logit(p01[i]) <- mu[i] logit(p11[i]) <- mu[i] + delta[i] delta[i] ~ dnorm(d, inv.tau2) mu[i] ~ dnorm(0, 0.0001) } alpha ~ dnorm(0, 0.0001) beta ~ dnorm(0, 0.0001) kappa ~ dnorm(0, inv.phi2)I(0,) d ~ dnorm(0, 0.0001) inv.tau2 <- pow(tau, -2) tau ~ dnorm(0, inv.phi2)I(0,) }" return(out) } jags.dat <- list(N = length(y), y = y, n0 = n00 + n01, n1 = n10 + n11, n01 = n01, n11 = n11, inv.phi2 = phi^(-2)) inits <- NULL rng.seeds <- sample(1000000, n.chains) for(i in 1:n.chains){ inits[[i]] <- list(alpha = rnorm(1), beta = rnorm(1), d = rnorm(1), kappa = runif(1, 0.1, 1.9), tau = runif(1, 0.1, 1.9), .RNG.name = "base::Wichmann-Hill", .RNG.seed = rng.seeds[i]) } } } if(is.element("add", het)){ if(is.element("unif", sd.prior)){ modelstring <- function(o){ out <- " model{ for(i in 1:N){ y[i] ~ dnorm(alpha + beta*se[i], prec[i]) prec[i] <- 1/(pow(gamma, 2) + pow(se[i], 2)) se[i] <- sqrt(1/(n0[i]*p01[i]*(1-p01[i])) + 1/(n1[i]*p11[i]*(1-p11[i]))) n01[i] ~ dbin(p01[i], n0[i]) n11[i] ~ dbin(p11[i], n1[i]) logit(p01[i]) <- mu[i] logit(p11[i]) <- mu[i] + delta[i] delta[i] ~ dnorm(d, inv.tau2) mu[i] ~ dnorm(0, 0.0001) } alpha ~ dnorm(0, 0.0001) beta ~ dnorm(0, 0.0001) gamma ~ dunif(0, upp.gamma) d ~ dnorm(0, 0.0001) inv.tau2 <- pow(tau, -2) tau ~ dunif(0, upp.tau) }" return(out) } jags.dat <- list(N = length(y), y = y, n0 = n00 + n01, n1 = n10 + n11, n01 = n01, n11 = n11, upp.gamma = upp.het, upp.tau = upp.het) inits <- NULL rng.seeds <- sample(1000000, n.chains) for(i in 1:n.chains){ inits[[i]] <- list(alpha = rnorm(1), beta = rnorm(1), d = rnorm(1), gamma = runif(1, 0.1, 1.9), tau = runif(1, 0.1, 1.9), .RNG.name = "base::Wichmann-Hill", .RNG.seed = rng.seeds[i]) } } if(is.element("hn", sd.prior)){ modelstring <- function(o){ out <- " model{ for(i in 1:N){ y[i] ~ dnorm(alpha + beta*se[i], prec[i]) prec[i] <- 1/(pow(gamma, 2) + pow(se[i], 2)) se[i] <- sqrt(1/(n0[i]*p01[i]*(1-p01[i])) + 1/(n1[i]*p11[i]*(1-p11[i]))) n01[i] ~ dbin(p01[i], n0[i]) n11[i] ~ dbin(p11[i], n1[i]) logit(p01[i]) <- mu[i] logit(p11[i]) <- mu[i] + delta[i] delta[i] ~ dnorm(d, inv.tau2) mu[i] ~ dnorm(0, 0.0001) } alpha ~ dnorm(0, 0.0001) beta ~ dnorm(0, 0.0001) gamma ~ dnorm(0, inv.phi2)I(0,) d ~ dnorm(0, 0.0001) inv.tau2 <- pow(tau, -2) tau ~ dnorm(0, inv.phi2)I(0,) }" return(out) } jags.dat <- list(N = length(y), y = y, n0 = n00 + n01, n1 = n10 + n11, n01 = n01, n11 = n11, inv.phi2 = phi^(-2)) inits <- NULL rng.seeds <- sample(1000000, n.chains) for(i in 1:n.chains){ inits[[i]] <- list(alpha = rnorm(1), beta = rnorm(1), d = rnorm(1), gamma = runif(1, 0.1, 1.9), tau = runif(1, 0.1, 1.9), .RNG.name = "base::Wichmann-Hill", .RNG.seed = rng.seeds[i]) } } } jags.m <- jags.model(file = textConnection(modelstring()), data = jags.dat, inits = inits, n.chains = n.chains, n.adapt = n.adapt) update(jags.m, n.iter = n.burnin) params <- c("beta") samps <- coda.samples(model = jags.m, variable.names = params, n.iter = n.iter, thin = thin) quants <- quantile(unlist(samps), probs = c(sig.level/2, 1 - sig.level/2, 0.5)) if(traceplot){ par(mfrow = c(n.chains,1)) for(i in 1:n.chains){ temp <- as.vector(samps[[i]]) tp <- plot(temp, type = "l", col = "red", ylab = paste("beta[", i, "]", sep = ""), xlab = "Iterations", main = paste("Chain",i)) } print(tp) } out$est.bay <- as.numeric(quants[3]) out$ci.bay <- quants[c(1, 2)] temp <- name <- NULL if(coda){ for(i in 1:n.chains){ temp <- cbind(temp, as.vector(samps[[i]])) name <- c(name, paste("beta[", i, "]", sep = "")) } beta <- data.frame(temp) names(beta) <- name out$samps.bay <- beta } return(out) }

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# bibliotecas library(tidyverse) library(rio) library(DBI) library(bigrquery) library(reshape2) # diretorio setwd("~/Documentos/bdmais") # descobrindo onde as bases são iguais nomedasvar <- function(ano){ colnames(import(paste0("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_",ano,".csv"), setclass = "tbl")) } lista_var1 <- map(2015:2019, nomedasvar) # mesmas variaveis MAS 2018 tem uma situzinha # vetor de nomes vetor_nomes <- c("prop_aprovados_anos_inciais_ef","prop_reprovados_anos_iniciais_ef", "prop_abandono_anos_iniciais_ef", "prop_aprovados_anos_finais_ef","prop_reprovados_anos_finais_ef", "prop_abandono_anos_finais_ef", "prop_aprovados_em", "prop_reprovados_em", "prop_abandono_em") ## importando dados do fluxo escolar # abre de 15,16,17,19 abre_15_19 <- function(ano){ read.csv2(paste0("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_",ano,".csv"), colClasses = rep("character", 13), sep = ",")%>% set_names(c("ano", "diretoria", "municipio", "codigo_rede_ensino", vetor_nomes)) } list15_19 <- map(c(2015,2016,2017,2019) , abre_15_19) bases15_19 <- reduce(list15_19, bind_rows) # caso de 2018 base18<- read.csv2("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_2018.csv", colClasses = rep("character", 13))%>% set_names(c("ano", "diretoria", "municipio", "codigo_rede_ensino", vetor_nomes)) # caso de 2014 base14 <- read.csv2("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_2014.csv", sep = ",", colClasses = rep("character", 13))%>% select(-CODMUN)%>% set_names("ano", "codigo_rede_ensino", "municipio", vetor_nomes) # caso de 2013 base13 <- read.csv2("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_2013.csv", sep = ",", colClasses = rep("character", 13))%>% set_names("ano", "municipio", "diretoria", "codigo_rede_ensino", vetor_nomes) # caso de 2012 base12 <- read.csv2("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_2012.csv", sep = ",", colClasses = rep("character", 13))%>% select(-CODMUN, -MUN)%>% set_names( "municipio", "codigo_rede_ensino", "rede_ensino", vetor_nomes)%>% mutate(ano = "2012", across(everything(), ~recode(.x, "NULL" = NA_character_))) # caso de 2011 base11 <- read.csv2("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_2011.csv", sep = ",", colClasses = rep("character", 13))%>% select(-starts_with("TOT"), -ends_with('A'))%>% set_names("ano", "municipio", vetor_nomes)%>% mutate( across(everything(), ~recode(.x, "NULL" = NA_character_))) # pondo numa lista lista <- list(base11,base12,base13,base14,base18, bases15_19) basefinal <- reduce(lista, bind_rows) # pegando os nomes dos municípios bq_auth(path = "chavebigquery/My Project 55562-2deeaad41d85.json") id_projeto <- "double-voice-305816" con <- dbConnect( bigrquery::bigquery(), billing = id_projeto, project = "basedosdados" ) query <- 'SELECT id_municipio, municipio, sigla_uf FROM `basedosdados.br_bd_diretorios_brasil.municipio`' diretorio_com_id <- dbGetQuery(con, query) # mudando o nome do municipio para se encaixar diretorio_com_id_alt <- diretorio_com_id%>% mutate(municipio = str_to_upper(municipio))%>% mutate(municipio = str_replace_all(municipio, "Á", "A" ))%>% mutate(municipio = str_replace_all(municipio, "É", "E" ))%>% mutate(municipio = str_replace_all(municipio, "Í", "I" ))%>% mutate(municipio = str_replace_all(municipio, "Ó", "O" ))%>% mutate(municipio = str_replace_all(municipio, "Ú", "U" ))%>% mutate(municipio = str_replace_all(municipio, "Â", "A" ))%>% mutate(municipio = str_replace_all(municipio, "Ê", "E" ))%>% mutate(municipio = str_replace_all(municipio, "Î", "I" ))%>% mutate(municipio = str_replace_all(municipio, "Ô", "O" ))%>% mutate(municipio = str_replace_all(municipio, "Û", "U" ))%>% mutate(municipio = str_replace_all(municipio, "Ã", "A" ))%>% mutate(municipio = str_replace_all(municipio, "Õ", "O" ))%>% mutate(municipio = str_replace_all(municipio, "Ç", "C" ))%>% mutate(chave = str_c(sigla_uf,municipio))%>% select(chave, id_municipio) # juntando com a base final basefinal_comid <- basefinal%>% mutate(sigla_uf = "SP", chave = str_c(sigla_uf,municipio), rede = case_when(rede_ensino == "ESTADUAL" ~ "estadual"),)%>% left_join(diretorio_com_id_alt, by = 'chave')%>% select(-chave, -municipio, -rede_ensino, -codigo_rede_ensino) # exportando a base export(basefinal_comid, "bases_prontas/fluxoescolarmun.csv", na = "", quote = TRUE)

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#' @title carbonise #' @description Calculate the carbon storage (above ground biomass and soil carbon) and sequestration for UK priority habitats. #' @param x habitats sf dataframe #' @param habitats name of the column containing priority habitats types #' @return sf features with stored above ground carbon and sequestered carbon per year for each feature #' @details The function calculates the amount of above ground carbon stored in each priority habitat type using data taken from two papers (see main package description for references). #' It also calculates stored soil carbon using data from the Global Soil Organic Carbon map. The soil carbon data only covers the UK. You need to supply the name of the field with the UK priority habitat descriptions in. #' The function can be slow if you have a lot of habitat features, as it summarises soil carbon data from a large raster. #' The input dataset needs to be an sf features dataset. #' The CRS for this package is EPSG:27700. You may need to transform your data for the function to work. #' @examples #' # NOT RUN #' # c <- carbonise(x, habitats = "S41Habitat") #' @seealso #' \code{\link[dplyr]{mutate_all}},\code{\link[dplyr]{mutate}},\code{\link[dplyr]{mutate-joins}} #' \code{\link[sf]{geos_measures}} #' @rdname carbonise #' @export #' @importFrom dplyr mutate left_join across #' @importFrom sf st_area #' @importFrom exactextractr exact_extract carbonise <- function(x, habitats){ # convert factors to character for join x <- x %>% dplyr::mutate(dplyr::across(where(is.factor), as.character)) # calculate feature areas in hectares x <- x %>% dplyr::mutate(Area = as.numeric(sf::st_area(x) / 10000)) # join carbon data to habitats data # cx <- dplyr::left_join(x, carbon, by = c(habitats = "S41Habitat")) cx <- merge(x, carbon, by.x = habitats, by.y = "S41Habitat") # calculate stored C and sequestered C per feature cx$storedC <- as.numeric(cx$Area * cx$AGB) cx$seqC <- as.numeric(cx$Area * cx$Cseq) # calculate soil carbon per feature cx$soilC <- exactextractr::exact_extract(soilcarbon, cx, "mean") # calculate total C cx$totalC <- cx$storedC * cx$soilC return(cx) }

