pierreqi/r_code_50k · Datasets at Hugging Face

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nwfsc-timeseries/MARSS

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Covar--Covar_sec6_01_set-up-seasonal-dat.R

################################################### ### code chunk number 11: Covar_sec6_01_set-up-seasonal-dat ################################################### years <- fulldat[, "Year"] >= 1965 & fulldat[, "Year"] < 1975 phytos <- c( "Diatoms", "Greens", "Bluegreens", "Unicells", "Other.algae" ) dat <- t(fulldat[years, phytos]) # z.score data again because we changed the mean when we subsampled dat <- zscore(dat) # number of time periods/samples TT <- ncol(dat)

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/scripts/pipeline/import_plates.R

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tanaylab/tet-gastrulation

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import_plates.R

import_plates <- function(mat_nm, metadata, base_dir = "data/umi.tables/") { metadata$Amp.Batch.ID <- metadata$plate metadata$Seq.Batch.ID <- metadata$plate metadata$Batch.Set.ID <- metadata$plate write.table(x = metadata, file = paste("config/key_", mat_nm, ".txt", sep = ""), quote = F, sep = "\t", row.names = F) mcell_import_multi_mars( mat_nm = mat_nm, dataset_table_fn = paste("config/key_", mat_nm, ".txt", sep = ""), base_dir = base_dir, patch_cell_name = F, force = TRUE ) mat <- scdb_mat(mat_nm) nms <- c(rownames(mat@mat), rownames(mat@ignore_gmat)) bad_genes <- c(grep("^mt\\-", nms, v = T), "Neat1", grep("ERCC", nms, v = T), "Atpase6", "Xist", "Malat1", "Cytb", "AK018753", "AK140265", "AK163440", "DQ539915") mat <- scm_ignore_genes(scmat = mat, ig_genes = bad_genes) # ignore small and large cells small_cells <- colnames(mat@mat)[colSums(mat@mat) < 2000] large_cells <- colnames(mat@mat)[colSums(mat@mat) > 12000] mat <- scm_ignore_cells(scmat = mat, ig_cells = c(small_cells, large_cells)) # read metadata mat_md <- mat@cell_metadata mat_md$cell <- rownames(mat@cell_metadata) f <- colnames(mat_md) %in% c("molecule", "spike_count") mat_md <- mat_md[, !f] metadata_cells <- read.table(file = "data/metadata_cells_scrna_sequencing.txt", h = T, sep = "\t", stringsAsFactors = F) metadata_cells <- metadata_cells[colnames(metadata_cells) != "plate"] mat_md <- left_join(mat_md, metadata_cells, by = "cell") rownames(mat_md) <- mat_md$cell mat@cell_metadata <- mat_md # ignore empty wells and wells with duplicate cells new_ignore_cells <- unique(c(mat_md$cell[mat_md$embryo %in% c("empty", "duplicate")], mat@ignore_cells)) mat <- scm_ignore_cells(scmat = mat, ig_cells = new_ignore_cells) scdb_add_mat(id = mat_nm, mat = mat) file.remove(paste("config/key_", mat_nm, ".txt", sep = "")) }

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/man/plot.apc.Rd

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volkerschmid/bamp

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plot.apc.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_apc.R \name{plot.apc} \alias{plot.apc} \title{Plot apc object} \usage{ \method{plot}{apc}(x, quantiles = c(0.05, 0.5, 0.95), ...) } \arguments{ \item{x}{apc object} \item{quantiles}{quantiles to plot. Default: \code{c(0.05,0.5,0.95)} is median and 90\% credible interval.} \item{...}{Additional arguments will be ignored} } \value{ plot } \description{ Plot apc object } \details{ Plot of age, period and cohort effects from apc objects. If covariates have been used for period/cohort, a second plot with covariate, absolute effect and relative effect is created. Absolute effect is relative effect times covariate. } \examples{ \dontrun{ data(apc) model <- bamp(cases, population, age="rw1", period="rw1", cohort="rw1", periods_per_agegroup = 5) plot(model) } }

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/r/scripts/probit_logit.R

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rotabori/project_ad_analisis_datos

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probit_logit.R

## PROJECT: ANALISIS DE DATOS ## PROGRAM: probit_logit.r ## PROGRAM TASK: REGRESION LINEAL ## AUTHOR: RODRIGO TABORDA ## AUTHOR: JUAN PABLO MONTENEGRO ## DATE CREATEC: 2020/06/02 ## DATE REVISION 1: ## DATE REVISION #: ####################################################################; ## #0 PROGRAM SETUP ####################################################################; rm(list=ls()) ## #0.1 ## SET PATH FOR READING/SAVING DATA; # setwd() ### #0.2 ## INSTALL PACKAGES; # #SOLO ES NECESARIO INSTALARLOS UNA VEZ # # install.packages("margins") # #PAQUETE PARA EFECTOS MARGINALES # # install.packages("ggplot2") # #PAQUETE PARA GRAFICOS # # install.packages("readstata13") # #PAQUETE PARA LECTURA ARCHIVO STATA13 # ## #0.3 ## CALL PACKAGES; #ES NECESARIO LLAMARLOS CADA RUTINA library(margins) #PAQUETE PARA EFECTOS MARGINALES library(ggplot2) #PAQUETE PARA GR?FICOS library(readstata13) #PAQUETE PARA LECTURA ARCHIVO STATA13 ####################################################################; ## #20 ## DATOS IEFIC; ####################################################################; ## #20.1 ## DATA-IN; iefic <- read.dta13("http://www.rodrigotaborda.com/ad/data/iefic/2016/iefic_2016_s13.dta") # iefic <- na.omit(iefic) # REMOVE N.A. OBSERVATIONS iefic01 <- iefic[c("p2540", "ingreso")] iefic01 <- na.omit(iefic01) ## #20.2 ## REGRESION LINEAL; ## #20.3 ## REGRESION LOGIT; logit01 <- glm(data = iefic01, formula = p2540 ~ ingreso, family = "binomial") summary(logit01) ## #20.3.1 ## VALORES PREDICHOS; logit01_b <- logit01$coefficients ingreso_pred <- c(seq(from = 0, to = 20000000, by = 500000)) logit01_xb <- matrix(data = 0, ncol = 1, nrow = length(ingreso_pred)) for (i in 1:length(ingreso_pred)) { logit01_xb[i,] <- logit01_b[1] + logit01_b[2]*ingreso_pred[i] } logit01_pred <- exp(logit01_xb)/(1+exp(logit01_xb)) plot(logit01_pred, type = "l") ## #20.3.2 ## EFECTO MARGINAL; logit01_mgeff <- (exp(logit01_xb)/(1+exp(logit01_xb))^2) * logit01_b[2] plot(logit01_mgeff, type = "l") ## #20.3.4 ## EFECTO MARGINAL + MARGINS; summary(margins(logit01, variables = "ingreso")) logit01_mgeff01 <- margins(logit01, at = list(ingreso = seq(from = 0, to = 20000000, by = 500000))) cplot(logit01, "ingreso", what = "prediction", main = "Probabilidad predicha") #PROBABILIDAD PREDICHA cplot(logit01, "ingreso", what = "effect", main = "Efecto marginal", draw = T) #EFECTO MARGINAL ####################################################################; ## #99 CLEAN ####################################################################; rm(list=ls())

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/R Programming/Week 4/Week 4.R

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meethariprasad/Data-Science

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Week 4.R

# Generating random variables from normal distribution x = rnorm(10) #generate random numbers from normal distribution x summary(x) x = rnorm(10, 20, 2) #generate random numbers from normal distribution with # mean = 20, sd = 2 x summary(x) # Generate random numbers from a linear model, where x, the predictor, is a normal distribution set.seed(20) x = rnorm(100) # standard normal distribution; mean=0, sd=1 e = rnorm(100, 0, 2) # standard normal distribution; mean=0, sd=2 y = 0.5 + 2 * x + e # the linear model summary(y) plot(x,y) # Generate random numbers from a linear model, where x, the predictor, is a binomial distribution set.seed(20) x = rbinom(100, 1, 0.5) # binomial distribution e = rnorm(100, 0, 2) y = 0.5 + 2 * x + e summary(y) plot(x,y) # R profiling system.time(readLines("http://www.jhsph.edu")) hilbert = function(n) { i = 1:n 1/ outer (i-1, i, "+") } x = hilbert(1000) system.time(svd(x)) hilbert = function(n) # R profiling using the Rprof { i = 1:n 1/ outer (i-1, i, "+") } x = hilbert(1000) Rprof("hilbert") y = (svd(x)) Rprof(NULL) summaryRprof("hilbert")

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/man/checkYearValidity.Rd

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gsimchoni/yrbss

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checkYearValidity.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/checkYearValidity.R \name{checkYearValidity} \alias{checkYearValidity} \title{Check the validity of a year} \usage{ checkYearValidity(year) } \arguments{ \item{year}{a four-digit number representing the desired year} } \value{ an ERROR if the year is not in the yrbss_data_binary dataset, the year itself if it is valid, and 2015 if it is NULL. } \description{ Helper method to check the validity of a year } \examples{ checkLocationValidity("CA") }

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/plot4.r

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antekai/ExploratoryDataAnalysis

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plot4.r

## Getting data (reading the txt file data as powerDF, ## note for R: T==TRUE,F==FALSE) powerDF <- read.table( "c:/household_power_consumption.txt", header = T, sep = ";", na.strings = "?", stringsAsFactors = F, colClasses = c("character","character","numeric","numeric", "numeric","numeric","numeric","numeric","numeric")) ## creating the DateTime variable powerDF$DateTime <- strptime(paste(powerDF$Date, powerDF$Time), format="%d/%m/%Y %H:%M:%S") ## Cleaning Data (subsetting on required dates) powerDF$Date <- as.Date(powerDF$Date, format="%d/%m/%Y") power2plot <- subset(powerDF, powerDF$Date == "2007-02-01" | powerDF$Date == "2007-02-02") ## Exploratory Data Analysis (creating the plot) par(mfrow = c(2, 2), ps = 12,bg=NA) with(power2plot, { plot(power2plot$DateTime, power2plot$Global_active_power, type = "l", ylab = "Global Active Power", xlab = "") plot(power2plot$DateTime, power2plot$Voltage, type = "l", xlab = "datetime", ylab = "Voltage") plot(power2plot$DateTime, power2plot$Sub_metering_1, type = "l", col = "black", ylab = "Energy sub metering", xlab = "") lines(power2plot$DateTime, power2plot$Sub_metering_2, type = "l", col = "red") lines(power2plot$DateTime, power2plot$Sub_metering_3, type = "l", col = "blue") legend("topright", col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty = 1, pt.cex = .5, cex = .5, box.lty = 1, box.lwd = 1, bty = "n") plot(power2plot$DateTime, power2plot$Global_reactive_power, type = "l", xlab = "datetime", ylab = "Global_reactive_power") }) ## saving the plot as .png file & configuring dimensions (same as example plots) dev.copy(png, "plot4.png", units="px", width = 504, height = 504) dev.off()

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/steam/scripts/colourspace_transform.r

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emcake/emcake.github.io

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refs/heads/master

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colourspace_transform.r

xyzpiv <- function( n ) { #return (n > 0.04045 ? Math.Pow((n + 0.055) / 1.055, 2.4) : n / 12.92) * 100.0; x <- if (n > 0.04045) ((n+0.055)/1.055)^2.4 else n/12.92; return (x * 100); } rgb2xyz2 <- function( r, g, b ) { rp = xyzpiv( r ); gp = xyzpiv( g ); bp = xyzpiv( b ); X<- rp*0.4124 + gp*0.3576 + bp*0.1805; Y<- rp*0.2126 + gp*0.7152 + bp*0.0722; Z<- rp*0.0193 + gp*0.1192 + bp*0.9505; return (c(X, Y, Z)); } rgb2xyz <- function( i ) { return (rgb2xyz2( i[1], i[2], i[3])); } rgb2xyzM <- function( M ) { retM = M; d = dim( M ); for ( x in 1:d[1] ) { for( y in 1:d[2] ) { retM[x,y,] = rgb2xyz( M[x,y,] ); } } return (retM ); } labpiv <- function( n ) { eps = 216/24389; kap = 24389/27; p <- if(n < eps) (kap * n + 16) / 116 else n^ (1/3); return(p) } xyz2lab2 <- function( x, y, z ) { white = rgb2xyz2(1, 1, 1); xn = labpiv(x / white[1]); yn = labpiv(y / white[2]); zn = labpiv(z / white[3]); L = max(0, 116*yn - 16); A = 500*(xn-yn); B = 200*(yn-zn); return(c(L, A, B)); } xyz2lab <- function( i ) { return (xyz2lab2( i[1], i[2], i[3] )); } rgb2lab <- function( r, g, b ) { xyz = rgb2xyz( r, g, b ); return (xyz2lab( xyz )); } rgb2labM <- function( M ) { retM = M; d = dim(M); for ( x in 1:d[1] ) { for( y in 1:d[2] ) { retM[x,y,] = xyz2lab( rgb2xyz( M[x,y,] ) ); } } return( retM ); } rgb2i <- function( M ) { d = dim(M); retM = matrix( data=NA, nrow=d[1], ncol=d[2] ); for ( x in 1:d[1] ) { for( y in 1:d[2] ) { retM[x,y] = mean(M[x,y,]); } } return (retM); } rgb2xyz.Image <- function( i ) { lab = rgb2xyz( i[,,1], i[,,2], i[,,3] ); d=dim( i ); return (Image( lab, dim=d, colormode=Color )); } rgb2lab.Image <- function( i ) { lab = rgb2lab( i[,,1], i[,,2], i[,,3] ); d=dim( i ); return (Image( lab, dim=d, colormode=Color )); }