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#Installing required packages install.packages(c("dplyr", "ggplot2", "forecast", "lubridate", "ROCR" , "rjson", "timeSeries", "xts", "highfrequency" , "manipulate", "tseries", "bsts", "boot", "mixtools")) # Loading Required Packages library(dplyr) library(ggplot2) library(forecast) library(lubridate) library(ROCR) library(rjson) library(timeSeries) library(xts) library(highfrequency) library(manipulate) library(tseries) library(bsts) library(boot) library(mixtools) # Setting work directory setwd("C:/Users/hosse/Desktop/ML/NormalDistSimulation") paste("Work Directory is Set Up to", getwd()) # Set seed value set.seed(20) ####################################################### ####### Bivariate Normal Distribution Simulation ###### ####################################################### # Number of random samples N <- 200 # Normal Distribution Parameter Setting rho <- -0.6 mu1 <- 1; s1 <- 2 mu2 <- 1; s2 <- 8 Mu <- c(mu1, mu2) Sigmas <- matrix(c(s1^2, s1*s2*rho, s2*s1*rho, s2^2), 2) # Function to draw ellipse for bivariate normal data ellipse_bvn <- function(bvn, alpha, col){ Xbar <- apply(bvn,2,mean) S <- cov(bvn) ellipse(Xbar, S, alpha = alpha, col=col, npoints = 250, newplot = FALSE, draw = TRUE) } # Sampling data for Simulation gibbs <- function (n, mu1, s1, mu2, s2, rho) { mat <- matrix(ncol = 2, nrow = n) x <- 0 y <- 0 mat[1, ] <- c(x, y) for (i in 2:n) { x <- rnorm(1, mu1+(s1/s2)*rho*(y - mu2), sqrt((1 - rho^2)*s1^2)) y <- rnorm(1, mu2+(s2/s1)*rho*(x - mu1), sqrt((1 - rho^2)*s2^2)) mat[i, ] <- c(x, y) } mat } bvn <- gibbs(N,mu1,s1,mu2,s2,rho) colnames(bvn) <- c("X1","X2") # Plotting Results plot(bvn,xlab="X1", ylab="X2", main = " Bivariate Normal Distribution Simulation Plot " , col="black") ellipse_bvn(bvn, 0.5, col = "red") ellipse_bvn(bvn, 0.05, col = "blue") ########################################################### ####### Multi-variate Normal Distribution Simulation ###### ########################################################### install.packages("mvtnorm") library(mvtnorm) # Function to claculate mean and cov of a given data sampleSize <- 1000 nbrVars <- 3 data_mat <- function (nbrVars, sampleSize, LB, UB) { data <- matrix(ncol = nbrVars, nrow = sampleSize) mean_mat <- matrix(ncol = nbrVars, nrow = 1) covar_mat <- matrix(ncol = nbrVars, nrow = nbrVars) sigma_mat <- matrix(ncol = nbrVars, nrow = 1) for(i in 1:nbrVars){ data[, i] <- sample(LB:UB, sampleSize, replace = TRUE) mean_mat[, i] <- mean(data[, i]) sigma_mat[, i] <- var(data[, i]) } covar_mat <- cov(data, method = "pearson") output <- list("Data" = data, "Mean Matrix" = mean_mat , "Covariance Matrix" = covar_mat, "Variance" = sigma_mat) return(output) } # Varibale to hold the simulation results mvn <- rmvnorm(n = 1000, unlist(as.list(data_mat(nbrVars, sampleSize, 1, 10)[2]))) for(i in 1:(ncol(mvn)-1)){ for(j in 1:ncol(mvn)){ if(j > i){ plot_main <- paste("Var", i, " and Var ", j, " Scatter Plot", sep="") plot(x = mvn[, i], y = mvn[, j], xlab= paste("X", i), ylab= paste("X", j) , main = plot_main , col= i*j) } } }

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# Plot 4. Combination of 4 plots - Variables / Time #read data stored in working directory data<-read.table('household_power_consumption.txt', header = TRUE, sep=";", na.strings="?", col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), colClasses = c( "character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")) #convert Date and Time variables to date and time classes data$Time <- strptime(paste(data$Date,data$Time), '%d/%m/%Y %H:%M:%S') data$Date <- as.Date(data$Date, '%d/%m/%Y') #subset data to include the spesific dates only subdata<-subset(data, Date == '2007-02-01' | Date == '2007-02-02') #open png device png('plot4.png',width = 480, height = 480) #set parameters. 2 rows and 2 columns par(mfrow = c(2, 2)) # 4.1. Global Active Power / Time #create plot / use line type / add x and y labels with(subdata,{ plot(Time,Global_active_power, type = 'l', xlab = '',ylab = 'Global Active Power') }) # 4.2. Voltage / Time #create plot / use line type / add x and y labels with(subdata,{ plot(Time,Voltage, type = 'l', xlab = 'datetime',ylab = 'Voltage') }) # 4.3. Energy Sub Metering / Time #create empty plot with type 'n' / replace x and y label #add Sub Metering 1, 2, and 3 line points in the plot with different color #add a legend with the respective plot data with(subdata,{ plot(Time,Sub_metering_1,type = 'n',xlab = '',ylab = 'Energy sub metering') points(Time,Sub_metering_1,type = 'l') points(Time,Sub_metering_2,type = 'l',col='red') points(Time,Sub_metering_3,type = 'l',col='blue') legend('topright', col=c('black','red','blue'), lty = c(1,1,1), legend=c('Sub_metering_1','Sub_metering_2','Sub_metering_3')) }) # 4.4. Global Reactive Power / Time #create plot / use line type / add x and y labels with(subdata,{ plot(Time,Global_reactive_power, type = 'l', xlab = 'datetime',ylab = 'Global_reactive_power') }) #close device dev.off() #the file is created in working directory

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serve.R

#' Live preview a site #' #' The function \code{serve_site()} executes the server command of a static site #' generator (e.g., \command{hugo server} or \command{jekyll server}) to start a #' local web server, which watches for changes in the site, rebuilds the site if #' necessary, and refreshes the web page automatically; \code{stop_server()} #' stops the web server. #' #' By default, the server also watches for changes in R Markdown files, and #' recompile them automatically if they are modified. This means they will be #' automatically recompiled once you save them. If you do not like this #' behavior, you may set \code{options(blogdown.knit.on_save = FALSE)} (ideally #' in your \file{.Rprofile}). When this feature is disabled, you will have to #' manually compile Rmd documents, e.g., by clicking the Knit button in RStudio. #' #' The site generator is defined by the global R option #' \code{blogdown.generator}, with the default being \code{'hugo'}. You may use #' other site generators including \code{jekyll} and \code{hexo}, e.g., #' \code{options(blogdown.generator = 'jekyll')}. You can define command-line #' arguments to be passed to the server of the site generator via the global R #' option \code{blogdown.X.server}, where \code{X} is \code{hugo}, #' \code{jekyll}, or \code{hexo}. The default for Hugo is #' \code{options(blogdown.hugo.server = c('-D', '-F', '--navigateToChanged'))} #' (see the documentation of Hugo server at #' \url{https://gohugo.io/commands/hugo_server/} for the meaning of these #' arguments). #' @param ... Arguments passed to \code{servr::\link{server_config}()} (only #' arguments \code{host}, \code{port}, \code{browser}, \code{daemon}, and #' \code{interval} are supported). #' @param .site_dir Directory to search for site configuration file. It defaults #' to \code{getwd()}, and can also be specified via the global option #' \code{blogdown.site_root}. #' @note For the Hugo server, the argument \command{--navigateToChanged} is used #' by default, which means when you edit and save a source file, Hugo will #' automatically navigate the web browser to the page corresponding to this #' source file (if the page exists). However, due to a Hugo bug #' (\url{https://github.com/gohugoio/hugo/issues/3811}), this automatic #' navigation may not always work for R Markdown posts, and you may have to #' manually refresh your browser. It should work reliably for pure Markdown #' posts, though. #' @export serve_site = function(..., .site_dir = NULL) { serve = switch( generator(), hugo = serve_it(), jekyll = serve_it( baseurl = get_config2('baseurl', ''), pdir = get_config2('destination', '_site') ), hexo = serve_it( baseurl = get_config2('root', ''), pdir = get_config2('public_dir', 'public') ), stop("Cannot recognize the site (only Hugo, Jekyll, and Hexo are supported)") ) serve(..., .site_dir = .site_dir) } server_ready = function(url) { # for some reason, R cannot read localhost, but 127.0.0.1 works url = sub('^http://localhost:', 'http://127.0.0.1:', url) !inherits( xfun::try_silent(suppressWarnings(readLines(url))), 'try-error' ) } # this function is primarily for users who click the Knit button in RStudio (the # main purposes are to suppress a message that is not useful to Knit button # users, and avoid rebuilding Rmd files because Knit button has done the job); # normally you wouldn't need to call it by yourself preview_site = function(..., startup = FALSE) { # when startup = FALSE, set knitting = TRUE permanently for this R session, so # that build_site() in serve_site() no longer automatically rebuilds Rmds on # save by default, and an Rmd has to be manually knitted if (startup) { opts$set(preview = TRUE) on.exit(opts$set(preview = NULL), add = TRUE) # open some files initially if specified init_files = get_option('blogdown.initial_files') if (is.function(init_files)) init_files = init_files() for (f in init_files) if (file_exists(f)) open_file(f) } else { opts$set(knitting = TRUE) on.exit(refresh_viewer(), add = TRUE) } invisible(serve_site(...)) } preview_mode = function() { isTRUE(opts$get('preview')) || isTRUE(opts$get('knitting')) } serve_it = function(pdir = publish_dir(), baseurl = site_base_dir()) { g = generator(); config = config_files(g) function(..., .site_dir = NULL) { root = site_root(config, .site_dir) if (root %in% opts$get('served_dirs')) { if (preview_mode()) return() servr::browse_last() return(message( 'The site has been served under the directory "', root, '". I have tried ', 'to reopen it for you with servr::browse_last(). If you do want to ', 'start a new server, you may stop existing servers with ', 'blogdown::stop_server(), or restart R. Normally you should not need to ', 'serve the same site multiple times in the same R session', if (is_rstudio()) c( ', otherwise you may run into issues like ', 'https://github.com/rstudio/blogdown/issues/404' ), '.' )) } owd = setwd(root); on.exit(setwd(owd), add = TRUE) server = servr::server_config(..., baseurl = baseurl, hosturl = function(host) { if (g == 'hugo' && host == '127.0.0.1') 'localhost' else host }) # launch the hugo/jekyll/hexo server cmd = if (g == 'hugo') find_hugo() else g host = server$host; port = server$port; intv = server$interval if (!servr:::port_available(port, host)) stop( 'The port ', port, ' at ', host, ' is unavailable', call. = FALSE ) args_fun = match.fun(paste0(g, '_server_args')) cmd_args = args_fun(host, port) if (g == 'hugo') { # RStudio Server uses a proxy like http://localhost:8787/p/56a946ed/ for # http://localhost:4321, so we must use relativeURLs = TRUE: # https://github.com/rstudio/blogdown/issues/124 tweak_hugo_env(server = TRUE, relativeURLs = if (is_rstudio_server()) TRUE) if (length(list_rmds(pattern = bundle_regex('.R(md|markdown)$')))) create_shortcode('postref.html', 'blogdown/postref') } # run a function (if configured) before starting the server if (is.function(serve_first <- getOption('blogdown.server.first'))) serve_first() # call jekyll directly or use the bundler gem if (g == 'jekyll' && getOption('blogdown.jekyll.bundler', FALSE)) { cmd = 'bundle'; cmd_args = c('exec', g, cmd_args) } # if requested not to demonize the server, run it in the foreground process, # which will block the R session if (!server$daemon) return(system2(cmd, cmd_args)) verbose = get_option('blogdown.server.verbose', FALSE) pid = if (is_psx <- getOption('blogdown.use.processx', xfun::loadable('processx'))) { proc = processx::process$new( cmd, cmd_args, cleanup_tree = TRUE, stdout = if (verbose && processx::is_valid_fd(1L)) '', stderr = if (verbose && processx::is_valid_fd(2L)) '' else '|' ) I(proc$get_pid()) } else { xfun::bg_process(cmd, cmd_args, verbose) } opts$append(pids = list(pid)) message( 'Launching the server via the command:\n ', paste(c(cmd, cmd_args), collapse = ' ') ) i = 0 repeat { Sys.sleep(1) # for a process started with processx, check if it has died with an error if (is_psx && !proc$is_alive()) { err = tryCatch( paste(gsub('^Error: ', '', proc$read_error()), collapse = '\n'), error = function(e) '' ) stop(if (err == '') { 'Failed to serve the site; see if blogdown::build_site() gives more info.' } else err, call. = FALSE) } if (server_ready(server$url)) break if (i >= get_option('blogdown.server.timeout', 30)) { s = proc_kill(pid) # if s == 0, the server must have been started successfully stop(if (s == 0) c( 'Failed to launch the site preview in ', i, ' seconds. Try to give ', 'it more time via the global option "blogdown.server.timeout", e.g., ', 'options(blogdown.server.timeout = 600).' ) else c( 'It took more than ', i, ' seconds to launch the server. An error might ', 'have occurred with ', g, '. You may run blogdown::build_site() and see ', 'if it gives more info.' ), call. = FALSE) } i = i + 1 } server$browse() # server is correctly started so we record the directory served opts$append(served_dirs = root) Sys.setenv(BLOGDOWN_SERVING_DIR = root) message( 'Launched the ', g, ' server in the background (process ID: ', pid, '). ', 'To stop it, call blogdown::stop_server() or restart the R session.' ) # delete the resources/ dir if it is empty if (g == 'hugo') del_empty_dir('resources') # whether to watch for changes in Rmd files? if (!get_option('blogdown.knit.on_save', TRUE)) return(invisible()) # rebuild specific or changed Rmd files rebuild = function(files) { if (is.null(b <- get_option('blogdown.knit.on_save'))) { b = !isTRUE(opts$get('knitting')) if (!b) { options(blogdown.knit.on_save = b) message( 'It seems you have clicked the Knit button in RStudio. If you prefer ', 'knitting a document manually over letting blogdown automatically ', 'knit it on save, you may set options(blogdown.knit.on_save = FALSE) ', 'in your .Rprofile so blogdown will not knit documents automatically ', 'again (I have just set this option for you for this R session). If ', 'you prefer knitting on save, set this option to TRUE instead.' ) files = b # just ignore changed Rmd files, i.e., don't build them } } xfun::in_dir(root, build_site(TRUE, run_hugo = FALSE, build_rmd = files)) } # build Rmd files that are new and don't have corresponding output files rebuild(rmd_files <- filter_newfile(list_rmds())) watch = servr:::watch_dir('.', rmd_pattern, handler = function(files) { files = list_rmds(files = files) # ignore Rmd files in the public/ directory, in case users forgot to set # ignoreFiles in config.yaml and Rmd files would be copied to public/ # (they should not be): https://github.com/rstudio/blogdown/issues/610 i = if (g == 'hugo') !xfun::is_sub_path(files, rel_path(publish_dir())) else TRUE rmd_files <<- files[i] }) unix = xfun::is_unix() watch_build = function() { # stop watching if stop_server() has cleared served_dirs if (is.null(opts$get('served_dirs'))) return(invisible()) if (watch()) { if (is_psx) proc$suspend() else if (unix) tools::pskill(pid, tools::SIGSTOP) try(rebuild(rmd_files)) if (is_psx) proc$resume() else if (unix) tools::pskill(pid, tools::SIGCONT) refresh_viewer() } if (get_option('blogdown.knit.on_save', TRUE)) later::later(watch_build, intv) } watch_build() return(invisible()) } } jekyll_server_args = function(host, port) { c('serve', '--port', port, '--host', host, get_option( 'blogdown.jekyll.server', c('--watch', '--incremental', '--livereload') )) } hexo_server_args = function(host, port) { c('server', '-p', port, '-i', host, get_option('blogdown.hexo.server')) } #' @export #' @rdname serve_site stop_server = function() { ids = NULL # collect pids that we failed to kill quitting = isTRUE(opts$get('quitting')) for (i in opts$get('pids')) { # no need to kill a process started by processx when R is quitting if (quitting && inherits(i, 'AsIs')) next if (proc_kill(i, stdout = FALSE, stderr = FALSE) != 0) ids = c(ids, i) } if (length(ids)) warning( 'Failed to kill the process(es): ', paste(i, collapse = ' '), '. You may need to kill them manually.' ) else if (!quitting) message('The web server has been stopped.') set_envvar(c('BLOGDOWN_SERVING_DIR' = NA)) opts$set(pids = NULL, served_dirs = NULL) } get_config2 = function(key, default) { res = yaml_load_file('_config.yml') res[[key]] %n% default } # refresh the viewer because hugo's livereload doesn't work on RStudio # Server: https://github.com/rstudio/rstudio/issues/8096 (TODO: check if # it's fixed in the future: https://github.com/gohugoio/hugo/pull/6698) refresh_viewer = function() { if (!is_rstudio_server()) return() server_wait() rstudioapi::executeCommand('viewerRefresh') } server_wait = function() { Sys.sleep(get_option('blogdown.server.wait', 2)) }