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/r-packages/omop-utils/man/typedTbl.Rd

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terminological/etl-analysis

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typedTbl.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utilityFunctions.R \name{typedTbl} \alias{typedTbl} \title{#' Normalise an omop dataframe to a consistent format to construct a single feature set accross #' #' with the following columns: #' #' * cohort_person_id, #' * cohort_entry_datetime, #' * feature_source, e.g. measurement, observation, etc... #' * feature_name, #' * feature_concept_id, #' * feature_value_as_number, #' * feature_value_as_date, #' * feature_value_as_name, #' * feature_value_as_concept_id, #' * feature_days_offset, #' * feature_display #' #' or the equivalent with observation_ prefix #' #' @param omopDf - a df which may be a dbplyr table #' @param outcome - is the #' @return a dbplyr dataframe normaliseColumns = function(omopDf, prefix = "feature") { omopDf = omopDf %>% omop$inferType() if (""class(omopDf) omopDf = omopDf %>% mutate(prefix_source = ) %>% rename( prefix_datetime = any_of(c("")), prefix_concept_id = any_of(c("")), prefix_value_as_number = any_of(c("")), prefix_value_as_number_min = any_of(c("")), prefix_value_as_number_max = any_of(c("")), prefix_value_as_number_unit = any_of(c("")), prefix_value_as_date = any_of(c("")), prefix_value_as_name = any_of(c("")), prefix_value_as_concept_id = any_of(c("")), prefix_days_offset = days_offset, prefix_display = c() ) replaceList = colnames(omopDf) %>% stringr::str_replace("prefix",prefix) replaceList = replaceList[replaceList != colnames(omopDf)] names(replaceList) = colnames(omopDf) omopDf = omopDf %>% rename(replaceList) return(omopDf) #x = tibble(y=c(1,2,3),w=c(3,2,1)) #x %>% select(y,z=any_of(c("a","c")))#,"w","d","y"))) }} \usage{ typedTbl(con, tableName) } \arguments{ \item{con}{- an omop database connection} \item{tableName}{- the table name} } \value{ the dbplyr table with addition class information } \description{ c("care_site","cdm_source","concept","concept_ancestor","concept_class","concept_relationship","concept_synonym","condition_era", "condition_occurrence","cost","device_exposure","domain","dose_era","drug_era","drug_exposure","drug_strength", "fact_relationship","location","location_history","measurement","metadata","note","note_nlp","observation","observation_period", "payer_plan_period","persist","persistance","person","procedure_occurrence","provider","relationship","source_to_concept_map", "specimen","survey_conduct","visit_detail","visit_occurrence","vocabulary") } \details{ a dbplyr table with addition class information }

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/שיעור2.R

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yosefLuzon/EXmacine

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שיעור2.R

v1<-c(1,2,3,4,5,6) v2<-c(7,8,9,10,11,12) mat1 <- rbind(v1,v2) mat2<-cbind(v1,v2) mat1 class<-(mat1) #random numbers # normal distu dnorm(0,0,0) qnorm(0.5,mean=0,sd=1) #הסתברות שהתוצאה תהיה קטנה או שווה ל2 pnorm(1,0,1) #דגימה מיתוך התפלגות נורמאלית rnorm(10,0,1) # דגימות-10, התפלגות נורמאלית-ממוצע 0 mean(rnorm(1000000,0,1))#חוק מספרים הגדולים #דגימה מיתוך התפלגות אחידה דיפולטיבי מינימום 0 מקסימום 1 e<-runif(5)*10 #גרפים data<-matrix(c(1:6,c(10,11,15,17,55,78)),6,2) #יצירת גרף של עמודה 1 מול עמודה 2 plot(data[,1], data[,2]) data #עמ ליצור קו נוסיף סוג L, עמ לנ=התחיל מנקודה מסוימת נוסיף משתנה של גבול. plot(data[,1], data[,2],type = "l", ylim=c(0,60)) #היסטורגמה #דגימות מהתפלגות אחידה u<-runif(1000,0,100) #בניית ההיסטוגרם hist(u, ylim=c(1,50)) n<-rnorm(1000,0,100) hist(n) #שני גרפים אחד ליד השני חייב להריץ את הפונקצייה par par(mfrow=c(2,1)) plot(seq(-4,4,0.01), dnorm(seq(-4,4,0.01)),type="l") plot(seq(-4,4,0.1), dnorm(seq(-4,4,0.1),mean=0,sd=0.5),type="l") #יצירת פונקצייה toFar<-function(cel){ x<-cel*1.8 x+32} #אחרי הכתיבה של הפונקצייה נריץ אותה על מנת שתהיה זמינה toFar(33) toFar1<-function(cel){ x<-cel*1.8+32 return(x)} toFar1(33) #פונקציית גזירה derivf<-function(x){ (f(x+0.1)-f(x))/0.01 } f<-function(x){ x^3+X+10 } derivf(5) #lists l<-list(owner='jack', sum=3000) l[[1]] #$notation l$sum #data frame מיבנה נתונים כמו DB כאשר כל ווקטור במטריצה יכול להחזיק סוג מידע אחרכל עמודה חייבת להיות מאותו סוג. brands<-c('Ford','Mazda','Fiat') from<-c('us','japan','italy') rank<-c(3,2,1) cars<-data.frame(brands,from,rank) cars$brands #levels? האם הם חלק מקבוצה מובנית של 3 אבריםאו שזה שלושה ערכים. cars$rank typeof(cars$from) #filters brands.filter<-cars$brands=='Fiat' cars[brands.filter,1:2] #מידע על הרשימה summary(cars) #lavel שr נתן לערכים ברשימה str(cars) #איך מיבאים קובץ מבחוץ? worms<-read.table("worms.txt", header = T) worms$Damp typeof(worms$Damp)

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/man/coalesce_values.Rd

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ying14/yingtools2

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d3056b6fe8a906311639c75c0dd456f18fe0ff7c

refs/heads/master

2023-08-16T21:14:22.291830

2023-08-14T20:02:58

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coalesce_values.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/yingtools2.R \name{coalesce_values} \alias{coalesce_values} \title{Coalesce values into one summary variable.} \usage{ coalesce_values(..., sep = "=", collapse = "|", omit.na = FALSE) } \arguments{ \item{...}{variables to coalesce together.} \item{sep}{character string separating variable name and value. Default is "="} \item{collapse}{character string separating variable/value pairs. Default is "|"} \item{omit.na}{whether or not to remove variable in the case of NA.} } \value{ A character vector of same length as the variables, displaying variable names plus values. } \description{ Summarize the value of several variables in a single character vector by concatenating variable name and values. } \examples{ cid.patients \%>\% mutate(demographics=coalesce_values(agebmt>60,sex,race)) \%>\% count(demographics,sort=TRUE) }

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/inst/templates/bs4Dash/boilerplates/controlbar.R

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RinteRface/RinteRfaceVerse

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refs/heads/master

2020-05-06T14:07:12.335036

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controlbar.R

controlbar <- bs4DashControlbar(skin = "dark", title = NULL, width = 250)

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/scratchpad/cluster_old.R

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emilieea88/beer-data-science