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/R/findBAFvariance.R

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smgogarten/GWASTools

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findBAFvariance.R

# Description: This function determines which scans/chromosomes # are a given number of SDs from the mean, using new BAF values # produced by meanBAFSDbyChromWindow. The function returns a matrix # (with columns "scanID", "chromosome", "bin", and "sex") # of scan/chromosome combinations more than X SDs from the mean. findBAFvariance <- function(sd.by.chrom.window, sd.by.scan.chrom.window, sex, sd.threshold) { chromosomes <- names(sd.by.scan.chrom.window) n.chromosomes <- length(chromosomes) # create a matrix to hold scan, chr combination that satisfies our SDs from mean comparison res <- vector() r <- 0 # loop through chromosomes for(s in 1:n.chromosomes) { # autosomes if (chromosomes[s] != "X") { # loop through scans for(n in 1:nrow(sd.by.scan.chrom.window[[s]])) { bc <- 0 # loop through bins for(b in 1:ncol(sd.by.scan.chrom.window[[s]])) { sdev <- sd.by.chrom.window[[s]]["SD",b] m <- sd.by.chrom.window[[s]]["Mean",b] val <- sd.by.scan.chrom.window[[s]][n,b] samp <- rownames(sd.by.scan.chrom.window[[s]])[n] if(!is.na(val) & !is.na(sdev) & !is.na(m) & val > sd.threshold*sdev+m) { bc <- bc+1 if(bc==1) { res <- rbind(res, c(samp, chromosomes[s], bc, sex[n])) r <- r+1 } else { res[r,3] <- bc } } } # end loop through bins } # end loop through scans } # end if autosome # X chrom if (chromosomes[s] == "X") { # loop through scans for(n in 1:nrow(sd.by.scan.chrom.window[[s]])) { bc <- 0 for(b in 1:ncol(sd.by.scan.chrom.window[[s]])) { # loop through bins sdf <- sd.by.chrom.window[[s]]["Female SD",b] mf <- sd.by.chrom.window[[s]]["Female Mean",b] sdm <- sd.by.chrom.window[[s]]["Male SD",b] mm <- sd.by.chrom.window[[s]]["Male Mean",b] val <- sd.by.scan.chrom.window[[s]][n,b] samp <- rownames(sd.by.scan.chrom.window[[s]])[n] if(sex[n] == "F") { # it's a female if(!is.na(val) & !is.na(sdf) & !is.na(mf) & val > sd.threshold*sdf+mf) { bc <- bc+1 if(bc==1) { res <- rbind(res, c(samp, "X", bc, sex[n])) r <- r+1 } else { res[r,3] <- bc } } } else { # it's a male if(!is.na(val) & !is.na(sdm) & !is.na(mm) & val > sd.threshold*sdm+mm) { bc <- bc+1 if(bc==1) { res <- rbind(res, c(samp, "X", bc, sex[n])) r <- r+1 } else { res[r,3] <- bc } } } } # end loop through bins } # end loop through scans } # end if X } # end loop through chromosomes colnames(res) <- c("scanID", "chromosome", "bin", "sex") return(res) }

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/man/myWaitForJobs.Rd

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heiniglab/eQTLpipeline

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refs/heads/master

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myWaitForJobs.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/eqtl_lib.R \name{myWaitForJobs} \alias{myWaitForJobs} \title{basic eQTL interface} \usage{ myWaitForJobs(reg, waittime = 3, nretry = 100) } \arguments{ \item{reg}{BatchJobs registry} \item{waittime}{time to wait before updating job status} \item{nretry}{number of time to retry getting the job status before throwing #' an error} } \description{ Extension to the waitForJobs function of the BatchJobs package which shows some strange behaviour when waiting for jobs (database locked) so we need to make it extra failsafe. } \seealso{ Other eqtl functions: \code{\link{eqtl.min.p}}, \code{\link{eqtl.run}}, \code{\link{eqtl}}, \code{\link{trans.qtl}} } \concept{eqtl functions}

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/man/logR.Rd

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MikkoVihtakari/MarineDatabase

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refs/heads/master

2022-11-06T18:17:31.395171

2020-06-29T14:23:09

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logR.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/logR.R \name{logR} \alias{logR} \title{Calculate logarithmic response ratio from a long table} \usage{ logR( X, response, levels, groups, control, ci.type = NULL, ci.conf = 0.95, base = "e", paired_tests = FALSE, unlog = FALSE, all.data = FALSE, signif = 2, sqrt_transform = FALSE ) } \arguments{ \item{X}{data frame} \item{response}{character vector specifying the names of the columns for which the response ratio should be calculated.} \item{levels}{Name of the column that contains factor which should be used to separate response ratios.} \item{groups}{character vector specifying the names of the columns, which should used as grouping factors.} \item{control}{the name of the control/base factor level in \code{levels} column.} \item{ci.type}{indicates the distribution to be used for confidence intervals. \code{'z'} refers to normal distribution and \code{'t'} to t distribution. The default (\code{NULL}) is to decide the distribution based on the lowest \eqn{\sqrt(n)*mean(response)/sd(response)} (from Hedges et al. 1999). If over 10\% of values are less than 3, t-distribution is used. Otherwise normal. Note that Hedges et al. (1999) adviced for using normal distribution and that the t-distribution is an experimental addition, but I have used it in publications. The CIs will be wider (i.e. less "significant" results) when t-distribution is used. Consider using \code{paired_tests} to confirm your CIs.} \item{ci.conf}{the confidence level for the confidence interval. Defaults to 0.95 (95\%).} \item{base}{either "e" (default), 2 or 10 defining the base for the log response ratio. While "e" (i.e. \code{ln}) is the most used variant (see Hedges et al. 1999), 2 and 10 based logarithms are easier to read. Experimental. DO NOT USE in publications.} \item{paired_tests}{Logical indicating whether \link[stats]{wilcox.test} should be used to "confirm" the results indicated by the confidence intervals for the response ratios.} \item{unlog}{logical indicating whether the output should be unlogged. Defaults to \code{FALSE}. Read the page 1152 under eq. 9 and what follows on the next page from Hedges et al. (1999) before you switch this to \code{TRUE} in publications. The unlogging is done by simply exponentiating for confidence intervals, while the response ratio (mean) is calculated as \eqn{\exp(LnR + var/2)} after Greenacre (2016). Currently untested for response ratios.} \item{all.data}{logical indicating whether all data used in calculations should be returned instead of a concise table of relevant results. Defaults to \code{FALSE}.} \item{signif}{number of significant digits in output. Defaults to 2. If \code{'all'} output will not be rounded.} \item{sqrt_transform}{Logical indicating whether values should be square root transformed prior calculation of means. This option makes the distributions more normally distributed, but might change the outcome. Highly experimental. DO NOT USE in publications.} } \value{ Returns a list where \code{$data} element contains the calculated response ratios for each \code{response} in a separate list named by the response's column name. \code{$info} contains information how the response ratios were calculated and, if \code{paired_tests = TRUE}, the \code{$tests} element gives \link[stats]{wilcox.test} results to "confirm" significance of the confidence intervals for the response ratios. Nonconforming tests are listed under \code{$nonconforming}. } \description{ Calculates logarithmic response ratio from a long table where each row represents one measurement. } \details{ The calculations are based on Hedges et al. (1999), with the exception that t-distribution is used to acquire confidence intervals instead of normal distribution. The difference is minimal for sample sizes > 20, but the confidence intervals will be a lot more conservative for small sample sizes leading to fewer false positives. Use \code{ci.type = "z"} to use normal distribution for CI estimation as described in the original source. \strong{Note} that the function does not currently calculate dependent sample response ratios, as the pooled variance needs to be penalized by the correlation term for such analyses. See Lajeunesse (2011) and \href{https://stats.stackexchange.com/questions/141443/calculating-effect-size-lnr-variances-for-studies-with-different-study-designs}{CrossValidated} for further information. The square root transformation routine is experimental and little tested, but seems to produce slightly less nonconforming test results against \link[stats]{wilcox.test} for non-normal data. It is recommended to plot your raw values to confirm any results given by this function. } \references{ Hedges, L. V, Gurevitch, J., & Curtis, P.S. (1999) The meta-analysis of response ratios in experimental ecology. Ecology, 80, 1150–1156. Lajeunesse, M. J. (2011). On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology, 92, 2049-2055. Greenacre, M., (2016). Data reporting and visualization in ecology. Polar Biology 39, 2189–2205. doi:10.1007/s00300-016-2047-2 } \author{ Mikko Vihtakari }

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/03_plotting.R

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guidocor/R-for-the-lazy-psychologist

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# https://www.datacamp.com/courses/data-visualization-with-ggplot2-1

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/data/genthat_extracted_code/mapproj/examples/map.grid.Rd.R

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surayaaramli/typeRrh

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refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