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refs/heads/master

2022-01-08T05:21:04.217954

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cluster_old.R

# source("./run_it.R") # source("./read_from_db.R") source("./most_popular_styles.R") library(NbClust) # ------------------- kmeans ------------ # only using top beer styles # select only predictor and outcome columns, take out NAs, and scale the data beer_for_clustering <- popular_beer_dat %>% select(name, style, styleId, style_collapsed, abv, ibu, srm) %>% # not very many beers have SRM so may not want to omit based on it... na.omit() %>% filter( abv < 20 & abv > 3 ) %>% filter( ibu < 200 ) beer_for_clustering_predictors <- beer_for_clustering %>% select(abv, ibu, srm) %>% rename( abv_scaled = abv, ibu_scaled = ibu, srm_scaled = srm ) %>% scale() %>% as_tibble() # # take out outliers # beer_for_clustering <- beer_for_clustering_w_scaled %>% # filter( # abv_scaled < 5 & abv_scaled > -2 # take out the nonalcoholic beers # ) %>% # filter( # ibu_scaled < 5 # ) # beer_for_clustering <- bind_cols(beer_for_clustering, beer_for_clustering_w_scaled) # beer_for_clustering_predictors <- beer_for_clustering %>% # select( # abv_scaled, ibu_scaled, srm_scaled # ) # # separate into predictors and outcomes and scale the predictors # beer_for_clustering_predictors_w_outliers <- beer_for_clustering %>% select(abv, ibu, srm) %>% rename( # abv_scaled = abv, # ibu_scaled = ibu, # srm_scaled = srm # ) %>% scale() %>% # as_tibble() # take out some abv and ibu outliers from the clustered beer data # filter( # !(ibu > 300) # take out outliers # ) %>% # filter( # !(abv > 20) # ) beer_for_clustering_outcome <- beer_for_clustering %>% select(name, style, styleId, style_collapsed) # what's the optimal number of clusters? # nb <- NbClust(beer_for_clustering_predictors, distance = "euclidean", # min.nc = 2, max.nc = 15, method = "kmeans") # hist(nb$Best.nc[1,], breaks = max(na.omit(nb$Best.nc[1,]))) # do clustering set.seed(9) clustered_beer_out <- kmeans(x = beer_for_clustering_predictors, centers = 10, trace = TRUE) clustered_beer <- as_tibble(data.frame(cluster_assignment = factor(clustered_beer_out$cluster), beer_for_clustering_outcome, beer_for_clustering_predictors, beer_for_clustering %>% select(abv, ibu, srm))) # the three combinations of plots clustered_beer_plot_abv_ibu <- ggplot(data = clustered_beer, aes(x = abv, y = ibu, colour = cluster_assignment)) + geom_jitter() + theme_minimal() + ggtitle("k-Means Clustering of Beer by ABV, IBU, SRM") + labs(x = "ABV", y = "IBU") + labs(colour = "Cluster Assignment") clustered_beer_plot_abv_ibu clustered_beer_plot_abv_srm <- ggplot(data = clustered_beer, aes(x = abv, y = srm, colour = cluster_assignment)) + geom_jitter() + theme_minimal() + ggtitle("k-Means Clustering of Beer by ABV, IBU, SRM") + labs(x = "ABV", y = "SRM") + labs(colour = "Cluster Assignment") clustered_beer_plot_abv_srm clustered_beer_plot_ibu_srm <- ggplot(data = clustered_beer, aes(x = ibu, y = srm, colour = cluster_assignment)) + geom_jitter() + theme_minimal() + ggtitle("k-Means Clustering of Beer by ABV, IBU, SRM") + labs(x = "IBU", y = "SRM") + labs(colour = "Cluster Assignment") clustered_beer_plot_ibu_srm # take a look at individual clusters cluster_1 <- clustered_beer %>% filter(cluster_assignment == "1") cluster_1 cluster_6 <- clustered_beer %>% filter(cluster_assignment == "6") cluster_6 cluster_9 <- clustered_beer %>% filter(cluster_assignment == "9") cluster_9 # see how styles clustered themselves # table of counts cluster_table_counts <- table(style = clustered_beer$style_collapsed, cluster = clustered_beer$cluster_assignment) # cb_spread <- clustered_beer %>% select( # cluster_assignment, style # ) %>% group_by(cluster_assignment) %>% # spread(key = cluster_assignment, value = style, convert = TRUE) # tsne # library(tsne) # # cb <- clustered_beer %>% sample_n(100) # # colors = rainbow(length(unique(cb$style))) # names(colors) = unique(cb$style) # # ecb = function (x,y) { # plot(x,t='n'); # text(x, labels=cb$style, col=colors[cb$style]) } # # tsne_beer = tsne(cb[,4:6], epoch_callback = ecb, perplexity=20) # # # ---------- functionize -------- source("./most_popular_styles.R") library(NbClust) # only using top beer styles # select only predictor and outcome columns, take out NAs, and scale the data cluster_it <- function(df, preds, to_scale, resp, n_centers) { df_for_clustering <- df %>% select_(.dots = c(response_vars, cluster_on)) %>% na.omit() %>% filter( abv < 20 & abv > 3 ) %>% filter( ibu < 200 ) df_all_preds <- df_for_clustering %>% select_(.dots = preds) df_preds_scale <- df_all_preds %>% select_(.dots = to_scale) %>% rename( abv_scaled = abv, ibu_scaled = ibu, srm_scaled = srm ) %>% scale() %>% as_tibble() df_preds <- bind_cols(df_preds_scale, df_all_preds[, (!names(df_all_preds) %in% to_scale)]) df_outcome <- df_for_clustering %>% select_(.dots = resp) %>% na.omit() set.seed(9) clustered_df_out <- kmeans(x = df_preds, centers = n_centers, trace = TRUE) clustered_df <- as_tibble(data.frame( cluster_assignment = factor(clustered_df_out$cluster), df_outcome, df_preds, df_for_clustering %>% select(abv, ibu, srm))) return(clustered_df) } # ----------- main clustering into 10 clusters ------- cluster_on <- c("abv", "ibu", "srm") to_scale <- c("abv", "ibu", "srm") response_vars <- c("name", "style", "styleId", "style_collapsed") clustered_beer <- cluster_it(df = popular_beer_dat, preds = cluster_on, to_scale = to_scale, resp = response_vars, n_centers = 10) # ----------------- pared styles ----------------- styles_to_keep <- c("Blonde", "India Pale Ale", "Stout", "Tripel", "Wheat") bn_certain_styles <- beer_ingredients_join %>% filter( style_collapsed %in% styles_to_keep ) cluster_on <- c("abv", "ibu", "srm", "total_hops", "total_malt") to_scale <- c("abv", "ibu", "srm") response_vars <- c("name", "style", "style_collapsed") certain_styles_clustered <- cluster_it(df = bn_certain_styles, preds = cluster_on, to_scale = to_scale, resp = response_vars, n_centers = 5) table(style = certain_styles_clustered$style_collapsed, cluster = certain_styles_clustered$cluster_assignment) ggplot() + geom_point(data = certain_styles_clustered, aes(x = abv, y = ibu, shape = cluster_assignment, colour = style_collapsed), alpha = 0.5) + geom_point(data = style_centers_certain_styles, aes(mean_abv, mean_ibu), colour = "black") + geom_text_repel(data = style_centers_certain_styles, aes(mean_abv, mean_ibu, label = style_collapsed), box.padding = unit(0.45, "lines"), family = "Calibri", label.size = 0.3) + ggtitle("Selected Styles (colors) matched with Cluster Assignments (shapes)") + labs(x = "ABV", y = "IBU") + labs(colour = "Style") + theme_bw() # ** cluster_prep <- function(df, preds, to_scale, resp) { # browser() df_for_clustering <- df %>% select_(.dots = c(response_vars, cluster_on)) %>% na.omit() %>% filter( abv < 20 & abv > 3 # Only keep beers with ABV between 3 and 20 and an IBU less than 200 ) %>% filter( ibu < 200 ) df_all_preds <- df_for_clustering %>% select_(.dots = preds) df_preds_scale <- df_all_preds %>% select_(.dots = to_scale) %>% rename( abv_scaled = abv, ibu_scaled = ibu, srm_scaled = srm ) %>% scale() %>% as_tibble() df_preds <- bind_cols(df_preds_scale, df_all_preds[, (!names(df_all_preds) %in% to_scale)]) df_outcome <- df_for_clustering %>% select_(.dots = resp) %>% na.omit() cluster_prep_out <- list(preds = df_preds, outcome = df_outcome) return(cluster_prep_out) } cluster_on <- c("abv", "ibu", "srm", "total_hops", "total_malt") to_scale <- c("abv", "ibu", "srm", "total_hops", "total_malt") response_vars <- c("name", "style", "style_collapsed") cp <- cluster_prep(df = beer_totals, preds = cluster_on, to_scale = to_scale, resp = response_vars)

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/resources/geneAnnotations/Exome/b37/getCanonicalTargets.R

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soccin/seqCNA

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2023-09-03T08:45:57.762043

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getCanonicalTargets.R

library(tidyverse) library(data.table) library(magrittr) library(stringr) ISOFORM0="/opt/common/CentOS_6-dev/vcf2maf/v1.6.12/data/isoform_overrides_at_mskcc" ISOFORM1="/opt/common/CentOS_6-dev/vcf2maf/v1.6.12/data/isoform_overrides_uniprot" isoform0=read_tsv(ISOFORM0, col_names=c("TID","gene_name","refseq_id","ccds_id"),skip=1) isoform1=read_tsv(ISOFORM1, col_names=c("TID","gene_name","refseq_id","ccds_id"),skip=1) isoforms=bind_rows(isoform0,isoform1) %>% distinct(gene_name,.keep_all=T) canonicalTranscripts = isoforms %>% distinct(TID) %>% pull # canonical=read_tsv("ucsc_mm10_knownCanonical.txt.gz") # canonicalTranscriptsUCSC=canonical %>% distinct(transcript) %>% pull # canonicalTranscripts=read_tsv("ucsc_mm10_knownToEnsembl.txt.gz") %>% # rename(ID=`#name`) %>% # filter(ID %in% canonicalTranscriptsUCSC) %>% # distinct(value) %>% # pull GTFFILE="/ifs/depot/annotation/H.sapiens/ensembl/v75/Homo_sapiens.GRCh37.75.gtf" gtf=read_tsv(GTFFILE,col_names=F,comment="#",col_types=list(X1 = col_character())) iList=gtf %>% mutate(transcript_id=str_match(X9,'transcript_id "([^;]*)";')[,2]) %>% mutate(gene_name=str_match(X9,'gene_name "([^;]*)";')[,2]) %>% mutate(exon_number=str_match(X9,'exon_number "(\\d+)";')[,2]) %>% select(-X9) %>% filter(X3=="exon") %>% filter(transcript_id %in% canonicalTranscripts) %>% mutate(X1=gsub("^chr","",X1)) %>% select(X1,X4,X5,gene_name,transcript_id,exon_number) %>% rename(chrom=X1,start=X4,stop=X5,gene=gene_name,transcript=transcript_id,exon=exon_number) ozfile=gzfile("gene_annotations.txt.gz","w",compression=9) write_tsv(iList,ozfile) close(ozfile)

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/run_analysis.R

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sdronava/data_cleaning_proj

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b5ba8c8d09e8330ef1ce6ebe4bc0f2c440f22de7

refs/heads/master

2020-05-31T06:54:17.592793

2015-02-22T14:50:58

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run_analysis.R

library(dplyr) runAnalysis <- function() { # load the train data set with just the means and stds trainSet <- createTrainDataSet () # load the test data set with just the means and stds testSet <- createTestDataSet () # join the two data sets with just the means and stds completeSet <- rbind(trainSet, testSet) ##print(dim(completeSet)) ## Calculate the averages of means and stds per subject per activity. result <- createAverages(completeSet) ## Write the result out. if(!file.exists("./results")) { dir.create("./results")} write.table(result, file = "results/avg_mean_and_stds_all_data.txt",row.names=FALSE, na="", sep=",", quote=FALSE) } createAverages <- function(data) { activities <- getActivityNames() subjects <- unique(getAllSubjects()) features <- getAnalysisFeatures() ####################################### ##result <- merge(activities , subjects, by=NULL) ## cross join - 6X30 = 180. ##result$V1.x = NULL ##result <- rename(result, Activity=V2, Subject=V1.y) ################################# ##Create Result Data frame. colNames <- c("subject", "activity") result <- as.data.frame(c("subject")) result["Activity"] <- c("activity") for(featIdx in 1:length(features[, 2])) { iFeature <- toString(features[featIdx, 2]) colNames <- c(colNames, iFeature) result[toString(iFeature)] <- c(iFeature) } names(colNames) <- colNames names(result) <- colNames ## Split by subjects (30 data sets) dataBySubj <- split(data, data[, 2]) numOfSubjs <- length(subjects[,1]) for(subjIdx in 1 : numOfSubjs ) { iSubj <- toString(subjIdx) subjData <- dataBySubj[[ iSubj ]]; dataByActivity <- split(subjData , subjData [, 3]) numOfActivities <- length(activities[, 1]) for(activityIdx in 1: numOfActivities ) { iActivity <- activities[activityIdx, 2] activityData <- dataByActivity[[ iActivity ]] newrow <- as.data.frame(c(iSubj)) newrow["Activity"] <- toString(iActivity) for(featIdx in 1:length(features[, 2])) { iFeature <- toString(features[featIdx, 2]) iValue <- mean(activityData [[ toString(iFeature) ]]) newrow[toString(iFeature)] <- as.numeric(iValue) } names(newrow) <- colNames result <- rbind(result, newrow) } } result <- result[-c(1), ] #Rename the columns to meet tidy data standards. ##newColNames(colNames) #Send the results result } newColNames <- function(oldNames) { n <- length(oldNames) newNames <- c() for(idx in 1 : n) { old <- oldNames[idx] ## print(old) } } createTestDataSet <- function() { ### Get Features to be analysed. analysisFeatures <- getAnalysisFeatures() #### Read activity names. activityNames <- getActivityNames() #### Read y_test testActivities <- read.csv("UCI HAR Dataset/test/y_test.txt", sep=" ", header=FALSE) testActivities <- as.data.frame(testActivities$V1) testActivities <- as.data.frame(apply(testActivities, 1, function(s) sub("1", activityNames[1,2], s, fixed=TRUE))) testActivities <- as.data.frame(apply(testActivities, 1, function(s) sub("2", activityNames[2,2], s, fixed=TRUE))) testActivities <- as.data.frame(apply(testActivities, 1, function(s) sub("3", activityNames[3,2], s, fixed=TRUE))) testActivities <- as.data.frame(apply(testActivities, 1, function(s) sub("4", activityNames[4,2], s, fixed=TRUE))) testActivities <- as.data.frame(apply(testActivities, 1, function(s) sub("5", activityNames[5,2], s, fixed=TRUE))) testActivities <- as.data.frame(apply(testActivities, 1, function(s) sub("6", activityNames[6,2], s, fixed=TRUE))) ##print(dim(testActivities )) testSubjects <- getTestSubjects() #### X_test.txt testReadings <- read.csv("UCI HAR Dataset/test/X_test.txt", sep="", header=FALSE) ##print(dim(testReadings)) # Join the columns to create a data set for the test data. temp <- data.frame(id=1:2947) temp[["Subject"]] = testSubjects[ , 1] temp[["Activity"]] = testActivities [ , 1] ##print(dim(temp)) sz <- length(analysisFeatures[, 1]) for( index in 1:sz ) { columnName <- analysisFeatures[index, 2] columnNum <- analysisFeatures[index, 1] temp[[toString(columnName)]] = testReadings [, columnNum] } ##print(dim(temp)) temp } createTrainDataSet <- function() { ##### Read features.txt, rename and identify columns in the data set of interest. analysisFeatures = getAnalysisFeatures() #### Read activity names. activityNames <- getActivityNames() #### Read y_train activities <- read.csv("UCI HAR Dataset/train/y_train.txt", sep=" ", header=FALSE) activities <- as.data.frame(activities$V1) activities <- as.data.frame(apply(activities , 1, function(s) sub("1", activityNames[1,2], s, fixed=TRUE))) activities <- as.data.frame(apply(activities , 1, function(s) sub("2", activityNames[2,2], s, fixed=TRUE))) activities <- as.data.frame(apply(activities , 1, function(s) sub("3", activityNames[3,2], s, fixed=TRUE))) activities <- as.data.frame(apply(activities , 1, function(s) sub("4", activityNames[4,2], s, fixed=TRUE))) activities <- as.data.frame(apply(activities , 1, function(s) sub("5", activityNames[5,2], s, fixed=TRUE))) activities <- as.data.frame(apply(activities , 1, function(s) sub("6", activityNames[6,2], s, fixed=TRUE))) ##print(dim(activities )) #### Read subject_test subjects <- getTrainSubjects() ##print(dim(subjects)) #### X_test.txt readings <- read.csv("UCI HAR Dataset/train/X_train.txt", sep="", header=FALSE) ##print(dim(readings)) # Join the columns to create a data set for the test data. data <- data.frame(id=1:7352) data [["Subject"]] = subjects[ , 1] data [["Activity"]] = activities [ , 1] ##print(dim(data )) sz <- length(analysisFeatures[, 1]) for( index in 1:sz ) { columnName <- analysisFeatures[index, 2] columnNum <- analysisFeatures[index, 1] data [[toString(columnName)]] = readings [, columnNum] } ##print(dim(data )) data } getActivityNames <- function() { #### Read activity names. activityNames <- read.csv("UCI HAR Dataset/activity_labels.txt", sep=" ", header=FALSE) activityNames } getTestSubjects <- function() { #### Read subject_test subjects <- as.data.frame(read.csv("UCI HAR Dataset/test/subject_test.txt", sep=" ", header=FALSE)) ##print(dim(testSubjects)) } getTrainSubjects <- function() { subjects <- as.data.frame(read.csv("UCI HAR Dataset/train/subject_train.txt", sep=" ", header=FALSE)) } getAllSubjects <- function(){ s1 <- getTestSubjects() s2 <- getTrainSubjects() all <- rbind(s1, s2) } getAnalysisFeatures <- function() { ##### Read features.txt, rename and identify columns in the data set of interest. featureList <- read.csv("UCI HAR Dataset/features.txt", sep=" ", header=FALSE); featureList <- rename(featureList, number=V1, name=V2); analysisFeatures = featureList[grepl("-mean[(][)]|-std[(][)]", featureList$name) == TRUE, ] analysisFeatures }