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map.grid.Rd.R

library(mapproj) ### Name: map.grid ### Title: Draw a latitude/longitude grid on a projected map ### Aliases: map.grid ### Keywords: aplot ### ** Examples library(maps) m <- map("usa", plot=FALSE) map("usa", project="albers", par=c(39, 45)) map.grid(m) # get unprojected world limits m <- map('world', plot=FALSE) # center on NYC map('world', proj='azequalarea', orient=c(41, -74, 0)) map.grid(m, col=2) points(mapproject(list(y=41, x=-74)), col=3, pch="x", cex=2) map('world', proj='orth', orient=c(41, -74, 0)) map.grid(m, col=2, nx=6, ny=5, label=FALSE, lty=2) points(mapproject(list(y=41, x=-74)), col=3, pch="x", cex=2) # center on Auckland map('world', proj='orth', orient=c(-36.92, 174.6, 0)) map.grid(m, col=2, label=FALSE, lty=2) points(mapproject(list(y=-36.92, x=174.6)), col=3, pch="x", cex=2) m <- map('nz') # center on Auckland map('nz', proj='azequalarea', orient=c(-36.92, 174.6, 0)) points(mapproject(list(y=-36.92, x=174.6)), col=3, pch="x", cex=2) map.grid(m, col=2)

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/C-score, dist.meu.R

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csdambros/R-functions

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refs/heads/master

2022-03-11T13:52:43.523166

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C-score, dist.meu.R

###função criada por Cristian de Sales Dambros em 20/11/2009 ###Retorna o índice C-score (Roberts & Stone 1990) para a dada matriz cscore<- function (x) { Nsites <- nrow(x) S <- colSums(x) R <- ncol(x) P<-((R * (R - 1))/2) ab <- 0 for (i in 1:R) { for (j in (1:R)[-i]) { if(j<i){next}else{ Q <- sum(x[, i] * x[, j]) ab <-ab+ ((S[i] - Q) * (S[j] - Q))/P} }} ab } ###################### dist.COR<- function (x) { x<-x[,colSums(x)!=nrow(x)] R <- ncol(x) ab <- 0 for (i in 1:R) { for (j in (1:R)[-i]) { if(j<i){next}else{ ab<-ab+cor(x[,i],x[,j],method="pearson") }}} ab }

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# 1 - Model fit summary. fit.summary<- function(fit) { return(fitMeasures(fit, c("chisq", "df", "pvalue", "rmsea", "srmr", "CFI", "tli", "aic", "bic"))) } # 2 - Modification indices sum. mi.sum<- function(mi, mod.list) { # l1: number of factor l1<- length(mod.list) for (i in 1:l1) { f.item<- unlist(mod.list[i]) # l2: number of item in one factor l2<- length(f.item) for (j in 2:l2) { item<- subset(mi, lhs== f.item[1] & rhs== f.item[j] & op== "=~") mi.sum<- sum(item$mi) if (j==2) mi.sum.vector<- mi.sum else mi.sum.vector<- c(mi.sum.vector, mi.sum) } if (i==1) mi.sum.list<- list(mi.sum.vector) else mi.sum.list[[i]]<- mi.sum.vector } return(mi.sum.list) }

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/R/DubObs.R

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DubObs.R

DubMatch = function(front.data=NA, rear.data=NA, combined.data=NA, front.obs, rear.obs, time=6, open=5){ #check if combined data was given, if not, read in each file if(class(front.data)=="data.frame"){ f=front.data[front.data$obs==front.obs, ] } if(class(rear.data)=="data.frame"){ r=rear.data[rear.data$obs==rear.obs, ] } if(class(front.data)=="character"){ data=read.csv(front.data, header=TRUE, stringsAsFactors = FALSE) f=data[data$obs==front.obs, ] } if(class(rear.data)=="character"){ data=read.csv(rear.data, header=TRUE, stringsAsFactors = FALSE) r=data[data$obs==rear.obs, ] } if(class(combined.data)=="character"){ data=read.csv(combined.data, header=TRUE, stringsAsFactors = FALSE) f=data[data$obs==front.obs, ] r=data[data$obs==rear.obs, ] } if(class(combined.data)=="data.frame"){ f=combined.data[combined.data$obs==front.obs, ] r=combined.data[combined.data$obs==rear.obs, ] } f$matched=0 r$matched=0 f.tran=unique(f$tran) r.tran=unique(r$tran) common=as.character(f.tran[!is.na(match(f.tran, r.tran))]) f=f[f$tran %in% common,] r=r[r$tran %in% common,] #empty data frame to populate with matches matches=data.frame(yr=integer(), tran=character(), ch=character(), sppn=character(), grp=integer(), unit=character(), front=character(), rear=character(), crew=character(), stringsAsFactors = FALSE ) for (i in 1:length(f$yr)){ # matches=rbind(matches, c(f$yr[i], f$tran[i], "10", f$sppn[i], f$grp[i], f$unit[i], f$obs[i], r$obs[1], paste(f$obs[i], r$obs[1]))) for (j in 1:length(r$yr)){ if(r$matched[j]==1) {next} if(r$yr[j]!=f$yr[i]) {next} if(r$tran[j]!=f$tran[i]) {next} if(r$sppn[j]!=f$sppn[i]) {next} if(r$unit[j]!=f$unit[i]) {next} if(abs(f$ctime[i]-r$ctime[j])<=time){ newline=data.frame(yr=f$yr[i], tran=f$tran[i], ch="11", sppn=f$sppn[i], grp=(f$grp[i]+r$grp[j])/2, unit=f$unit[i], front=f$obs[i], rear=r$obs[j], crew=paste(f$obs[i], r$obs[j], sep=""), stringsAsFactors = FALSE ) matches=rbind(matches, newline) #matches= rbind(matches, list(f$yr[i], f$tran[i], "11", f$sppn[i], (f$grp[i]+r$grp[j])/2, f$unit[i], f$obs[i], r$obs[j], paste(f$obs[i], r$obs[j]))) f$matched[i]=1 r$matched[j]=1 break } } if (f$matched[i]==0){ newline=data.frame(yr=f$yr[i], tran=f$tran[i], ch="10", sppn=f$sppn[i], grp=f$grp[i], unit=f$unit[i], front=f$obs[i], rear=r$obs[1], crew=paste(f$obs[i], r$obs[1], sep=""), stringsAsFactors = FALSE ) matches=rbind(matches, newline) } } for (k in 1:length(r$yr)){ if(r$matched[k]==1) {next} newline=data.frame(yr=r$yr[k], tran=r$tran[k], ch="01", sppn=r$sppn[k], grp=r$grp[k], unit=r$unit[k], front=f$obs[1], rear=r$obs[k], crew=paste(f$obs[1], r$obs[k], sep=""), stringsAsFactors = FALSE ) matches=rbind(matches, newline) #matches=rbind(matches, c(r$yr[k], r$tran[k], "01", r$sppn[k], r$grp[k], r$unit[k], f$obs[1], r$obs[k], paste(f$obs[1], r$obs[k]))) } return(list(matches=matches, f=f) ) }

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/man/GEVASummary-class.Rd

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GEVASummary-class.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/c_GEVASummary.R \docType{class} \name{GEVASummary-class} \alias{GEVASummary-class} \alias{show,GEVASummary-method} \alias{plot,GEVASummary,missing-method} \alias{inputdata,GEVASummary-method} \alias{inputvalues,GEVASummary-method} \alias{inputweights,GEVASummary,logical-method} \alias{inputweights,GEVASummary,missing-method} \alias{inputnames,GEVASummary-method} \alias{featureTable,GEVASummary-method} \alias{factors,GEVASummary-method} \alias{factors<-,GEVASummary,factor-method} \alias{factors<-,GEVASummary,character-method} \alias{infolist,GEVASummary,missing-method} \alias{infolist<-,GEVASummary,list-method} \alias{quantiles,GEVASummary-method} \alias{analysis.params,GEVASummary-method} \alias{groupsets,GEVASummary-method} \alias{groupsets<-,GEVASummary,TypedList-method} \alias{groupsets<-,GEVASummary,GEVAGroupSet-method} \alias{get.summary.method.GEVASummary} \alias{get.variation.method.GEVASummary} \alias{as.matrix.GEVASummary} \alias{as.expression.GEVASummary} \title{GEVA Summary-Variation Table} \value{ A \code{\linkS4class{GEVASummary}} object } \description{ The \code{GEVASummary} class represents the calculation results for summary and variation from a \code{\linkS4class{GEVAInput}}. This class inherits from \code{\link{SVTable}}. } \section{Slots}{ \describe{ \item{\code{sv}}{\verb{numeric matrix} composed by two columns: \code{S} (summary) and \code{V} (variation) \cr (Inherited from \code{\linkS4class{SVTable}})} \item{\code{inputdata}}{GEVAInput-class with the data input} \item{\code{sv.method}}{Names of the statistical methods used to summarize data} \item{\code{info}}{list with additional information} }} \section{Methods}{ (See also the inherited methods from \code{\linkS4class{SVTable}})\cr \sspace\cr\strong{Conversion and coercion} \describe{ \item{\code{as.expression(x, ginput, ...)}}{Gets the expression that reproduces this \code{GEVASummary} object, including function parameters used by \code{geva.summary}. The \code{ginput} argument is optional but can be specified to replace the internal \code{GEVAInput}} \item{\code{as.matrix(x, ...)}}{Equivalent to \code{sv(x)}} } \sspace\cr\strong{Grouping} \describe{ \item{\code{groupsets(object) <- value}}{Converts this instance to \code{\linkS4class{GEVAGroupedSummary}} and sets the list of \code{\linkS4class{GEVAGroupSet}} objects. Can be used with \verb{$<name>} to specify the object name in the list. If \code{value} is a \code{GEVAGroupSet}, inserts the element and sets the name based on the value call} \item{\code{groupsets(object)}}{Gets the list of \code{\linkS4class{GEVAGroupSet}} objects attached to this instance. Only applicable for \code{\linkS4class{GEVAGroupedSummary}} objects} } \sspace\cr\strong{Plotting} \describe{ \item{\code{plot(x, y, ...)}}{Draws a SV-plot. The horizontal axis is for \emph{summary} (S) and the vertical axis is for \emph{variation} (V)} } \sspace\cr\strong{Properties} \describe{ \item{\code{analysis.params(gobject)}}{Returns a \code{list} of analysis parameters passed to \code{\link{geva.summarize}} to obtain this object} \item{\code{get.summary.method(x)}}{Gets a \code{character} for the summarization method name} \item{\code{get.variation.method(x)}}{Gets a \code{character} for the variation calculation method name} } \sspace\cr\strong{Sub-slot accessors} \describe{ \item{\code{factors(object) <- value}}{Sets the value to the \code{factor} slot in the internal \code{\linkS4class{GEVAInput}}} \item{\code{factors(object)}}{Gets the \code{factor} defined in the \code{factors} slot in the internal \code{\linkS4class{GEVAInput}}} \item{\code{featureTable(object)}}{Gets the \code{data.frame} from the \code{ftable} slot in the internal \code{\linkS4class{GEVAInput}}} \item{\code{infolist(object, field = NULL, ...)}}{Gets the \code{list} from the \code{info} slot. \cr If \code{recursive} is \code{TRUE}, appends the contents from the \code{info} slot in the internal \code{\linkS4class{GEVAInput}}} \item{\code{inputvalues(object)}}{Gets the \code{matrix} from the \code{values} slot in the internal \code{\linkS4class{GEVAInput}}} \item{\code{inputweights(object, normalized)}}{Gets the \code{matrix} from the \code{weights} slot in the internal \code{\linkS4class{GEVAInput}}} } }