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/dlt.caret.smda.R

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dlt-lee/dlt

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242f9cf53720474d39d1997174e1a395731aa7f7

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2021-12-24T12:07:20.073163

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dlt.caret.smda.R

data<-dlt count<-dim(dlt)[1] #dlt.mda <- function(data,count) { library(caret) library(sparseLDA) library(mda) library(rda) trains_1 <-tail(data,count)[1:(count-3),] trains_2 <-tail(data,count)[2:(count-2),] trains_3 <-tail(data,count)[3:(count-1),] results<-tail(data,(count-3)) tests_1<-tail(data,count)[1:(count-2),] tests_2<-tail(data,count)[2:(count-1),] tests_3<-tail(data,(count-2)) #A: trn1<-trains_1$n trn2<-trains_2$n trn3<-trains_3$n a1.1<-trains_1$a1 a2.1<-trains_1$a2 a3.1<-trains_1$a3 a4.1<-trains_1$a4 a5.1<-trains_1$a5 a1.2<-trains_2$a1 a2.2<-trains_2$a2 a3.2<-trains_2$a3 a4.2<-trains_2$a4 a5.2<-trains_2$a5 a1.3<-trains_3$a1 a2.3<-trains_3$a2 a3.3<-trains_3$a3 a4.3<-trains_3$a4 a5.3<-trains_3$a5 resa1<-results$a1 resa2<-results$a2 resa3<-results$a3 resa4<-results$a4 resa5<-results$a5 #B: b1.1<-trains_1$b1 b2.1<-trains_1$b2 b1.2<-trains_2$b1 b2.2<-trains_2$b2 b1.3<-trains_3$b1 b2.3<-trains_3$b2 resb1<-results$b1 resb2<-results$b2 trains.a1<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resa1) trains.a2<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resa2) trains.a3<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resa3) trains.a4<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resa4) trains.a5<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resa5) trains.b1<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resb1) trains.b2<-data.frame(trn1,trn2,trn3, a1.1,a2.1,a3.1,a4.1,a5.1, a1.2,a2.2,a3.2,a4.2,a5.2, a1.3,a2.3,a3.3,a4.3,a5.3, b1.1,b2.1, b1.2,b2.2, b1.3,b2.3, resb2) set.seed(100) ctrl<-trainControl(method = "LGOCV", summaryFunction = twoClassSummary, classProbs = TRUE, #index = list(trainset = trains.a1), savePredictions = TRUE) smdaFit.a1<-train(resa1~ #a1.1+a2.1+a3.1+a4.1+a5.1+ a1.2+a2.2+a3.2+a4.2+a5.2+ a1.3+a2.3+a3.3+a4.3+a5.3+ #b1.1+b2.1+ b1.2+b2.2+ b1.3+b2.3, data = trains.a1, method = "smda", metric = "ROC", tuneGrid = expand.grid(.subclasses = 1:14), trControl = ctrl )

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/30_mirna_seq/02_r_code/02_comparisons/03_literature_mirs.R

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slobentanzer/integrative-transcriptomics

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refs/heads/master

2022-07-18T10:32:51.331439

2021-01-19T15:17:52

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03_literature_mirs.R

#LITERATURE MIRS IN SCZ AND BD#### rm(list=ls()) home= '~/GitHub/' rootdir = paste(home, "integrative-transcriptomics", sep="") setwd(rootdir) library(ggplot2) library(venn) library(RColorBrewer) library(RNeo4j) graph <- startGraph("http://localhost:7474/db/data/") mir.matrix <- readRDS(file = "working_data/mir_de_matrix_countchange.rds") mir.matrix <- mir.matrix[rowSums(mir.matrix[,1:8]) != 0,] nrow(mir.matrix) #DE mirs nrow(mir.matrix[rowSums(mir.matrix[,1:4]) != 0,]) #DE mirs nrow(mir.matrix[rowSums(mir.matrix[,5:8]) != 0,]) #DE mirs long_term <- rownames(mir.matrix) long_term <- unlist(lapply(strsplit(long_term, "-"), function(x) paste(x[1:3], collapse = "-"))) long_term <- gsub("r", "R", long_term) long_term_lookup <- data.frame(shortname = long_term, name = rownames(mir.matrix)) long_term <- unique(long_term) #literature mirs#### #SCZ#### { bc2011mirs <- read.table("./raw_data/bc2011scz_mirs.txt", encoding = "UTF-8") bc2011mirs <- bc2011mirs[,1] bc2011mirs <- bc2011mirs[c(grep("miR‐", bc2011mirs, fixed = T), grep("let‐", bc2011mirs, fixed = T))] bc2011mirs <- bc2011mirs[-grep("SNP", bc2011mirs, fixed = T)] bc2011mirs <- gsub("(", "", bc2011mirs, fixed = T) bc2011mirs <- gsub(")", "", bc2011mirs, fixed = T) bc2011mirs <- gsub("*", "", bc2011mirs, fixed = T) bc2011mirs <- gsub("‐", "-", bc2011mirs, fixed = T) bc2011mirs <- gsub("-3p", "", bc2011mirs, fixed = T) bc2011mirs <- gsub("-5p", "", bc2011mirs, fixed = T) bc2011mirs[2] <- "miR-128" bc2011mirs <- unique(bc2011mirs) } idx <- lapply(bc2011mirs, function(x) grep(paste0(x, "$"), long_term)) scz_found <- unique(unlist(lapply(idx, function(x) long_term[x]))) length(bc2011mirs) length(long_term) length(scz_found) length(scz_found)/length(long_term) #BD#### { fries2018mirs <- scan("./raw_data/fries2018bd_mirs.txt", what = "") fries2018mirs <- fries2018mirs[c(grep("miR-", fries2018mirs, fixed = T), grep("let-", fries2018mirs, fixed = T))] fries2018mirs <- gsub("*", "", fries2018mirs, fixed = T) fries2018mirs <- gsub(",", "", fries2018mirs, fixed = T) fries2018mirs <- gsub(".", "", fries2018mirs, fixed = T) fries2018mirs <- gsub(";", "", fries2018mirs, fixed = T) fries2018mirs <- gsub("-3p", "", fries2018mirs, fixed = T) fries2018mirs <- gsub("-5p", "", fries2018mirs, fixed = T) fries2018mirs <- unique(fries2018mirs) } idx <- lapply(fries2018mirs, function(x) grep(paste0(x, "$"), long_term)) bd_found <- unique(unlist(lapply(idx, function(x) long_term[x]))) length(fries2018mirs) length(bd_found) length(bd_found)/length(long_term) intersect_lit <- unique(fries2018mirs[fries2018mirs %in% bc2011mirs]) intersect_found <- bd_found[bd_found %in% scz_found] #save#### save(bc2011mirs, fries2018mirs, long_term, file = "./working_data/mirna_seq/mir_disease_literature.RData") #table#### both_mirs <- unique(c(bc2011mirs, fries2018mirs)) both_mirs <- both_mirs[order(both_mirs)] both_mirs <- data.frame(name = both_mirs) both_mirs$BD <- both_mirs$name %in% fries2018mirs both_mirs$SCZ <- both_mirs$name %in% bc2011mirs both_mirs$citation <- NA for(i in 1:nrow(both_mirs)){ BD <- as.numeric(both_mirs$BD[i]) SCZ <- as.numeric(both_mirs$SCZ[i]) if(BD+SCZ == 2){ both_mirs$citation[i] <- "Fries et al., 2018 & Beveridge and Cairns, 2012" } else if (BD == 1) { both_mirs$citation[i] <- "Fries et al., 2018" } else { both_mirs$citation[i] <- "Beveridge and Cairns, 2012" } } both_mirs$name <- paste0("hsa-", both_mirs$name) write.table(both_mirs, file = "./out/DataS4.csv", row.names = F, quote = T, sep = ";") #predicted overlap#### primate_pred <- readRDS(file = "./working_data/mirna_seq/predicted_mirs_primate_overlap.rds") conserved_pred <- readRDS(file = "./working_data/mirna_seq/predicted_mirs_conserved_overlap.rds") tfm_pred <- readRDS(file = "./working_data/mirna_seq/predicted_mirs_tf_mir_overlap.rds") primate_pred <- unlist(lapply(strsplit(primate_pred, "-"), function(x) paste(x[2:3], collapse = "-"))) conserved_pred <- unlist(lapply(strsplit(conserved_pred, "-"), function(x) paste(x[2:3], collapse = "-"))) tfm_pred <- unlist(lapply(strsplit(tfm_pred, "-"), function(x) paste(x[2:3], collapse = "-"))) primate_pred[primate_pred %in% bc2011mirs] primate_pred[primate_pred %in% fries2018mirs] conserved_pred[conserved_pred %in% bc2011mirs] conserved_pred[conserved_pred %in% fries2018mirs] tfm_pred[tfm_pred %in% bc2011mirs] tfm_pred[tfm_pred %in% fries2018mirs] #venn#### length(bc2011mirs[bc2011mirs %in% gsub("hsa-", "", long_term)]) length(fries2018mirs[fries2018mirs %in% gsub("hsa-", "", long_term)]) bc_fries_mirs <- fries2018mirs[fries2018mirs %in% bc2011mirs] length(bc_fries_mirs) length(bc_fries_mirs[bc_fries_mirs %in% gsub("hsa-", "", long_term)]) length(long_term) length(bc2011mirs) length(fries2018mirs) allmirs <- unique(c(gsub("hsa-", "", long_term), bc2011mirs, fries2018mirs)) venn_frame <- matrix(ncol = 3, nrow = length(allmirs), rep(0)) rownames(venn_frame) <- allmirs venn_frame <- data.frame(venn_frame) colnames(venn_frame) <- c("DE", "SCZ", "BD") #countchange for(i in 1:3) { temp <- switch(i, gsub("hsa-", "", long_term), bc2011mirs, fries2018mirs) venn_frame[, i] <- as.numeric(rownames(venn_frame) %in% temp) } svg("img/literature_venn.svg") venn(venn_frame, zcolor = brewer.pal(4, "Set1"), cexsn = .7, cexil = 1) dev.off() venn_frame[rowSums(venn_frame) == 3,] venn_frame[rowSums(venn_frame) == 2,] saveRDS(venn_frame, file = "working_data/literature_de_overlap_frame.rds")

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/man/vcov.nlstac.Rd

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cran/nlstac

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2023-04-13T16:09:33.706071

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vcov.nlstac.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vcov.nlstac.R \name{vcov.nlstac} \alias{vcov.nlstac} \title{Calculate Variance-Covariance Matrix for a nlstac Fitted Model Object} \usage{ \method{vcov}{nlstac}(object, ...) } \arguments{ \item{object}{An object of class \code{"nlstac"} obtained by the \code{nls_tac} function.} \item{...}{Ignored, for compatibility issues.} } \value{ A matrix of the estimated covariances between the parameter estimates. } \description{ Returns the variance-covariance matrix of the main parameters of a fitted model object. The “main” parameters of model correspond to those returned by coef, } \author{ \strong{Mariano Rodríguez-Arias} (\email{arias@unex.es}). \emph{Deptartment of Mathematics} \strong{Juan Antonio Fernández Torvisco} (\email{jfernandck@alumnos.unex.es}). \emph{Department of Mathematics} University of Extremadura (Spain) \strong{Rafael Benítez} (\email{rafael.suarez@uv.es}). \emph{Department of Business Mathematics} University of Valencia (Spain) }

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/inst/util/mkRPPATumorDataset.R

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rmylonas/SuperCurvePAF

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mkRPPATumorDataset.R

### ### $Id: mkRPPATumorDataset.R 947 2015-01-21 17:44:54Z proebuck $ ### (Re)creates 'rppaTumor' dataset object found in 'data' directory. ### local({ ##------------------------------------------------------------------------- makeRPPAs <- function(antibody, filename, datadir, xform=function(x) tolower(x)) { ## Check argumments stopifnot(is.character(antibody) && length(antibody) == 1) stopifnot(is.character(filename) && length(filename) == 1) stopifnot(is.character(datadir) && length(datadir) == 1) stopifnot(is.function(xform)) ## Begin processing assign(varname <- make.names(xform(antibody)), RPPA(filename, path=datadir, antibody=antibody), envir=environment(makeRPPAs)) return(varname) } ## ## Tumor data with 3 antibodies ## extdata.dir <- system.file("extdata", package="SuperCurveSampleData") rawdata.dir <- file.path(extdata.dir, "rppaTumorData") proteinassayfile <- file.path(rawdata.dir, "proteinAssay.tsv") proteinassay.df <- read.delim(proteinassayfile) rppas <- apply(proteinassay.df, 1, function(proteinassay, datadir) { makeRPPAs(proteinassay["Antibody"], proteinassay["Filename"], datadir) }, rawdata.dir) ## :BUG: last two lines of layout info file look hinky. layoutinfofile <- "layoutInfo.tsv" slidedesignfile <- "slidedesign.tsv" assign(design <- "tDesign", RPPADesign(rppa <- get(rppas[1]), grouping="blockSample", center=TRUE, aliasfile=layoutinfofile, designfile=slidedesignfile, path=rawdata.dir)) ## Update package data directory filename <- sprintf("%s.RData", sub("Data$", "", basename(rawdata.dir))) dataset <- file.path(system.file("data", package="SuperCurve"), filename) save(list=c(rppas, design), file=dataset, compress="xz", compression_level=9) })

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/cachematrix.R

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jleonard7/datasciencecoursera

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2021-01-17T09:11:08.913244

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cachematrix.R

## The purpose of these functions are to allow the user to ## cache the inverse of an invertible matrix. This is useful ## to reduce execution time of programs and calculations ## The function below defines 4 functions which are then ## stored in a list. The list is used in second function to ## cache inverse of a matrix or retrieve the cached inverse makeCacheMatrix <- function(x = matrix()) { i_mat <- NULL set_mat <- function(y) { x <<- y i_mat <<- NULL } get_mat <- function() x setInverse<- function(solve) i_mat <<- solve getInverse <- function() i_mat # This list contains 4 functions list(set = set_mat, get = get_mat, setInverse = setInverse, getInverse = getInverse) } ## This function receives as an input a list that contains ## the necessary functions to set and get a matrix as well ## as set the inverse and retrieve the inverse value cacheSolve <- function(x, ...) { # References the getInverse function of the list x i_mat <- x$getInverse() # If value is returned then says getting cached result if(!is.null(i_mat)) { message("Getting cached inverse of this matrix") # This makes it exit the function return(i_mat) } # Calls function to retrieve data data <- x$get() # Calls function to get inverse of matrix i_mat <- solve(data, ...) # Stores inverse for future retrieval x$setInverse(i_mat) # Returns value i_mat }

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/plot6.R

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TarushiRMittal/Exploratory-Data-Analysis-Week-4

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2020-06-16T18:58:27.132626

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plot6.R

emiss_stats <- readRDS("summarySCC_PM25.rds") classification_code_source <- readRDS("Source_Classification_Code.rds") baltimoreLA_cars <- subset(emiss_stats, emiss_stats$fips=="24510" | emiss_stats$fips=="06037" & emiss_stats$type=="ON-ROAD") baltimoreLA_cars_annual <- aggregate(baltimoreLA_cars$Emissions, by=list(baltimoreLA_cars$fips, baltimoreLA_cars$year), FUN=sum) colnames(baltimoreLA_cars_annual) <- c("City", "Year", "Emissions") library(ggplot2) qplot(Year, Emissions, data = baltimoreLA_cars_annual, color = City, geom = "line") + ggtitle("Emissions of PM2.5 in Baltimore City (24510) and LA County (06037)") + ylab("Total Emissions from Motor Vehicles (tons)") + xlab("Year")

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altitudeSysDef.R

#' altitudeSysDef #' #' altitudeSysDef #' #' #' @inheritParams common_attributes #' @param altitudeDatumName The identification given to the surface taken as the surface of reference from which altitudes are measured. See [altitudeDatumName()] #' @param altitudeResolution The minimum distance possible between two adjacent altitude values, expressed in Altitude Distance Units of measure. See [altitudeResolution()] #' @param altitudeDistanceUnits Units in which altitude is measured. See [altitudeDistanceUnits()] #' @param altitudeEncodingMethod The means used to encode the altitudes. See [altitudeEncodingMethod()] #' #' @return a altitudeSysDef list object #' #' @export altitudeSysDef <- function(altitudeDatumName = NULL, altitudeResolution = NULL, altitudeDistanceUnits = NULL, altitudeEncodingMethod = NULL){ Filter(Negate(is.null), list( altitudeDatumName = altitudeDatumName, altitudeResolution = altitudeResolution, altitudeDistanceUnits = altitudeDistanceUnits, altitudeEncodingMethod = altitudeEncodingMethod))}