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/TimeSeries_Models.R

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TimeSeries_Models.R

library(dplyr) library(forecast) library(tseries) library(dataPreparation) # Import rds objets for delhi and ghaziabad hourly files delpb <- readRDS("df_delpb_hourly.rds") gha <- readRDS("df_gha_hourly.rds") ########################################### # Delhi ########################################### ############# Training # Create time series object train_ts <- delpb %>% filter(Timeline >= '2018-01-01 00:00:00' & Timeline <= '2019-12-31 23:00:00') %>% select (PM2.5) %>% dplyr::rename(pm25_0 = PM2.5) %>% ts(frequency = 24) plot(decompose(train_ts, type="additive")) # ACF PACF Plots par(mfrow=c(1,2)) acf(train_ts) pacf(train_ts) # Ist order differencing par(mfrow=c(1,2)) acf(diff(train_ts)) pacf(diff(train_ts)) # Prepare xreg xreg_to_scale <- delpb %>% filter(Timeline >= '2018-01-01 00:00:00' & Timeline <= '2019-12-31 23:00:00') %>% subset(select = c(precipIntensity,precipProbability,apparentTemperature,dewPoint,pressure, windSpeed,windGust,cloudCover,uvIndex,visibility)) arimax_scales <- build_scales(xreg_to_scale, verbose = TRUE) saveRDS(arimax_scales,"arimax_scales_delpb.rds") arimax_scales <- readRDS("arimax_scales_delpb.rds") xreg_scale <- fastScale(xreg_to_scale, scales = arimax_scales, verbose = TRUE) xreg_train <- as.matrix(xreg_scale) # Build ARIMAX arimax_delpb <- forecast::auto.arima(train_ts,seasonal=TRUE,trace=TRUE,xreg=xreg_train, parallel=TRUE) # Regression with ARIMA(3,1,1)(0,0,2)[24] # Save rds object saveRDS(arimax_delpb,"arimax_delpb.rds") tsdiag(arimax_delpb) plot(arimax_delpb$x, col="black") lines(fitted(arimax_delpb), col="red") #Again, let's check if the residual series is white noise resi_auto_arima <- train_ts - fitted(arimax_delpb) adf.test(resi_auto_arima,alternative = "stationary") # Dickey-Fuller = -21.312, Lag order = 25, p-value = 0.01 kpss.test(resi_auto_arima) # KPSS Level = 0.060765, Truncation lag parameter = 14, p-value = 0.1 ############# Test test <- delpb %>% filter(Timeline >= '2020-01-01 00:00:00') # Prepare xreg xregt_to_scale <- delpb %>% filter(Timeline >= '2020-01-01 00:00:00') %>% subset(select = c(precipIntensity,precipProbability,apparentTemperature,dewPoint,pressure, windSpeed,windGust,cloudCover,uvIndex,visibility)) xregt_scale <- fastScale(xregt_to_scale, scales = arimax_scales, verbose = TRUE) xreg_test <- as.matrix(xregt_scale) # Model evaluation 96 hours fcast_auto_arima <- forecast(arimax_delpb, xreg=xreg_test[c(1:360),], n.ahead = 360) accuracy(fcast_auto_arima$fitted[1:360],delpb_test$PM2.5[1:360]) # ME RMSE MAE MPE MAPE #Test set -93.16557 155.2201 123.8407 -110.1408 120.9653 arimax_delpb_eval <- data.frame(Timeline=as.POSIXct(delpb_test$Timeline[1:360]), Forecast=fcast_auto_arima$fitted[1:360], Actual=delpb_test$PM2.5[1:360]) # save rds object saveRDS(arimax_delpb_eval,"C:/Users/send2/Documents/Ankur/MSc/Temp/rdsObject/arimax_delpb_eval.rds") ########################################### # Ghaziabad ########################################### ############# Training # Create time series object train_ts <- gha %>% filter(Timeline >= '2018-01-01 00:00:00' & Timeline <= '2019-12-31 23:00:00') %>% select (PM2.5) %>% dplyr::rename(pm25_0 = PM2.5) %>% ts(frequency = 24) plot(decompose(train_ts, type="additive")) # ACF PACF Plots par(mfrow=c(1,2)) acf(train_ts) pacf(train_ts) # Ist order differencing par(mfrow=c(1,2)) acf(diff(train_ts)) pacf(diff(train_ts)) # Prepare xreg xreg_to_scale <- gha %>% filter(Timeline >= '2018-01-01 00:00:00' & Timeline <= '2019-12-31 23:00:00') %>% subset(select = c(precipIntensity,precipProbability,apparentTemperature,dewPoint,pressure, windSpeed,windGust,cloudCover,uvIndex,visibility)) arimax_scales <- build_scales(xreg_to_scale, verbose = TRUE) saveRDS(arimax_scales,"arimax_scales_gha.rds") xreg_scale <- fastScale(xreg_to_scale, scales = arimax_scales, verbose = TRUE) xreg_train <- as.matrix(xreg_scale) # Build ARIMAX arimax_gha <- forecast::auto.arima(train_ts,seasonal=TRUE,trace=TRUE,xreg=xreg_train) # Best model: Regression with ARIMA(2,1,2)(1,0,0)[24] saveRDS(arimax_gha,"arimax_gha.rds") tsdiag(arimax_gha) plot(arimax_gha$x, col="black") lines(fitted(arimax_gha), col="red") #Again, let's check if the residual series is white noise resi_auto_arima <- train_ts - fitted(arimax_gha) adf.test(resi_auto_arima,alternative = "stationary") # Dickey-Fuller = -20.92, Lag order = 25, p-value = 0.01 kpss.test(resi_auto_arima) # KPSS Level = 0.035704, Truncation lag parameter = 14, p-value = 0.1 ############# Test gha_test <- gha %>% filter(Timeline >= '2020-01-01 00:00:00') # Prepare xreg xregt_to_scale <- gha %>% filter(Timeline >= '2020-01-01 00:00:00') %>% subset(select = c(precipIntensity,precipProbability,apparentTemperature,dewPoint,pressure, windSpeed,windGust,cloudCover,uvIndex,visibility)) xregt_scale <- fastScale(xregt_to_scale, scales = arimax_scales, verbose = TRUE) xreg_test <- as.matrix(xregt_scale) #Also, let's evaluate the model using MAPE fcast_auto_arima <- forecast(arimax_gha, xreg=xreg_test[c(1:360),], n.ahead = 360) accuracy(fcast_auto_arima$fitted[1:360],gha_test$PM2.5[1:360]) # ME RMSE MAE MPE MAPE #Test set -95.699 158.0659 125.8172 -84.58171 94.25856 arimax_gha_eval <- data.frame(Timeline=as.POSIXct(gha_test$Timeline[1:360]), Forecast=fcast_auto_arima$fitted[1:360], Actual=gha_test$PM2.5[1:360]) saveRDS(arimax_gha_eval,"arimax_gha_eval.rds")

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/man/errAAll.Rd

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RajeswaranV/proportion

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errAAll.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/511.Error-Failure_LimitBased_ADJ_All.R \name{errAAll} \alias{errAAll} \title{Calculates error, long term power and pass/fail criteria using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)} \usage{ errAAll(n, alp, h, phi, f) } \arguments{ \item{n}{- Number of trials} \item{alp}{- Alpha value (significance level required)} \item{h}{- Adding factor} \item{phi}{- Null hypothesis value} \item{f}{- Failure criterion} } \value{ A dataframe with \item{delalp}{ Delta-alpha is the increase of the nominal error with respect to real error} \item{theta}{ Long term power of the test} \item{Fail_Pass}{Fail/pass based on the input f criterion} \item{method}{Name of the method} } \description{ Calculates error, long term power and pass/fail criteria using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) } \details{ Calculates error, long term power and pass/fail criteria using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) } \examples{ n=20; alp=0.05;h=2; phi=0.99; f=-2 errAAll(n,alp,h,phi,f) } \seealso{ Other Error for adjusted methods: \code{\link{PloterrAAS}()}, \code{\link{PloterrAAll}()}, \code{\link{PloterrALR}()}, \code{\link{PloterrALT}()}, \code{\link{PloterrASC}()}, \code{\link{PloterrATW}()}, \code{\link{PloterrAWD}()}, \code{\link{errAAS}()}, \code{\link{errALR}()}, \code{\link{errALT}()}, \code{\link{errASC}()}, \code{\link{errATW}()}, \code{\link{errAWD}()} } \concept{Error for adjusted methods}

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#' Polite file download #' #' @param bow host introduction object of class polite, session created by bow() or nod #' @param new_filename optional new file name to use when saving the file #' @param suffix optional characters added to file name #' @param sep separator between file name and suffix. Default "__" #' @param path path where file should be saved. Defults to folder named "downloads" created in the working directory #' @param overwrite if TRUE will overwrite file on disk #' @param mode character. The mode with which to write the file. Useful values are "w", "wb" (binary), "a" (append) and "ab". Not used for methods "wget" and "curl". #' @param ... other parameters passed to download.file #' #' @return Full path to file indicated by url saved on disk #' @export #' #' @examples #' \dontrun{ #' bow("www.mysite.com") %>% #' nod("file.txt") %>% #' rip() #' } #' @importFrom here here #' @importFrom tools file_path_sans_ext file_ext rip <- function(bow, new_filename=NULL, suffix=NULL, sep="__", path="downloads", overwrite=FALSE, mode="wb", ...){ url <- bow$url if(!dir.exists(here::here(path))) dir.create(here::here(path)) if(!is.null(suffix)) suffix <- paste0(sep, suffix) new_filename <- new_filename %||% paste0(tools::file_path_sans_ext(basename(url)), suffix, ".", tools::file_ext(basename(url)), sep="") if(file.exists(here::here(path, new_filename)) && !overwrite){ warning("File already exists", call. = FALSE) return(here::here(path, new_filename)) } bow$download_file_ltd(url, here::here(path, new_filename), mode=mode, ...) return(here::here(path, new_filename)) }

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global.R

library(tidyverse) library(tidylog) library(janitor) library(shiny) library(leaflet) library(scales) library(plotly) library(knitr) library(shinydashboard) d <- read_csv("data/corona.csv") d_last <- d %>% filter(date == max(date)) %>% mutate(color = case_when( confirmed_num <= quantile(confirmed_num, .25) ~ "green", confirmed_num > quantile(confirmed_num, .25) & confirmed_num <= quantile(confirmed_num, .5)~ "yellow", confirmed_num > quantile(confirmed_num, .5) & confirmed_num <= quantile(confirmed_num, .75)~ "orange", TRUE ~"red" )) # source('/cloud/project/helpers.R') # # # # data files paths -------------------------------------------------------- # # # confirmed_url <- # "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv" # deaths_url <- # "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv" # recorved_url <- # "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv" # # # # # data cleaning ----------------------------------------------------------- # # l_urls <- list(confirmed_url, deaths_url, recorved_url) # c_titles <- c("confirmed_num", "deaths_num", "recovered_num") # # d <- # map2(l_urls, c_titles, clean_df5) %>% # reduce(left_join)

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/extraindo_dados_TWEETER.R

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WOLFurriell/twitterR

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refs/heads/master

2020-04-15T14:10:29.591089

2019-01-08T22:40:32

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extraindo_dados_TWEETER.R

library(twitteR) library(tm) library(wordcloud) library(RColorBrewer) library(tm) library(dplyr) library(data.table) library(WriteXLS) rm(list = ls()) setwd("D:/TWITTER/Bancos_tweets") #----------------------------- #colocando as chaves api_key <- "xxxxxxxxxxxxxxxxxxxxxxxxxxx" api_secret <- "xxxxxxxxxxxxxxxxxxxxxxxxxxx" access_token <- "xxxxxxxxxxxxxxxxxxxxxxxxxxx" access_token_secret <- "xxxxxxxxxxxxxxxxxxxxxxxxxxx" setup_twitter_oauth(api_key,api_secret,access_token,access_token_secret) #Retirar as primeiras 3200 com retweets e replies ##----------------- MARINA twsilva_marina<-userTimeline('silva_marina',n=3200,includeRts=T,excludeReplies=F) silva_marina_df<-twListToDF(twsilva_marina) silva_marina_df2<-sapply(twsilva_marina, function(x) x$getText()) saveRDS(silva_marina_df, file="twsilva_marina.Rds") saveRDS(silva_marina_df2, file="twsilva_marina2.Rds") ##----------------- DILMA twdilma<-userTimeline('dilmabr',n=3200,includeRts=T,excludeReplies=F) dilma_df<-twListToDF(twdilma) dilma_df2<-sapply(twdilma, function(x) x$getText()) saveRDS(dilma_df, file="twdilma.Rds") saveRDS(dilma_df2, file="twdilma2.Rds") ##----------------- AECIO twaecio<-userTimeline('AecioNeves',n=3200,includeRts=T,excludeReplies=F) aecio_df<-twListToDF(twaecio) aecio_df2<-sapply(twaecio, function(x) x$getText()) saveRDS(aecio_df, file="twaecio.Rds") saveRDS(aecio_df2, file="twaecio2.Rds") #retirar por datas específicas #podemos verificar a data do último tweet exportado por userTimeline e pegar o restante por aqui #twsilva_marina2<- searchTwitter('from:silva_marinaNeves', n =1500,lang = 'pt', # since ='2014-07-06',until = '2014-10-11') ## extrair o texto de todos os tweets #write.table(silva_marina_df,'D:/TWITTER/Bancos_tweets/twsilva_marina4.txt') #remover emojis, é possível usar 'ASCII'