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jeffgriesemer/r-project

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r

octane_blog_01_eda.R

library(tidyverse) library(dplyr) library(lubridate) # visual library(ggplot2) library(ggrepel) library(scales) library(themes) #install.packages("googlesheets") library(googlesheets) rm(list=ls()) df_stats <- gs_title("golf_stats") df_2018_drive <- df_stats %>% gs_read(ws = "2018 driving accur") df_2018 <- df_2018_drive %>% select(RANK=RANK_2018,PLAYER_NAME,ROUNDS,FAIRWAY_PCT) %>% filter(PLAYER_NAME == 'Tiger Woods') %>% mutate(YEARMO='2018-12', CATEGORY='Driving Accuracy') df_tiger_2019 <- df_stats %>% gs_read(ws = "2019 driving accr") df_tiger_2019_03 <- df_tiger_2019 %>% filter(PLAYER_NAME == 'Tiger Woods') %>% select(RANK=RANK_LAST_WEEK_2019,PLAYER_NAME,ROUNDS,FAIRWAY_PCT) %>% mutate(YEARMO='2019-03', CATEGORY='Driving Accuracy') df_tiger_2019_04 <- df_tiger_2019 %>% filter(PLAYER_NAME == 'Tiger Woods') %>% select(RANK=RANK_NOW,PLAYER_NAME,ROUNDS,FAIRWAY_PCT) %>% mutate(YEARMO='2019-04', CATEGORY='Driving Accuracy') df_tiger_drives <- rbind(df_2018,df_tiger_2019_03) df_tiger_drives <- rbind(df_tiger_drives, df_tiger_2019_04) df_tiger_drives ##### gir df_2018_gir <- df_stats %>% gs_read(ws = "2018 gir") df_tiger_2018_gir <- df_2018_gir %>% filter(PLAYER_NAME == 'Tiger Woods') %>% select(RANK=RANK_LAST_WEEK,PLAYER_NAME,ROUNDS,PCT_HIT) %>% mutate(YEARMO='2018-12', CATEGORY='GIR') df_2019_gir <- df_stats %>% gs_read(ws = "2019 gir") df_tiger_2019_03_gir <- df_2019_gir %>% filter(PLAYER_NAME == 'Tiger Woods') %>% select(RANK=RANK_LAST_WEEK,PLAYER_NAME,ROUNDS,PCT_HIT) %>% mutate(YEARMO='2019-03', CATEGORY='GIR') df_tiger_2019_04_gir <- df_2019_gir %>% filter(PLAYER_NAME == 'Tiger Woods') %>% select(RANK=RANK_THIS_WEEK,PLAYER_NAME,ROUNDS,PCT_HIT) %>% mutate(YEARMO='2019-04', CATEGORY='GIR') df_tiger_gir <- rbind(df_tiger_2018_gir, df_tiger_2019_03_gir) df_tiger_gir <- rbind(df_tiger_gir, df_tiger_2019_04_gir) df_tiger_gir ### putting ### df_2018_gir <- df_stats %>% gs_read(ws = "2018 gir") df_tiger_2018_gir <- df_2018_gir %>% filter(PLAYER_NAME == 'Tiger Woods') %>% select(RANK=RANK_LAST_WEEK,PLAYER_NAME,ROUNDS,PCT_HIT) %>% mutate(YEARMO='2018-12', CATEGORY='GIR') ############### df_2019 <- df_2019_drive %>% filter(RANK_NOW <= 130) %>% mutate(tiger_flag= ifelse(PLAYER_NAME == 'Tiger Woods', T, F)) %>% arrange(RANK_NOW) df_2019$PLAYER_NAME <- as.factor(df_2019$PLAYER_NAME) fct_reorder() ggplot() + geom_point(data=df_2019, aes(x=reorder(PLAYER_NAME,RANK_NOW), y=RANK_NOW, color=tiger_flag)) + geom_point(data=df_2018, aes(x=PLAYER_NAME, y=RANK_2018, color=tiger_flag)) +coord_flip() df_2018 <- read.table("clipboard", header = T, sep = "\t",comment="&", fill = T) names(df$X.) <- "HIT PCT" df_mrg <- merge(df,df_2018, by="PLAYER.NAME", all.x=T) df_mrg_final <- df_mrg %>% select(PLAYER.NAME, RANK_NOW = RANK.THIS.WEEK.x, PCT_NOW = X..x) df_2019 <- df %>% select(1:7) ggplot( df_mrg, aes(x = PLAYER.NAME, y = X., color=)) + geom_point(size = 2, alpha = .6) + geom_smooth(size = 1.5, color = "darkgrey") + scale_y_continuous(label = scales::dollar, limits = c(50000, 250000)) + scale_x_continuous(breaks = seq(0, 60, 10), limits = c(0, 60)) + labs(x = "x", y = "y", title = "title", subtitle = "subtitle") + #theme_minimal() + geom_text_repel(aes(label= )) ### espn stats library(ggrepel) library(ggplot2) library(dplyr) library(viridis) rm(list=ls()) library(tidyverse) library(googlesheets) df_stats <- gs_title("golf_stats_espn") df_2019 <- df_stats %>% gs_read(ws = "2019_stats") df_2019$DRVE_TOTAL <- NULL # data wrangling - cleanup str(df_2019) describe(df_2019$AGE) df_2019_filtered <- df_2019 %>% mutate(AGE_numeric = !(is.na(as.numeric(AGE)))) %>% filter(AGE_numeric == TRUE) %>% mutate(AGE = as.numeric(AGE)) df_2019_filtered <- rename(df_2019_filtered, "RANK_DRV_ACC" = "RK") # EDA library(DataExplorer) #introduce(df_2019_filtered) plot_intro(df_2019_filtered) plot_missing(df_2019) plot_bar(df_2019) plot_histogram(df_2019_filtered) plot_boxplot(df_2019_filtered, by = "YDS_DRIVE") plot_boxplot(df_2019_filtered %>% select(-1), by = "AGE") #plot_boxplot(df_2019, by = "PLAYER") plot_correlation(na.omit(df_2019), maxcat = 5L) plot_correlation(na.omit(df_2019), type = "c") plot_correlation(na.omit(df_2019), type = "d") library(funModeling) library(Hmisc) eda_func <- function(data) { glimpse(df_2019) df_status(df_2019) freq(df_2019) profiling_num(df_2019) plot_num(data) describe(df_2019$AGE) } eda_func(df) sink("file") describe(df) sink() # cor(df_2019) # error - must be numeric library(ggcorrplot) corr <- cor(df_2019_filtered %>% select(-1,-2)) ggcorrplot(corr, type = "lower", outline.col = "black", lab=TRUE, ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726")) # visualize as a circle indicating significance (circle size) and coorelation ggcorrplot(corr, type = "lower", outline.col = "black", method="circle", ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726")) ######## create ntile df_2019_filtered <- df_2019_filtered %>% mutate(RNK_GIR = ntile(GREENS_REG, 10), RNK_PUT = ntile(-PUTT_AVG,10), RNK_SAVE = ntile(SAVE_PCT,10), RNK_AGE = ntile(AGE,10), RNK_YDS = ntile(YDS_DRIVE,10), RNK_ACC = ntile(DRIVING_ACC,10)) #df_2019_filtered <- df_2019_filtered %>% mutate(RNK_INDEX = RNK_GIR + RNK_PUT + RNK_SAVE + RNK_AGE + RNK_YDS + RNK_ACC) #df_2019_filtered <- df_2019_filtered %>% mutate(RNK_INDEX_GRP = ntile(RNK_INDEX, 10)) library(ggrepel) #ggplot(df_2019_filtered %>% filter(RNK_INDEX_GRP > 5), aes(x=PLAYER, y=RNK_INDEX_GRP,color=as.factor(RNK_INDEX_GRP))) + # geom_point() + # coord_flip() + # geom_label_repel(aes(label=PLAYER)) df_rnk <- gather(df_2019_filtered, "VARIABLE", "RANK", 10:15) df_rnk_tiger <- df_rnk %>% filter(grepl("Tiger",PLAYER)) ggplot(df_rnk_tiger, aes(x=VARIABLE, y=RANK)) + geom_point() ##### outliers df_2019_filtered %>% filter(AGE > 50) df_2019_filtered %>% filter(GREENS_REG > 73) ### regression line - age vs driving distance ggplot(df_2019_filtered, aes(x=AGE, y=YDS_DRIVE)) + geom_point(size=3, color="forest green") ggplot(df_2019_filtered, aes(x=AGE, y=YDS_DRIVE)) + geom_point(size=3, color="forest green") + geom_smooth(method = "lm") ggplot(df_2019_filtered, aes(x=AGE, y=YDS_DRIVE)) + geom_point(size=3, color="forest green") + geom_smooth(method = "loess") ggplot(df_2019_filtered, aes(x=DRIVING_ACC, y=YDS_DRIVE, color=AGE)) + geom_point(size=3) + geom_smooth(method = "lm") library(viridis) ggplot(df_2019_filtered, aes(x=DRIVING_ACC, y=YDS_DRIVE, color=AGE)) + geom_point(size=3) + geom_smooth(method = "lm") + scale_color_viridis() library(ggrepel) ggplot(df_2019_filtered, aes(x=DRIVING_ACC, y=YDS_DRIVE, color=AGE)) + geom_point(size=4, alpha=.8) + geom_smooth(method = "lm") + scale_color_viridis() + scale_y_continuous(limits=c(250,325)) + geom_label_repel(data=df_2019_filtered %>% filter((YDS_DRIVE >= 305 | YDS_DRIVE < 275) | DRIVING_ACC > 70), aes(label=PLAYER), size=3.5) + labs(title="PGA statistics - 2019", subtitle = "Yards Per Drive vs. Driving Accuracy by Age", caption="Stats from http://www.espn.com/golf/statistics", x="Driving Accuracy",y="Yards Per Drive") ################################################################ df_2019$YDS_RND <- round(df_2019$YDS_DRIVE, -1) df_2019$YDS_DECILE <- cut(df_2019$YDS_RND, 10, labels=c('10','20','30','40','50','60','70','80','90','100')) df_2019$RNK_YDS <- rank(-df_2019$YDS_DRIVE) df_2019$RNK_ACC <- rank(df_2019$DRIVING_ACC) df_2019$RNK_GIR <- rank(-df_2019$GREENS_REG) df_2019$RNK_PUT <- rank(df_2019$PUTT_AVG) ggplot(df_2019, aes(x=YDS_DRIVE, y=PLAYER, fill=YDS_RND)) + geom_tile(size=3) + geom_tile(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(color="tiger")) + geom_text_repel(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(label=paste(PLAYER, ' / Rank #', RNK_YDS, sep=''), color="tiger"),size=4) + labs(title="Tiger Woods - 2019", subtitle = "Driving Distance", ylab="") + scale_fill_viridis(discrete = F, option="D", guide=guide_legend(title="Driving Yards")) ggplot(df_2019, aes(x=DRIVING_ACC, y=PLAYER, fill=DRIVING_ACC)) + geom_tile(size=3) + geom_tile(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(color="tiger")) + geom_text_repel(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(label=paste(PLAYER, ' / Rank #',RNK_ACC,sep=''), color="tiger"),size=4) + labs(title="Tiger Woods - 2019", subtitle = "Driving Accuracy", ylab="") + scale_fill_viridis(discrete = F, option="D", guide=guide_legend(title="Driving Accuracy")) ggplot(df_2019, aes(x=GREENS_REG, y=PLAYER, fill=GREENS_REG)) + geom_tile(size=3) + geom_tile(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(color="tiger")) + geom_text_repel(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(label=PLAYER, color="tiger"),size=4) + labs(title="Tiger Woods - 2019", subtitle = "Greens in Regulation", ylab="") + scale_fill_viridis(discrete = F, option="D", guide=guide_legend(title="Greens in Reg")) ggplot(df_2019, aes(x=PUTT_AVG, y=PLAYER, fill=PUTT_AVG)) + geom_tile(size=3) + geom_tile(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'), aes(color="tiger")) + #geom_text_repel(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(label=paste(PLAYER, ' / Rank #',RNK_PUT,sep=''), color="tiger"),size=4) + labs(title="Tiger Woods - 2019", subtitle = "Putting Average", ylab="") + scale_fill_viridis(discrete = F, option="D", guide=guide_legend(title="Putting")) ggplot(df_2019, aes(x=reorder(PLAYER,DRIVING_ACC), y=DRIVING_ACC, fill=YDS_DRIVE)) + geom_bar(stat="identity",size=3) + geom_bar(stat="identity",data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(color="tiger")) + geom_text_repel(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(label=paste(PLAYER, ' / Rank #',RNK_ACC,sep=''), color="tiger"),size=4) + labs(title="Tiger Woods - 2019", subtitle = "Driving Accuracy", ylab="") + scale_fill_viridis(discrete = F, option="D", guide=guide_legend(title="Driving Distance")) ggplot(df_2019, aes(x=reorder(PLAYER,DRIVING_ACC), y=DRIVING_ACC, color=YDS_DRIVE)) + geom_point(size=4) + geom_point(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(color="tiger")) + coord_flip() + geom_text_repel(data=df_2019 %>% filter(PLAYER == 'Tiger Woods'),aes(label=paste(PLAYER, ' / Rank #', RNK_ACC,sep=''), color="tiger"),size=4) + labs(title="Tiger Woods - 2019", subtitle = "Driving Accuracy", ylab="") + scale_color_viridis(discrete = F, option="D", guide=guide_legend(title="Driving Distance")) df_2019 ggplot(df_2019, aes(x=YDS_DRIVE, y=PLAYER, fill=RNK_YDS, label=PLAYER)) + geom_point() + # gghighlight(PLAYER == 'Tiger Woods', label_key = PLAYER) geom_text_repel(data=subset(df_2019, RNK_YDS = 107), aes(x=YDS_DRIVE, y=PLAYER))