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/man/eel.test2.Rd

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cran/Compositional

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refs/heads/master

2023-07-06T09:05:20.638372

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eel.test2.Rd

\name{Exponential empirical likelihood hypothesis testing for two mean vectors} \alias{eel.test2} \title{ Exponential empirical likelihood hypothesis testing for two mean vectors } \description{ Exponential empirical likelihood hypothesis testing for two mean vectors. } \usage{ eel.test2(y1, y2, tol = 1e-07, R = 0, graph = FALSE) } \arguments{ \item{y1}{ A matrix containing the Euclidean data of the first group. } \item{y2}{ A matrix containing the Euclidean data of the second group. } \item{tol}{ The tolerance level used to terminate the Newton-Raphson algorithm. } \item{R}{ If R is 0, the classical chi-square distribution is used, if R = 1, the corrected chi-square distribution (James, 1954) is used and if R = 2, the modified F distribution (Krishnamoorthy and Yanping, 2006) is used. If R is greater than 3 bootstrap calibration is performed. } \item{graph}{ A boolean variable which is taken into consideration only when bootstrap calibration is performed. IF TRUE the histogram of the bootstrap test statistic values is plotted. } } \details{ Exponential empirical likelihood is a non-parametric hypothesis testing procedure for one sample. The generalization to two (or more samples) is via searching for the mean vector that minimises the sum of the two test statistics. } \value{ A list including: \item{test}{ The empirical likelihood test statistic value. } \item{modif.test}{ The modified test statistic, either via the chi-square or the F distribution. } \item{dof}{ The degrees of freedom of the chi-square or the F distribution. } \item{pvalue}{ The asymptotic or the bootstrap p-value. } \item{mu}{ The estimated common mean vector. } \item{runtime}{ The runtime of the bootstrap calibration. } } \references{ Jing Bing-Yi and Andrew TA Wood (1996). Exponential empirical likelihood is not Bartlett correctable. Annals of Statistics 24(1): 365-369. G.S. James (1954). Tests of Linear Hypothese in Univariate and Multivariate Analysis when the Ratios of the Population Variances are Unknown. Biometrika, 41(1/2): 19-43 Krishnamoorthy K. and Yanping Xia (2006). On Selecting Tests for Equality of Two Normal Mean Vectors. Multivariate Behavioral Research 41(4): 533-548. Owen A. B. (2001). Empirical likelihood. Chapman and Hall/CRC Press. Amaral G.J.A., Dryden I.L. and Wood A.T.A. (2007). Pivotal bootstrap methods for k-sample problems in directional statistics and shape analysis. Journal of the American Statistical Association 102(478): 695-707. Preston S.P. and Wood A.T.A. (2010). Two-Sample Bootstrap Hypothesis Tests for Three-Dimensional Labelled Landmark Data. Scandinavian Journal of Statistics 37(4): 568-587. Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406-422. } \author{ Michail Tsagris. R implementation and documentation: Michail Tsagris \email{mtsagris@uoc.gr}. } %\note{ %% ~~further notes~~ %} \seealso{ \code{\link{el.test2}, \link{maovjames}, \link{maov}, \link{hotel2T2}, \link{james}, \link{comp.test} } } \examples{ y1 = as.matrix(iris[1:25, 1:4]) y2 = as.matrix(iris[26:50, 1:4]) eel.test2(y1, y2) eel.test2(y1, y2 ) eel.test2( y1, y2 ) } \keyword{ Multivariate hypothesis testing } \keyword{ non parametric test }

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/myFunctions/R/visualization.R

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vikram-g/R-Functions

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visualization.R

#' #'Plot logit curve #' #'Plot the logit curve along with dot histogram of the actual target values. #' #' @param df Dataframe containing the target as well as variable to plot #' @param varName Name of the variable that has to be plotted #' @param target Name of the target variable #' @return Logit plot as output #' @export #' plot_logit_curve <- function(df,varName,target){ require("ggplot2") require("dplyr") plt_dat <- df[,c(varName,target)] names(plt_dat) <- c("idv","dv") glm_model <- glm(dv ~ idv, data=plt_dat, family=binomial(link="logit")) plt_dat <- cbind(plt_dat,as.data.frame(predict(glm_model, newdata = plt_dat, type="link", se=TRUE))) plt_dat <- plt_dat[!is.na(plt_dat$idv),] ones <- plt_dat[plt_dat$dv == 1,] ones$breaks <- cut(ones$idv, breaks=30,include.lowest=TRUE) ones$breaks <- gsub(("\\["),x = gsub(("\\]"),x = gsub(("\\("),x = as.character(ones$breaks), replacement = ""), replacement = ""), replacement = "") ones$break1 <- sapply(1:nrow(ones), function(x) as.numeric(strsplit(ones$breaks[x],",")[[1]][1])) ones$break2 <- sapply(1:nrow(ones), function(x) as.numeric(strsplit(ones$breaks[x],",")[[1]][2])) ones$breaks <- (ones$break1 + ones$break2)/2 ones$break1 <- NULL ones$break2 <- NULL zeros <- plt_dat[plt_dat$dv == 0,] zeros$breaks <- cut(zeros$idv, breaks=30,include.lowest=TRUE) zeros$breaks <- gsub(("\\["),x = gsub(("\\]"),x = gsub(("\\("),x = as.character(zeros$breaks), replacement = ""), replacement = ""), replacement = "") zeros$break1 <- sapply(1:nrow(zeros), function(x) as.numeric(strsplit(zeros$breaks[x],",")[[1]][1])) zeros$break2 <- sapply(1:nrow(zeros), function(x) as.numeric(strsplit(zeros$breaks[x],",")[[1]][2])) zeros$breaks <- (zeros$break1 + zeros$break2)/2 zeros$break1 <- NULL zeros$break2 <- NULL alls <- rbind(ones, zeros) h <- alls %>% group_by(dv, breaks) %>% summarise(n = n()) %>% mutate(pct = ifelse(dv==0, n/sum(n), 1 - n/sum(n))) # Calculate confidence intervals std <- qnorm(0.95 / 2 + 0.5) plt_dat$ymin <- glm_model$family$linkinv(plt_dat$fit - std * plt_dat$se.fit) plt_dat$ymax <- glm_model$family$linkinv(plt_dat$fit + std * plt_dat$se.fit) plt_dat$fit <- glm_model$family$linkinv(plt_dat$fit) # Rescale to 0-1 # Plot everything logit_plot <- ggplot(plt_dat, aes(x=idv, y=dv)) + geom_segment(data=h, size=4, show.legend=FALSE, aes(x=breaks, xend=breaks, y=dv, yend=pct, colour=factor(dv))) + #geom_point(color = "green", alpha = 0.1) + geom_ribbon(data=plt_dat, fill = "blue", aes(y=fit, ymin=ymin, ymax=ymax), alpha=0.5) + #geom_dotplot( # aes(fill = factor(dv)), method = "histodot", binpositions = "all", # stackgroups = TRUE, stackdir = "centerwhole", binwidth = 1, alpha = 0.25,stackratio = 1,position="identity" #) + scale_fill_discrete(name = target) + scale_y_continuous(labels = scales::percent) + geom_line(data=plt_dat, aes(y=fit), color = "red") + labs(x=varName, y=paste0(target)) + ggtitle(paste0(varName,' - Logit plot')) plot(logit_plot) }

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/R/test_new_gam.R

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ToonVanDaele/trias-test

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test_new_gam.R

### Test gam methods library(tidyverse) library(mgcv) library(gratia) # Load data df_s <- readRDS(file = "./data/df_s.RDS") df_pp <- readRDS(file = "./data/df_pp.RDS") df_xy <- readRDS(file = "./data/df_xy.RDS") spec_namE1s <- readRDS(file = "./data/spec_namE1s.RDS") # Select species spn <- "1718308" spn <- "1690429" spn <- "2219863" #piek - volledig weg door class correction # spn <- "2362868" #spn <- "2439261" #grote dataset (134000 records). "inner loop 1; can't correct step size spn <- "2706056" #goede tijdreeks mE1t niet al te veel zeros df_ss <- filter(df_s, taxonKey == spn) df_sp <- filter(df_pp, taxonKey == spn) df_ss$eea_cell_code <- as.factor(df_ss$eea_cell_code) head(df_ss) head(df_sp) # SomE1 minor problem with cobs < obs (should never be the case) # should be solved at the preprocessing stage # temp <- df_ss %>% # mutate(oops = ifelse(cobs < obs, TRUE, FALSE)) # summary(temp) # # dd <- temp %>% # filter(oops == TRUE) %>% # group_by(eea_cell_code) %>% # summarise(grid = n()) df_ss <- df_ss %>% mutate(cobs = ifelse(cobs < obs, obs, cobs)) # Plot reeks voor één enkele cel df_ss %>% filter(eea_cell_code == "1kmE13839N3112") %>% dplyr::select(year, obs, cobs) %>% gather(key = type, value = obs, - year) %>% ggplot(aes(x = year, y = obs, colour = type)) + geom_point() + geom_line() ###################################################### # GAM # Model A - gaussian mA <- gam(obs ~ s(year, bs = "tp"), data = df_ss, method = "REML") summary(mA) plot(mA) temp <- predict(object = mA, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mA$coefficients[1]) df_mA <- df_ss df_mA$fit <- temp$fit[,1] + intercept df_mA$ucl <- temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96 df_mA$lcl <- temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96 summary(df_mA) #appraise(mA) # How does it fit? df_mA %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) ############################################### # Model B - negative binomial mB <- gam(obs ~ s(year, bs = "tp"), family = nb(), data = df_ss, method = "REML") summary(mB) plot(mB) #appraise(mB) # How does it fit? temp <- predict(object = mB, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mB$coefficients[1]) df_mB <- df_ss df_mB$fit <- exp(temp$fit[,1] + intercept) df_mB$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_mB$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_mB %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) # Model C - negative binomial - correct for class observations # How does cobs look like? df_temp <- df_ss %>% group_by(year) %>% summarise(yobs = sum(obs), ycobs = sum(cobs)) ggplot(df_temp, aes(x = year, y = ycobs)) + geom_point() + geom_line() ggplot(df_temp, aes(x = yobs, y = ycobs)) + geom_point() # C1 -> class observations as smoother mC1 <- gam(obs ~ s(year, bs = "tp") + s(cobs, k = 3), family = nb(), data = df_ss, method = "REML") summary(mC1) plot(mC1) appraise(mC1) # How does it fit? temp <- predict(object = mC1, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mC1$coefficients[1]) df_mC1 <- df_ss df_mC1$fit <- exp(temp$fit[,1] + intercept) df_mC1$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_mC1$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_mC1 %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) + ggtitle("long data - nb - class observation as smoother") # C2 -> class observations as linear predictor mC2 <- gam(obs ~ s(year, bs = "tp") + cobs, family = nb(), data = df_ss, method = "REML") summary(mC2) plot(mC2) appraise(mC2) # How does it fit? temp <- predict(object = mC2, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mC2$coefficients[1]) df_mC2 <- df_ss df_mC2$fit <- exp(temp$fit[,1] + intercept) df_mC2$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_mC2$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_mC2 %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% filter(!year == 1990) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) # C3 -> class observations as offset mC3 <- gam(obs ~ offset(cobs) + s(year, bs = "tp"), family = nb(), data = df_ss, method = "REML") summary(mC3) plot(mC3) appraise(mC3) # How does it fit? temp <- predict(object = mC3, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mC3$coefficients[1]) df_mC3 <- df_ss df_mC3$fit <- exp(temp$fit[,1] + intercept) df_mC3$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_mC3$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_mC3 %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) # Model D - negative binomial - random effect cell ID # This model doesn't converge in a reasonable timE1 # length(unique(df_ss$eea_cell_code)) # # mD <- gam(obs ~ offset(cobs) + s(eea_cell_code, bs = "re") + s(year), family = nb(), # data = df_ss, method = "REML") # # summary(mD) # plot(mD) # appraise(mD) # # # How does it fit? # temp <- predict(object = mD, newdata = df_ss, type = "iterms", # interval = "prediction", se.fit = TRUE) # intercept <- unname(mD$coefficients[1]) # df_mD <- df_ss # df_mD$fit <- exp(temp$fit[,1] + intercept) # df_mD$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) # df_mD$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) # # df_mD %>% # group_by(year) %>% # summarise(yobs = sum(obs), # yfit = sum(fit), # ylcl = sum(lcl), # yucl = sum(ucl)) %>% # ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + # geom_line(aes(y = yfit), colour = "red") + # geom_ribbon(aes(ymax = yucl, ymin = ylcl), # fill = grey(0.5), # alpha = 0.4) # Model E: with s(x,y) smoother length(unique(df_ss$eea_cell_code)) df_ss <- df_ss %>% left_join(df_xy %>% dplyr::select(eea_cell_code, x, y, natura2000), by = "eea_cell_code") df_ss$eea_cell_code <- as.factor(df_ss$eea_cell_code) df_ss %>% filter(obs > 0) %>% group_by(eea_cell_code, year) %>% summarise(nc = n(), x = first(x), y = first(y)) %>% ggplot(aes(x = x, y = y, colour = nc)) + geom_point() + facet_wrap(~year) # Model mE1 - s(x,y) smoother only mE1 <- gam(obs ~ s(year) + s(x, y, bs = "gp", k = 100, m = c(3, 10000)), family = nb(), data = df_ss, method = "REML") summary(mE1) plot(mE1) appraise(mE1) # How does it fit? temp <- predict(object = mE1, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mE1$coefficients[1]) df_mE1 <- df_ss df_mE1$fit <- exp(temp$fit[,1] + intercept) df_mE1$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_mE1$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_mE1 %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) + ggtitle("long data - nb - s(s,y) only") # Model E2 - with s(x,y) smoother and smoother for class observations mE2 <- gam(obs ~ s(year) + s(cobs, k = 3) + s(x, y, bs = "gp", k = 100, m = c(3, 10)), family = nb(), data = df_ss, method = "REML") summary(mE2) draw(mE2) plot(mE2) appraise(mE2) # How does it fit? temp <- predict(object = mE2, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mE2$coefficients[1]) df_mE2 <- df_ss df_mE2$fit <- exp(temp$fit[,1] + intercept) df_mE2$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_mE2$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_mE2 %>% group_by(year) %>% summarise(yobs = sum(obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = yobs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) + ggtitle("long data - nb - s(x,y) + s(cobs)") ######################################################################"" #Model F - presence absence df_ss %>% group_by(year) %>% summarise(ypa_obs = sum(pa_obs)) %>% ggplot(aes(x = year, y = ypa_obs)) + geom_point() + geom_line() # # Model F1 - year smoother only mF1 <- gam(pa_obs ~ s(year), family = "binomial", data = df_ss, method = "REML") summary(mF1) plot(mF1) appraise(mF1) # How does it fit? temp <- predict(object = mF1, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mF1$coefficients[1]) df_mF1 <- df_ss df_mF1$fit <- 1 / (1 + exp(-(temp$fit[,1] + intercept))) df_mF1$ucl <- 1 / (1 + exp(-(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96))) df_mF1$lcl <- 1 / (1 + exp(-(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96))) df_mF1 %>% group_by(year) %>% summarise(y_pa_obs = sum(pa_obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = y_pa_obs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) # # Model F2 - year smoother + s(x,y) smoother mF2 <- gam(pa_obs ~ s(year) + s(x, y, bs = "gp", k = 100, m = c(3, 10000)), family = "binomial", data = df_ss, method = "REML") summary(mF2) plot(mF2) appraise(mF2) # How does it fit? temp <- predict(object = mF2, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mF2$coefficients[1]) df_mF2 <- df_ss df_mF2$fit <- 1 / (1 + exp(-(temp$fit[,1] + intercept))) df_mF2$ucl <- 1 / (1 + exp(-(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96))) df_mF2$lcl <- 1 / (1 + exp(-(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96))) df_mF2 %>% group_by(year) %>% summarise(y_pa_obs = sum(pa_obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = y_pa_obs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) # Model F3 - year smoother + s(x,y) smoother + pa_cobs smoother mF3 <- gam(pa_obs ~ s(year) + s(cobs) + s(x, y, bs = "gp", k = 100, m = c(3, 10000)), family = "binomial", data = df_ss, method = "REML") summary(mF3) plot(mF3) appraise(mF3) # How does it fit? temp <- predict(object = mF3, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mF3$coefficients[1]) df_mF3 <- df_ss df_mF3$fit <- 1 / (1 + exp(-(temp$fit[,1] + intercept))) df_mF3$ucl <- 1 / (1 + exp(-(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96))) df_mF3$lcl <- 1 / (1 + exp(-(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96))) df_mF3 %>% group_by(year) %>% summarise(y_pa_obs = sum(pa_obs), yfit = sum(fit), ylcl = sum(lcl), yucl = sum(ucl)) %>% ggplot(aes(x = year, y = y_pa_obs)) + geom_point() + geom_line() + geom_line(aes(y = yfit), colour = "red") + geom_ribbon(aes(ymax = yucl, ymin = ylcl), fill = grey(0.5), alpha = 0.4) ###################################################### # GAM - simple time series # # Model SA - negative binomial sA <- gam(obs ~ s(year, bs = "tp"), family = nb(), data = df_sp, method = "REML") summary(sA) plot(sA) appraise(sA) # How does it fit? temp <- predict(object = sA, newdata = df_sp, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(sA$coefficients[1]) df_sA <- df_sp df_sA$fit <- exp(temp$fit[,1] + intercept) df_sA$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_sA$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_sA %>% group_by(year) %>% ggplot(aes(x = year, y = obs)) + geom_point() + geom_line() + geom_line(aes(y = fit), colour = "red") + geom_ribbon(aes(ymax = ucl, ymin = lcl), fill = grey(0.5), alpha = 0.4) + ggtitle("short data - nb") # Model Sb - correct for class observations sB <- gam(obs ~ s(year, bs = "tp") + s(cobs, bs = "tp"), family = nb(), data = df_sp, method = "REML") summary(sB) plot(sB) appraise(sB) # How does it fit? temp <- predict(object = sB, newdata = df_sp, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(sB$coefficients[1]) df_sB <- df_sp df_sB$fit <- exp(temp$fit[,1] + intercept) df_sB$ucl <- exp(temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96) df_sB$lcl <- exp(temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96) df_sB %>% group_by(year) %>% ggplot(aes(x = year, y = obs)) + geom_point() + geom_line() + geom_line(aes(y = fit), colour = "red") + geom_ribbon(aes(ymax = ucl, ymin = lcl), fill = grey(0.5), alpha = 0.4) ## INLA library(INLA) inlaA <- inla(obs ~ s(year, bs = "tp"), data = df_ss, method = "REML") summary(inlamA) plot(mA) temp <- predict(object = mA, newdata = df_ss, type = "iterms", interval = "prediction", se.fit = TRUE) intercept <- unname(mA$coefficients[1]) df_mA <- df_ss df_mA$fit <- temp$fit[,1] + intercept df_mA$ucl <- temp$fit[,1] + intercept + temp$se.fit[,1] * 1.96 df_mA$lcl <- temp$fit[,1] + intercept - temp$se.fit[,1] * 1.96 summary(df_mA) #appraise(mA)