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/R/OUwieAvg.R

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refs/heads/master

2020-08-07T12:22:04.595425

2019-10-29T22:52:05

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r

OUwieAvg.R

#' Calculate AIC weights #' #' This function takes a vector of AIC (Akaike Information Criterion) values and returns a vector of AIC weights using the formula from Burnham and Anderson (2002). #' #' If \code{na.rm = FALSE} and any values in \code{AIC} are \code{NA}, all returned values will be \code{NA}. #' #' @param AIC A vector of values. #' @param na.rm Whether to remove NA values. #' @return A named vector of weights with names inherited from \code{AIC}. #' @export #' @examples #' AIC <- c(NA, 5, 10, 20, 25) #' #ignore NAs #' AICweights(AIC) #' #' #should return all NAs #' AICweights(AIC, na.rm = FALSE) AICweights <- function(AIC, na.rm = TRUE){ deltAIC <- deltaAIC(AIC, na.rm = na.rm) expAIC <- exp(-.5 * deltAIC) weights <- expAIC / sum(expAIC, na.rm = na.rm) names(weights) <- names(AIC) return(weights) } #' Calculate deltaAIC #' #' Calculate deltaAIC (Akaike Information Criterion), the absolute difference between the lowest AIC value and the other AIC values. #' #' If \code{na.rm = FALSE} and any values in \code{AIC} are \code{NA}, all returned values will be \code{NA}. #' #' @param AIC A vector of values. #' @param na.rm Whether to remove NA values. #' @return A vector of weights. #' @export #' @examples #' AIC <- c(NA, 5, 10, 20, 25) #' deltaAIC(AIC) #' #' #should return all NAs #' deltaAIC(AIC, na.rm = FALSE) deltaAIC <- function(AIC, na.rm = TRUE){ minAIC <- min(AIC, na.rm = na.rm) deltAIC <- AIC - minAIC return(deltAIC) } #' Extract parameters from OUwie results #' #' Extract various parameter values from the results of (many) OUwie analyses and maps the parameter values to different regimes based on the inputted regime map. #' Returns the parameters in a 4-D array, where the first dimension is the models, the second dimension is the parameters (including AICc), the third dimension is the regimes, and the fourth dimension is the replicates. #' #' The \code{regime.mat} is the most important component, as it indicates which parameters should be mapped to which regime for each model. #' For example, in an OU1 model, the user would likely want the parameters mapped to all regimes, whereas in an OUM model, the user would likely want the parameters for each regime mapped exclusively to that regime. #' In more complex scenarios, the user may have multiple OUM models in which regimes are split or combined in different manners, such that the parameters for one regime in one OUM model may map to multiple regimes in the overall dataset. #' The \code{rownames} of this matrix should identify names for the regimes and the \code{colnames} should identify the models. #' It is assumed that the order of the models/\code{colnames} in \code{regime.mat} matches the order of the models in \code{ou.results}. #' #' Valid options for \code{params} are "Alpha", "Sigma.sq", "Theta", "Theta.se", "Halflife" (phylogenetic half-life), "Stat.var" (stationary variance), "AIC", "AICc", and "BIC". #' #' @param ou.results A list of lists (or just a list) of unmodified results from an OUwie analysis #' @param regime.mat A data frame mapping regimes to total regime options for each model (see details) #' @param params A vector specifying which parameter should be calculated/returned (see details) #' @return An \code{ouwiepars} object. Basically a 4-D array that can be passed to other functions for further analysis/visualization. #' @export #' @examples #' \dontrun{ #' library(OUwie) #' data(tworegime) #' ou.results <- list() #' ou.results[[1]] <- OUwie(tree,trait,model=c("BM1")) #' ou.results[[2]] <- OUwie(tree,trait,model=c("BMS"), root.station = FALSE) #' ou.results[[3]] <- OUwie(tree,trait,model=c("OUM")) #' ou.results[[4]] <- OUwie(tree,trait,model=c("OUMV")) #' #' #Both regimes have same parameters for BM1 model. Both regimes have different parameters for other models. #' regime.mat <- data.frame(BM1 = c(1, 1), BMS = c(1,2), OUM = c(1,2), OUMV = c(1,2), row.names = c(1,2))} #' #' OUwieParSumm(ou.results, regime.mat) OUwieParSumm <- function(ou.results, regime.mat, params = c("Alpha","Sigma.sq","Theta","Theta.se", "AICc")){ if(is(ou.results[[1]], "OUwie")) ou.results <- list(ou.results) nruns <- length(ou.results) regimes <- rownames(regime.mat) nregs <- length(regimes) mods <- colnames(regime.mat) nmods <- length(mods) nparams <- length(params) ou.parameters <- array(NA, dim=c(nmods, nparams, nregs, nruns), dimnames=list(mods, params, regimes)) for(j in 1:nruns){ for(i in 1:nmods){ reg.temp <- colnames(ou.results[[j]][[i]]$solution) nreg.temp <- length(reg.temp) #loop through parameters for(param in params){ #Record AICc values if(param == "AIC"){ ou.parameters[i, "AIC", , j] <- ou.results[[j]][[i]]$AIC } else if(param == "AICc"){ ou.parameters[i, "AICc", , j] <- ou.results[[j]][[i]]$AICc } else if(param == "BIC"){ ou.parameters[i, "BIC", , j] <- ou.results[[j]][[i]]$BIC } else if(param == "Alpha"){ if(ou.results[[j]][[i]]$model=="BMS" | ou.results[[j]][[i]]$model=="BM1"){ ou.parameters[i, param, , j] <- 0 } else { for(k in 1:nreg.temp){ ou.parameters[i, param, which(regime.mat[,i] == reg.temp[k], useNames = FALSE), j] <- ou.results[[j]][[i]]$solution[1,k] } } } else if(param == "Sigma.sq"){ for(k in 1:nreg.temp){ ou.parameters[i, param, which(regime.mat[,i] == reg.temp[k], useNames = FALSE), j] <- ou.results[[j]][[i]]$solution[2,k] } } else if(param == "Theta"){ if(ou.results[[j]][[i]]$model=="BM1" | ou.results[[j]][[i]]$model=="OU1" | ou.results[[j]][[i]]$model=="BMS"){ ou.parameters[i, param, , j] <- ou.results[[j]][[i]]$theta[1,1] } else { for(k in 1:nreg.temp){ ou.parameters[i, param, which(regime.mat[,i] == reg.temp[k], useNames = FALSE), j] <- ou.results[[j]][[i]]$theta[k,1] } } } else if(param == "Theta.se"){ if(ou.results[[j]][[i]]$model=="BM1" | ou.results[[j]][[i]]$model=="OU1" | ou.results[[j]][[i]]$model=="BMS"){ ou.parameters[i, param, , j] <- ou.results[[j]][[i]]$theta[1,2] } else { for(k in 1:nreg.temp){ ou.parameters[i, param, which(regime.mat[,i] == reg.temp[k], useNames = FALSE), j] <- ou.results[[j]][[i]]$theta[k,2] } } } else if(param == "Halflife"){ if(ou.results[[j]][[i]]$model=="BMS" | ou.results[[j]][[i]]$model=="BM1"){ ou.parameters[i, param, , j] <- NA } else { for(k in 1:nreg.temp){ ou.parameters[i, param, which(regime.mat[,i] == reg.temp[k], useNames = FALSE), j] <- log(2)/ou.results[[j]][[i]]$solution[1,k] } } } else if(param == "Stat.var"){ if(ou.results[[j]][[i]]$model=="BMS" | ou.results[[j]][[i]]$model=="BM1"){ ou.parameters[i, param, , j] <- NA } else { for(k in 1:nreg.temp){ ou.parameters[i, param, which(regime.mat[,i] == reg.temp[k], useNames = FALSE), j] <- ou.results[[j]][[i]]$solution[2,k]/(2 * ou.results[[j]][[i]]$solution[1,k]) } } } } } } class(ou.parameters) <- "ouwiepars" return(ou.parameters) } #' Model average the parameters across (many) OUwie results using AICc #' #' Internally calculates AICc weights and uses them to model average the parameter values output from \code{OUwieParSumm}. #' #' \code{na.rm = TRUE} will remove any models where \code{AICc == NA}, but will use other models for model averaging; #' \code{na.rm = FALSE} will remove any replicates (rows of the output) with any models where \code{AICc == NA}. #' #' @param ou.parameters An object of class \code{ouwiepars} as output from \code{OUwieParSumm} or \code{CleanOUwieParameters} #' @param OU.only Whether any Brownian motion models should be dropped before model averaging #' @param na.rm Whether to ignore model results with \code{NA} AICc values #' @return A list with two elements: #' \item{Weights}{A data.frame of the AICc weights for each model across the replicates} #' \item{Counts}{A named vector giving the number of replicates in which each model has the highest AICc weight} #' @export #' @examples #' ou.parameters <- OUwieParSumm(ou.results, regime.mat) #' OUwieModelAvg(ou.parameters) OUwieModelAvg <- function(ou.parameters, OU.only = FALSE, na.rm = TRUE){ nmods <- dim(ou.parameters)[1] mods <- dimnames(ou.parameters)[[1]] params <- dimnames(ou.parameters)[[2]][!(dimnames(ou.parameters)[[2]] == "AICc")] nparams <- length(params) nregs <- dim(ou.parameters)[3] regs <- dimnames(ou.parameters)[[3]] nruns <- dim(ou.parameters)[4] ou.avg <- array(NA, dim=c(nparams, nregs, nruns), dimnames=list(params, regs)) use <- vector(mode = "logical", length = nmods) if(OU.only) use <- !(grepl("BM", mods)) else use <- rep(TRUE, nmods) for(i in 1:nruns){ aicc.weights <- AICweights(ou.parameters[use, "AICc", 1, i], na.rm = na.rm) #loop through parameters for (param in params){ ou.avg[param, , i] <- colSums(aicc.weights * ou.parameters[use, param, , i], na.rm = na.rm) } #need to calculate model-averaged theta.se values differently #from page 162 of Burnham and Anderson 2002 if("Theta.se" %in% params & "Theta" %in% params){ for (j in 1:nregs){ variance <- (ou.parameters[use, "Theta.se", j, i])^2 theta.avg <- ou.avg["Theta", j, i] ou.avg["Theta.se", j, i] <- sum(aicc.weights * sqrt(variance + (ou.parameters[use, "Theta", j, i] - theta.avg)^2), na.rm = na.rm) } } } return(ou.avg) } #' Clean extracted parameters from OUwie results #' #' Cleans the parameters that are extracted by \code{OUwieParamSum} based on user-specified upper and lower bounds. #' #' Parameter estimates outside of these bounds will result in the AICc being changed to NA, #' which will affect downstream model averaging (see \code{OUwieModelAvg}). #' #' @param ou.parameters An object of class \code{ouwiepars} as output from \code{OUwieParSumm} #' @param lower A list of lower bounds for model parameters #' @param upper A list of upper bounds for model parameters #' @return An \code{ouwiepars} object #' @export #' @examples #' ou.parameters <- OUwieParSumm(ou.results, regime.mat) #' #' #Sets the AICc for the BM1 model to NA, so it wouldn't be included in downstream model averaging #' OUwieCleanPar(ou.parameters, upper = list("AICc" = 45)) OUwieCleanPar <- function(ou.parameters, lower = list(), upper = list()){ nruns <- dim(ou.parameters)[4] params <- dimnames(ou.parameters)[[2]] try(if(!all(names(lower) %in% params)) stop(paste("Incorrect lower bound parameter(s) specified: ",paste0(names(lower)[!(names(lower) %in% params)],collapse=", ")))) try(if(!all(names(upper) %in% params)) stop(paste("Incorrect upper bound parameter(s) specified: ",paste0(names(upper)[!(names(upper) %in% params)],collapse=", ")))) for(i in 1:nruns){ for(param in names(lower)){ ou.parameters[which(rowSums(ou.parameters[ , param, , i] < lower[[param]]) > 0), "AICc", , i] <- NA } for(param in names(upper)){ ou.parameters[which(rowSums(ou.parameters[ , param, , i] > upper[[param]]) > 0), "AICc", , i] <- NA } } return(ou.parameters) } #' Summarize the model fit across (many) OUwie results using AICc #' #' Returns AICc weights and counts for OUwie results that have been processed using \code{OUwieParSumm}. #' #' \code{na.rm = TRUE} will remove any models where \code{AICc == NA}, but will use other models for AIC weight calculation; #' \code{na.rm = FALSE} will remove any replicates (rows of the output) with any models where \code{AICc == NA}. #' #' @param ou.parameters An object of class \code{ouwiepars} as output from \code{OUwieParSumm} or \code{CleanOUwieParameters} #' @param na.rm Whether to ignore model results with \code{NA} AICc values #' @return A list with two elements: #' \item{Weights}{A data.frame of the AICc weights for each model across the replicates} #' \item{Counts}{A named vector giving the number of replicates in which each model has the highest AICc weight} #' @export #' @examples #' ou.parameters <- OUwieParSumm(ou.results, regime.mat) #' OUwieAICSumm(ou.parameters) OUwieAICSumm <- function(ou.parameters, na.rm = TRUE){ nruns <- dim(ou.parameters)[4] mods <- dimnames(ou.parameters)[[1]] weights <- as.data.frame(array(NA, dim=c(nruns,length(mods)), dimnames=list(seq(1,nruns),mods))) for(i in 1:nruns){ weights[i,] <- AICweights(ou.parameters[ , "AICc", 1, i], na.rm = na.rm) } counts <- table(factor(unlist(apply(weights,1,which.max)),levels = seq(1:length(mods)))) names(counts) <- mods AICsumm <- list(weights,counts) names(AICsumm) <- c("Weights","Counts") return(AICsumm) }

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test-order_cells.R

context("test-order_cells") skip_not_travis <- function () { if (identical(Sys.getenv("TRAVIS"), "true")) { return(invisible(TRUE)) } skip("Not on Travis") } cds <- load_a549() set.seed(100) test_that("order_cells error messages work", { skip_on_travis() expect_error(order_cells(cds), "No dimensionality reduction for UMAP calculated. Please run reduce_dimension with reduction_method = UMAP, cluster_cells, and learn_graph before running order_cells." ) cds <- estimate_size_factors(cds) cds <- preprocess_cds(cds, num_dim = 20) cds <- reduce_dimension(cds) expect_error(order_cells(cds), "No cell clusters for UMAP calculated. Please run cluster_cells with reduction_method = UMAP and run learn_graph before running order_cells.") cds <- cluster_cells(cds) expect_error(order_cells(cds), "No principal graph for UMAP calculated. Please run learn_graph with reduction_method = UMAP before running order_cells.") cds <- learn_graph(cds) expect_error(order_cells(cds, root_cells = c("G07_B02_RT_587"), root_pr_nodes = c("Y_1")), "Please specify either root_pr_nodes or root_cells, not both.") expect_error(order_cells(cds, root_cells = c("hannah")), "All provided root_cells must be present in the cell data set.") expect_error(order_cells(cds, root_pr_nodes = c("hannah")), "All provided root_pr_nodes must be present in the principal graph.") expect_error(order_cells(cds), "(When not in interactive mode, either root_pr_nodes or root_cells must be provided.|No root node was chosen!)") expect_error(order_cells(cds, reduction_method = "tSNE"), "Currently only 'UMAP' is accepted as a reduction_method.") }) cds <- estimate_size_factors(cds) cds <- preprocess_cds(cds, num_dim = 20) cds <- reduce_dimension(cds, umap.fast_sgd=FALSE) cds <- cluster_cells(cds, cluster_method = "louvain") cds <- learn_graph(cds) test_that("order_cells works", { skip_on_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 11.9, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.0538, tol = 1e-3) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_10")) expect_equal(max(pseudotime(cds)), 6.34, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.0538, tol = 1e-3) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 13.2, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 1.5, tol = 1e-1) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 7.26, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 1.5, tol = 1e-1) }) cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE) cds <- cluster_cells(cds, cluster_method = "louvain") cds <- learn_graph(cds) test_that("order_cells works 3d", { skip_on_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 10.0, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_10")) expect_equal(max(pseudotime(cds)), 8.64, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 10.4, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 10.2, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) }) cds <- cluster_cells(cds, random_seed = 100) cds <- learn_graph(cds) test_that("order_cells works leiden", { skip_on_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 9.94, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.49, tol = 1e-2) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_2")) expect_equal(max(pseudotime(cds)), 4.72, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.35, tol = 1e-2) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 8.03, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121 , tol = 1e-3) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 5.85, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121 , tol = 1e-3) }) cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE) cds <- cluster_cells(cds) cds <- learn_graph(cds) test_that("order_cells works leiden 3d", { skip_on_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 9.94, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.49, tol = 1e-2) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_2")) expect_equal(max(pseudotime(cds)), 4.72, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.35, tol = 1e-2) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 8.03, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 5.85, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3) }) #### TRAVIS #### cds <- load_a549() set.seed(100) test_that("order_cells error messages work", { skip_not_travis() expect_error(order_cells(cds), "No dimensionality reduction for UMAP calculated. Please run reduce_dimension with reduction_method = UMAP, cluster_cells, and learn_graph before running order_cells." ) cds <- estimate_size_factors(cds) cds <- preprocess_cds(cds, num_dim = 20) cds <- reduce_dimension(cds) expect_error(order_cells(cds), "No cell clusters for UMAP calculated. Please run cluster_cells with reduction_method = UMAP and run learn_graph before running order_cells.") cds <- cluster_cells(cds) expect_error(order_cells(cds), "No principal graph for UMAP calculated. Please run learn_graph with reduction_method = UMAP before running order_cells.") cds <- learn_graph(cds) expect_error(order_cells(cds, root_cells = c("G07_B02_RT_587"), root_pr_nodes = c("Y_1")), "Please specify either root_pr_nodes or root_cells, not both.") expect_error(order_cells(cds, root_cells = c("hannah")), "All provided root_cells must be present in the cell data set.") expect_error(order_cells(cds, root_pr_nodes = c("hannah")), "All provided root_pr_nodes must be present in the principal graph.") expect_error(order_cells(cds), paste("When not in interactive mode, either", "root_pr_nodes or root_cells must be", "provided.")) expect_error(order_cells(cds, reduction_method = "tSNE"), "Currently only 'UMAP' is accepted as a reduction_method.") }) cds <- estimate_size_factors(cds) cds <- preprocess_cds(cds, num_dim = 20) cds <- reduce_dimension(cds, umap.fast_sgd=FALSE) cds <- cluster_cells(cds, cluster_method = "louvain") cds <- learn_graph(cds) test_that("order_cells works", { skip_not_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 11.9, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.0538, tol = 1e-4) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_10")) expect_equal(max(pseudotime(cds)), 6.34, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.0538, tol = 1e-4) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 13.2, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 1.5, tol = 1e-1) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 7.26, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 1.5, tol = 1e-1) }) cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE) cds <- cluster_cells(cds, cluster_method = "louvain") cds <- learn_graph(cds) test_that("order_cells works 3d", { skip_not_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 10.4, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_10")) expect_equal(max(pseudotime(cds)), 8.64, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 10.4, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 10.4, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3) }) cds <- cluster_cells(cds, random_seed = 100) cds <- learn_graph(cds) test_that("order_cells works leiden", { skip_not_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 9.94, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.49, tol = 1e-2) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_2")) expect_equal(max(pseudotime(cds)), 4.72, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.35, tol = 1e-2) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 8.03, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 6.1, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3) }) cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE) cds <- cluster_cells(cds) cds <- learn_graph(cds) test_that("order_cells works leiden 3d", { skip_not_travis() cds <- order_cells(cds, root_pr_nodes = "Y_1") expect_equal(max(pseudotime(cds)), 9.94, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.49, tol = 1e-2) cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_2")) expect_equal(max(pseudotime(cds)), 4.72, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 4.35, tol = 1e-2) cds <- order_cells(cds, root_cells = "G07_B02_RT_587") expect_equal(max(pseudotime(cds)), 8.03, tol = 1e-2) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3) cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598")) expect_equal(max(pseudotime(cds)), 6.1, tol = 1e-1) expect_equal(min(pseudotime(cds)), 0) expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3) })