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/data_prep/db_init.R

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RichardHHill/course_planner

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refs/heads/master

2021-07-13T12:29:30.357835

2020-08-08T01:59:40

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db_init.R

library(dplyr) library(RPostgres) Sys.setenv("R_CONFIG_ACTIVE" = "default") app_config <- config::get(file = "shiny_app/config.yml") conn <- tychobratools::db_connect(app_config$db) # DBI::dbExecute(conn, "DROP TABLE IF EXISTS semester_courses") # DBI::dbExecute(conn, "DROP TABLE IF EXISTS majors") # DBI::dbExecute(conn, "DROP TABLE IF EXISTS semester_names") # DBI::dbExecute(conn, "DROP TABLE IF EXISTS input_ids") ids_table_query <- "CREATE TABLE input_ids ( uid VARCHAR(36) PRIMARY KEY, time_created TIMESTAMPTZ NOT NULL DEFAULT NOW(), time_modified TIMESTAMPTZ NOT NULL DEFAULT NOW(), passkey TEXT, name TEXT );" DBI::dbExecute(conn, ids_table_query) semester_names_query <- "CREATE TABLE semester_names ( semester_uid VARCHAR(36), schedule_uid VARCHAR(36) REFERENCES input_ids (uid) ON DELETE CASCADE, semester_name TEXT );" DBI::dbExecute(conn, semester_names_query) semester_courses_query <- "CREATE TABLE semester_courses ( schedule_uid VARCHAR(36) REFERENCES input_ids (uid) ON DELETE CASCADE, semester_uid VARCHAR(36) REFERENCES semester_names (semester_uid) ON DELETE CASCADE, course_code TEXT, course_name TEXT );" DBI::dbExecute(conn, semester_courses_query) majors_query <- "CREATE TABLE majors ( schedule_uid VARCHAR(36) REFERENCES input_ids (uid) ON DELETE CASCADE, code TEXT, name TEXT, major_name TEXT, major_id TEXT );" DBI::dbExecute(conn, majors_query) DBI::dbDisconnect(conn)

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/man/rename_region_code_column.Rd

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FelipeJColon/covidregionaldata

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refs/heads/master

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2021-01-08T17:17:37

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rename_region_code_column.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_region_codes.R \name{rename_region_code_column} \alias{rename_region_code_column} \title{Helper to rename the region code column in each dataset to the correct code type for each country (e.g. ISO-3166-2).} \usage{ rename_region_code_column(data, country) } \arguments{ \item{data}{a data frame with a region_level_1_code column and optionally a region_level_2_code column} \item{country}{a string with the country of interest} } \value{ a tibble with the column(s) renamed to a sensible name } \description{ The package relies on column name 'region_level_1_code' etc. during processing but this often isn't the most sensible name for the column (e.g. iso-3166-2 makes more sense for US states). This simply renames the column as the final step in processing before returning data to the user. }

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/ch3_operctovol_20190102.R

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willking2/barnacle-IPM

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ch3_operctovol_20190102.R

### ch 3 using expt 2.4.1 ch 2 data ### Will King ### comparing operculum length to volume for banracles used in ch 2 mesocosm and field experiments. All barnacles pooled together and chosen randomly for measuring (all temps, meso/field, crowding, size pooled together) ### use this to do regression to predict vol from operc length, for ch 3 probability of reprod ### for operc-to-vol regression, only use data of volume 600mm3 or smaller, b/c that was the range used for the vol-to-prob-reproduction regression in ch 2, which you want to use in ch 3 # ---- load data and packages ---- setwd("~/PhD/PhD_projects/ch 3 IPM/analysis") ov <- read.csv('expt2.4.1_operctovol_20180514.csv', header = T) library(scales) # ----- adjust data part 1 ---- # calculate volume ov$vol <- ov$basal_diameter_mm^2 * ov$height_mm # ----- explore: operc against various measures ---- plot(ov$vol ~ ov$operculum_length_mm) abline(h = 600, lty = 2) # ---- adjust data: limit to 600 mm3 or below ---- ov2 <- ov[ov$vol <= 600, ] plot(ov2$vol ~ ov2$operculum_length_mm) # ---- analyze: vol ~ operc length ---- m1 <- lm(vol ~ operculum_length_mm , data = ov2 ) summary(m1) # ---- plot: vol ~ operc length, for supp fig ---- pdf('plots/operctovol_ch3.pdf', width = 5.5, height = 5.5) par(cex = 1.2, mar = c(4.5, 5, 0.5, 0.5)) # make blank graph plot(vol ~ operculum_length_mm , data = ov2 , pch = 2 , col = alpha('black', 0.55) , axes = F , xlab = '' , ylab = '' , xlim = c(0, 6) , ylim = c(0, 600) ) # add regression line lines(seq(1.15, 5.75, by = 0.01) , predict(m1 , newdata = data.frame(operculum_length_mm = seq(1.15, 5.75, by = 0.01) ) , type = 'response' ) , lwd = 2 ) # axes and labels axis(1 , tck = 0.02 , at = seq(0, 6, by = 1) #, labels = seq(0.5, 6.5) , pos = 0 ) axis(2 , las = 1 , tck = 0.02 , at = seq(0, 600, by = 100) , pos = 0 ) axis(3 , tck = 0.02 , at = seq(0, 6, by = 1) , labels = F , pos = 600 ) axis(4 , tck = 0.02 , at = seq(0, 600, by = 100) , labels = F , pos = 6 ) mtext(expression(paste('Body volume, ', mm^3)) , side = 2, line = 2, cex = 1.2) mtext('Operculum length, mm', side = 1, line = 2, cex = 1.2) dev.off()

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/man/trp.valueMatrix.Rd

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avilesd/productConfig

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refs/heads/master

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trp.valueMatrix.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/TRPFunctions.R \name{trp.valueMatrix} \alias{trp.valueMatrix} \title{Returns a Value Matrix using three reference points} \usage{ trp.valueMatrix(dataset, userid = NULL, attr = NULL, rounds = NULL, cost_ids = NULL, tri.refps = NULL, beta_f = 5, beta_l = 1.5, beta_g = 1, beta_s = 3) } \arguments{ \item{dataset}{data.frame with the user generated data from a product configurator. See \code{decisionMatrix} for specifications of the dataset.} \item{userid}{a vector of integers that gives the information of which users the matrix should be calculated. Vectorised.} \item{attr}{attributes IDs, vector of integer numbers corresponding to the attributes you desire to use; attr are assumed to be 1-indexed.} \item{rounds}{integer vector or text option. Which steps of the configuration process should be shown? Defaults are first and last step. Text options are \code{all, first, last}.} \item{cost_ids}{argument used to convert selected cost attributes into benefit attributes. Integer vector.} \item{tri.refps}{numeric matrix or vector - three numbers per attribute, indicating the minimum requirements, status-quo and the goals for a user (MR, SQ, G).} \item{beta(s)}{numeric arguments representing the psychological impact of an outcome equaling failer (_f), loss (_l), gain (_g) or success (_s). Default values are taken from our reference paper \code{(5,1,1,3)}.} } \value{ a list of value matrices for each user. } \description{ This function is based on the value function of the tri-reference point (trp) theory. It first builds a desicion matrix for each user and then applys the trp-value function over each value using the three given reference points (MR, SQ, G) and other four free parameters from the value function. See references. } \details{ This function is an improvement over \code{\link{trpValueMatrix and trpValueMatrix.oneAttr}} since it allows a matrix to be given through \code{tri.refps}. The matrix should have three columns, first column is for the minimum requirements, second for the status-quo, and third should be for the Goal (MR, SQ, G). It should have as many rows as attributes, i.e. one set of reference points for each attribute. General: The value matrix has ncol = number of attributes you selected or all(default) and nrow = number of rounds you selected or the first and last(default) for all selected users. \code{dataset} We assume the input data.frame has following columns usid = User IDs, round = integers indicating which round the user is in (0-index works best for 'round'), atid = integer column for referring the attribute ID (1 indexed), selected = numeric value of the attribute for a specific, given round, selectable = amount of options the user can chose at a given round, with the current configuration. \code{userid} is a necessary parameter, without it you'll get a warning. Default is NULL. \code{attr} Default calculates with all attributes. Attributes are automatically read from provided dataset, it is important you always provide the complete data so that the package functions properly. Moreover, \code{userid} and \code{attr} will not be sorted and will appear in the order you input them. \code{rounds} Default calculates with first and last rounds (initial and final product configuration). You can give a vector of arbitrarily chosen rounds as well. \code{cost_ids} Default assumes all your attributes are of benefit type, that is a higher value in the attribute means the user is better off than with a lower value. If one or more of the attributes in your data is of cost type, e.g. price, so that lower is better then you should identify this attributes as such, providing their id, they'll be converted to benefit type (higher amount is better). About reference points with cost_ids: For a cost attribute it should be true, that a lower value is better for the user, this should also hold for the three reference points. So contrary to normal/benefit attributes \code{ for cost attributes} reference points should follow that: \code{mr > sq > g}. Note: When converting a cost attribute to a benefit attribute its three reference points change as well, enter the unconverted refps, the function transforms them automatically when it detects a \code{cost_ids != NULL}. But since for cost attributes, lower is better, unconverted they should follow (G < SQ < MR). } \examples{ #Not runnable yet trpValueMatrix(pc_config_data, 9:11, mr = 0.5, sq = 2, g = 4.7) trpValueMatrix(my_data, userid = 11, attr = 1, cost_ids = 1, mr = 10, sq = 5, g =3) # Note that for cost attributes: MR > SQ > G trpValueMatrix(keyboard_data, 60, rounds = "first", attr=1, mr = 0.5, sq = 1.8, g = 2.5, beta_f = 6) trpValueMatrix(data1, 2) # Returns an error since no reference points entered (mr, sq, g) } \references{ [1]Wang, X. T.; Johnson, Joseph G. (2012) \emph{A tri-reference point theory of decision making under risk. }Journal of Experimental Psychology }

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/Test1.R

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ToniBarta/Clustering-data-machine-learning-in-R

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Test1.R

library(RPostgreSQL) library(hash) drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, dbname="momentus_development") dbListConnections(drv) # library(RPostgreSQL) dbGetInfo(drv) summary(con) # rs <- dbSendQuery(con,"select songs_skipped from intra_weather_categories") # fetch(rs,n=-1) ## return all elements dbListFields(con,"intra_time_of_days") # THIS DATA IS FOR INTRA_TIME_OF_DAYS intra_times = dbGetQuery(con,"select distinct user_id, case when variable = 'early morning' then 1 when variable = 'morning' then 2 when variable = 'late morning' then 3 when variable = 'afternoon' then 4 when variable = 'late afternoon' then 5 when variable = 'evening' then 6 when variable = 'night' then 7 when variable = 'late night' then 8 end, (1 - new_song_skipped*1.0/songs_skipped) AS discoverability from intra_time_of_days where songs_skipped != 0") # dada = as.matrix(da) plot(intra_times[3:2]) colnames(intra_times) <- c("user_id", "row_number", "discoverability") # THIS DATA IS FOR INTRA_DAY_OF_WEEKS intra_days = dbGetQuery(con,"select distinct user_id, case when variable = 'monday' then 1 when variable = 'tuesday' then 2 when variable = 'wednesday' then 3 when variable = 'thursday' then 4 when variable = 'friday' then 5 when variable = 'saturday' then 6 when variable = 'sunday' then 7 end, (1 - new_song_skipped*1.0/songs_skipped) AS discoverability from intra_day_of_weeks where songs_skipped != 0") plot(intra_days[3:2]) colnames(intra_days) <- c("user_id", "row_number", "discoverability") # THIS DATA IS FOR INTRA_MONTHS intra_months = dbGetQuery(con,"select distinct user_id, case when variable = 'january' then 1 when variable = 'february' then 2 when variable = 'march' then 3 when variable = 'april' then 4 when variable = 'may' then 5 when variable = 'june' then 6 when variable = 'july' then 7 when variable = 'august' then 8 when variable = 'september' then 9 when variable = 'october' then 10 when variable = 'november' then 11 when variable = 'december' then 12 end, (1 - new_song_skipped*1.0/songs_skipped) AS discoverability from intra_months where songs_skipped != 0") plot(intra_months[3:2]) colnames(intra_months) <- c("user_id", "row_number", "discoverability") # THIS DATA IS FOR INTRA_WEATHER intra_weathers = dbGetQuery(con, "SELECT DISTINCT iw.user_id, w.row_number, (1 - iw.new_song_skipped*1.0/iw.songs_skipped) AS discoverability from intra_weather_categories iw, (SELECT row_number() OVER(ORDER BY category), category FROM weathers GROUP BY category) w WHERE w.category = iw.variable AND iw.songs_skipped != 0 ORDER BY iw.user_id, w.row_number") plot(intra_weathers[3:2]) # THIS DATA IS FOR INTRA_LOCATION intra_locations = dbGetQuery(con, "SELECT DISTINCT ic.user_id, c.row_number, (1 - ic.new_song_skipped*1.0/ic.songs_skipped) AS discoverability from intra_cities ic, (SELECT row_number() OVER(ORDER BY city), city FROM locations GROUP BY city) c WHERE c.city = ic.variable AND ic.songs_skipped != 0 ORDER BY ic.user_id, c.row_number") plot(intra_locations[3:2]) # THIS DATA IS FOR TEMPERATURE intra_temperatures = dbGetQuery(con, "SELECT DISTINCT iw.user_id, w.row_number, (1 - iw.new_song_skipped*1.0/iw.songs_skipped) AS discoverability from intra_temperatures iw, (SELECT row_number() OVER(ORDER BY temperature), temperature FROM weathers GROUP BY temperature) w WHERE w.temperature = iw.variable AND iw.songs_skipped != 0 ORDER BY iw.user_id, w.row_number") plot(intra_temperatures[3:2]) # @@@@@@@@@@@@@ FUNCTION TO RETURN A 3d MATRIX BASED ON VARIABLE @@@@@@@@@@@@@@@@ getInfoBasedOnVariables <- function(intra_matrix){ ordered_intra = intra_matrix[with(intra_matrix, order(row_number)), ] j = 1 intra_3dMatrix = array(0, dim=c(max(ordered_intra$row_number),1000,2)) rowCount = ordered_intra$row_number[1] for (i in 1:(nrow(ordered_intra) - 1)){ if (ordered_intra$row_number[i] == ordered_intra$row_number[i+1]){ intra_3dMatrix[ordered_intra$row_number[i], j, 1] = ordered_intra[["user_id"]][i] intra_3dMatrix[ordered_intra$row_number[i], j, 2] = ordered_intra[["discoverability"]][i] j = j + 1 } else{ intra_3dMatrix[ordered_intra$row_number[i], j, 1] = ordered_intra[["user_id"]][i] intra_3dMatrix[ordered_intra$row_number[i], j, 2] = ordered_intra[["discoverability"]][i] rowCount = rowCount + 1 j = 1 } if (i == (nrow(ordered_intra) - 1)){ intra_3dMatrix[ordered_intra$row_number[i],j,1] = ordered_intra[["user_id"]][i + 1] intra_3dMatrix[ordered_intra$row_number[i],j,2] = ordered_intra[["discoverability"]][i + 1] } } return(intra_3dMatrix) } # @@@@@@@@@@@@@@@@@@@@@@@@@@ END OF FUNCTION @@@@@@@@@@@@@@@@@@@@@@@@@@ intra_times_3dMatrix = getInfoBasedOnVariables(intra_times) # to get rid of the 0's from the matrixs # numberMatrix <- intra_3dMatrix[ , rowSums(abs(intra_3dMatrix[, ,]))>0 & rowSums(abs(intra_3dMatrix[, ,]))>0, ] # @@@@@@@@@@@@@ FUNCTION TO RETURN A 3d MATRIX BASED ON USERS @@@@@@@@@@@@@ getInfoBasedOnUsers <- function(intra_matrix){ ordered_intra = intra_matrix[with(intra_matrix, order(user_id)), ] # TODO set up the actual array size userIdArray <<- array(0, 500) user3DMatrix = array(0, dim=c(400,20,2) ) count = 1 arrayCount = 1 j = 1 for (i in 1:(nrow(ordered_intra) - 1)){ if ( i == 1){ userIdArray[arrayCount] <<- ordered_intra[["user_id"]][i] arrayCount = arrayCount + 1 } if (ordered_intra$user_id[i] == ordered_intra$user_id[i+1]){ user3DMatrix[count, j, 1] = ordered_intra[["row_number"]][i] user3DMatrix[count, j, 2] = ordered_intra[["discoverability"]][i] j = j + 1 } else{ if (i != 1) { userIdArray[arrayCount] <<- ordered_intra[["user_id"]][i + 1] arrayCount = arrayCount + 1 } user3DMatrix[count, j, 1] = ordered_intra[["row_number"]][i] user3DMatrix[count, j, 2] = ordered_intra[["discoverability"]][i] count = count + 1 j = 1 } if (i == (nrow(ordered_intra) - 1)){ user3DMatrix[count,j,1] = ordered_intra[["row_number"]][i + 1] user3DMatrix[count,j,2] = ordered_intra[["discoverability"]][i + 1] } } return(user3DMatrix) } # @@@@@@@@@@@@@@@@@@@@@@@@@@ END OF FUNCTION @@@@@@@@@@@@@@@@@@@@@@@@@@ intra_times_user_3dMatrix = getInfoBasedOnUsers(intra_times) intra_times_userID = userIdArray intra_days_user_3dMatrix = getInfoBasedOnUsers(intra_days) intra_days_userID = userIdArray intra_months_user_3dMatrix = getInfoBasedOnUsers(intra_months) intra_months_userID = userIdArray intra_weathers_user_3dMatrix = getInfoBasedOnUsers(intra_weathers) intra_weathers_userID = userIdArray intra_locations_user_3dMatrix = getInfoBasedOnUsers(intra_locations) intra_locations_userID = userIdArray intra_temperatures_user_3dMatrix = getInfoBasedOnUsers(intra_temperatures) intra_temperatures_userID = userIdArray # user3DMatrix[1, ,] # numberMatrix <- user3DMatrix[ 1 , rowSums(abs(user3DMatrix[1 , ,]))>0 & rowSums(abs(user3DMatrix[1 , ,]))>0, ] # plot(numberMatrix) numberMatrix <- intra_times_user_3dMatrix[ 1 , rowSums(abs(intra_times_user_3dMatrix[1 , ,]))>0 & rowSums(abs(intra_times_user_3dMatrix[1 , ,]))>0, ] numberMatrix <- intra_days_user_3dMatrix[ 3 , rowSums(abs(intra_days_user_3dMatrix[3 , ,]))>0 & rowSums(abs(intra_days_user_3dMatrix[3 , ,]))>0, ] kmeansClustering <- function(intra_matrix, nr_clusters, nr_iterations, nr_start){ da <- intra_matrix[3:2] cl <- kmeans(da, nr_clusters, iter.max = nr_iterations, nstart = nr_start) #pdf('intra_temperature_cluster') plot(da, col=cl$cluster) require(graphics) points(cl$centers, col = 1:15, pch = 8) #dev.off() return(cl) } # intra_times_cluster = kmeansClustering(intra_times, 24, 50, 50) # intra_days_cluster = kmeansClustering(intra_days, 14, 50, 50) # intra_months_cluster = kmeansClustering(intra_months, 30, 50, 50) # # intra_locations_cluster = kmeansClustering(intra_locations, 50, 50, 50) # # intra_weathers_cluster = kmeansClustering(intra_weathers, 10, 100, 100) # intra_temperatures_cluster = kmeansClustering(intra_temperatures, 50, 50 ,50) # GETTING the users that are in the same cluster