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Francais.R

## ---- eval=FALSE--------------------------------------------------------- # antaresRead::setSimulationPath("D:/exemple_test", 0) # # # initialisation de l'étude flowbased # initFlowBased() ## ---- eval=FALSE--------------------------------------------------------- # # chemin du solver antares # setSolverAntares(path = "C:/Program Files/RTE/Antares/5.0.9/bin/antares-5.0-solver.exe") # # # affichage du solver renseigne # getSolverAntares() ## ---- eval=FALSE--------------------------------------------------------- # res_fb <- runSimulationFB(simulationName = "flowBased-Tuto")

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/final/zhihu_tfidf_score.R

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OOmegaPPanDDa/shiny_dsr_zhihu

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zhihu_tfidf_score.R

# source('~/dsr/script/zhihu_preprocessing.R') # library(dplyr) # library(rJava) # library(tm) # library(tmcn) # library(SnowballC) # library(slam) # library(XML) # library(RCurl) # library(Rwordseg) # library(Matrix) # if (!require('tmcn')) { # install.packages('tmcn',repos = 'http://R-Forge.R-project.org') # } # library(tmcn) # if (!require('Rwordseg')) { # install.packages("Rwordseg",repos = 'http://R-Forge.R-project.org') # } # library(Rwordseg) space_tokenizer <- function(x){ unlist(strsplit(as.character(x[[1]]),'[[:space:]]+')) } #text_filter <- function(data_frame) { # Keep only the columns with text # data_frame <- data.frame(data_frame$question_title, data_frame$question_detail, data_frame$ans) # colnames(data_frame) <- c("question_title","question_detail", "ans") # Remove symbols # data_frame$question_title <- clean_text(data_frame$question_title) # data_frame$question_detail <- clean_text(data_frame$question_detail) # data_frame$ans <- clean_text(data_frame$ans) # data_frame$question_title[is.na(data_frame$question_title)] <- '' # data_frame$question_detail[is.na(data_frame$question_detail)] <- '' # data_frame$ans[is.na(data_frame$ans)] <- '' # Remove empty rows or NA # return(data_frame) #} #stop_word_vector <- function(df) { # df$question <- paste(df$question_title, df$question_detail) #View(df) # document <- c(unique(df$question),unique(df$ans)) # stop_word <- get_stop_word(document) # return(stop_word) #} tf_idf_score <- function(df){ # df <- text_filter(df) # stop_words <- stop_word_vector(df) #df$ans_seg <- sapply(df$ans, function(x) paste(seg_worker[x], collapse = ' ')) # Transform the entire answer column into a corpus d_corpus <- VCorpus(VectorSource(as.vector(df$ans_seg))) # Remove punctuation d_corpus <- tm_map(d_corpus, removePunctuation) # Remove numbers d_corpus <- tm_map(d_corpus, removeNumbers) #inspect(d_corpus) #print(toTrad(stopwordsCN())) #Remove stopwords # d_corpus <- tm_map(d_corpus, removeWords, toTrad(stopwordsCN())) # d_corpus <- tm_map(d_corpus, removeWords, stop_words) # Remove whitespace d_corpus = tm_map(d_corpus, stripWhitespace) # Transform back into vector d_corpus <- Corpus(VectorSource(d_corpus)) # Use control list with space tokenizer control_list=list(wordLengths=c(2,Inf),tokenize=space_tokenizer) tdm <- TermDocumentMatrix(Corpus(VectorSource(d_corpus)), control = control_list) # Tf-idf computation tf <- apply(tdm, 2, sum) # term frequency idf <- function(word_doc){ log2( (length(word_doc)) / (nnzero(word_doc)+1)) } idf <- apply(tdm, 1, idf) dic_tfidf <- as.matrix(tdm) for(i in 1:nrow(tdm)){ for(j in 1:ncol(tdm)){ dic_tfidf[i,j] <- (dic_tfidf[i,j] / tf[j]) * idf[i] } } # Dealing with query q = paste(df$question_title[1], df$question_detail[1]) q_seg <- filter_segment(seg_worker[q], stop_words) query_frame <- as.data.frame(table(q_seg)) query_frame <- query_frame %>% na.omit() # Get short doc matrix all_term <- rownames(dic_tfidf) loc <- which(is.element(all_term, query_frame$q_seg)) s_tdm <- dic_tfidf[loc,] query_frame <- query_frame[is.element(query_frame$q_seg, rownames(s_tdm)),] s_tdm[is.na(s_tdm)]=0 # Result : cos similarity ranking cos_tdm <- function(x, y){ x%*%y / sqrt(x%*%x * y%*%y) } #print(s_tdm) #print(query_frame) doc_cos <- apply(s_tdm, 2, cos_tdm, y = query_frame$Freq) doc_cos[is.nan(doc_cos)] <- 0 return(doc_cos) }

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/tidytextapp_applications.R

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tidytextapp_applications.R

source('https://raw.githubusercontent.com/aashishkpandey/tidytextapp/master/tidytextapp_functions.R') #--------------------------------------------------------# # Create DTM of BD #--------------------------------------------------------# system.time({ years = 2015:2016 for (year in years) { bd.df = readRDS(paste0("D:\\31127 Aashish\\10KTech\\clean_data\\bd.df.",year,".Rds")) bdtext = gsub('table of contents|table of content',' ',bd.df$bd.text) bd.dtm = create_DTM( text = bdtext, docID = bd.df$file, replace_ngrm = T, rm_stop_words=T, textcleaning = T, lower=T, alphanum=T, drop_num=T, stop_custom = c('will','was','can'), smart_stop_words = T, tfidf = F, bi_gram_pct = 0.02, min_freq = 5, filter = 'pct', py.sent_tknzr = T) saveRDS(bd.dtm,paste0('D:\\31127 Aashish\\10KTech\\clean_data\\dtm.bd.',year,'.Rds')) } }) #--------------------------------------------------------# # Create DTM of RF #--------------------------------------------------------# system.time({ years = 2015:2016 for (year in years) { rf.df = readRDS(paste0("D:\\31127 Aashish\\10KTech\\clean_data\\rf.df.",year,".Rds")) rftext = gsub('table of contents|table of content',' ',rf.df$rf.text) rf.dtm = create_DTM( text = rftext, docID = rf.df$file, replace_ngrm = T, rm_stop_words=T, textcleaning = T, lower=T, alphanum=T, drop_num=T, stop_custom = c('will','was','can'), smart_stop_words = T, tfidf = F, bi_gram_pct = 0.02, min_freq = 5, filter = 'pct', py.sent_tknzr = T) saveRDS(rf.dtm,paste0('D:\\31127 Aashish\\10KTech\\clean_data\\dtm.rf.',year,'.Rds')) } })

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/code/24_7_study/cgm/cgm_data_overview.R

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cgm_data_overview.R

##cgms no_function() library(tidyverse) ###cgm masstools::setwd_project() rm(list = ls()) source("code/tools.R") load("data/24_7_study/cgm/data_preparation/expression_data") load("data/24_7_study/cgm/data_preparation/sample_info") load("data/24_7_study/cgm/data_preparation/variable_info") load("data/24_7_study/summary_info/day_night_df") load("data/24_7_study/summary_info/all_accurate_time") setwd("data/24_7_study/cgm/data_overview") day_night_df = day_night_df %>% dplyr::mutate( start_time = as.POSIXct(hms::as_hms(start)), end_time = as.POSIXct(hms::as_hms(end)), week = format(day, "%a") ) %>% dplyr::mutate(week = paste( week, lubridate::month(day), lubridate::day(day), sep = "-" )) %>% dplyr::mutate(week = factor(week, unique(week))) ##cgm temp_data_cgm = data.frame(accurate_time = sample_info$accurate_time, day = as.character(sample_info$day), hour = sample_info$hour, time = sample_info$time, value = as.numeric(expression_data[1,])) %>% dplyr::mutate( time = as.POSIXct(time), week = format(accurate_time, "%a") ) %>% dplyr::mutate(week = paste( week, lubridate::month(day), lubridate::day(day), sep = "-" )) %>% dplyr::mutate(week = factor(week, unique(week))) library(plyr) temp = temp_data_cgm %>% plyr::dlply(.variables = .(day)) temp %>% lapply(function(x){ as.character(range(x$accurate_time)) }) %>% do.call(rbind, .) temp %>% lapply(function(x){ as.character(range(x$time)) }) %>% do.call(rbind, .) library(scales) plot_cgm1 = ggplot() + geom_rect( mapping = aes( xmin = start, xmax = end, ymin = -Inf, ymax = Inf ), fill = "lightyellow", data = day_night_df, # alpha = 0.5, show.legend = FALSE ) + geom_line(aes(x = accurate_time, y = value, group = 1), data = temp_data_cgm, show.legend = FALSE) + labs(y = "Continuous glucose monitoring", x = "") + scale_x_datetime( breaks = date_breaks("4 hour"), date_labels = "%a %H:%M", limits = c(min(all_accurate_time), max(all_accurate_time)), timezone = "America/Los_Angeles" ) + geom_smooth(aes(x = accurate_time, y = value), method = "loess", se = FALSE, span = 0.01, color = "red", data = temp_data_cgm) + scale_y_continuous(expand = expansion(mult = c(0,0))) + base_theme + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size = 10), axis.line.x = element_blank(), # axis.ticks.x = element_blank(), panel.grid = element_blank(), panel.background = element_rect(fill = alpha("grey", 0.2)), plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")) plot_cgm1 plot_cgm2 = ggplot() + geom_rect( mapping = aes( xmin = start_time, xmax = end_time, ymin = -Inf, ymax = Inf ), fill = "lightyellow", data = day_night_df, show.legend = FALSE ) + geom_line(aes(x = time, y = value, group = 1, color = day), data = temp_data_cgm, show.legend = FALSE) + geom_point(aes(x = time, y = value, color = day), size = 0.3, alpha = 0.5, data = temp_data_cgm, show.legend = FALSE) + ggsci::scale_color_lancet() + labs(y = "Continuous glucose monitoring", x = "") + scale_x_datetime( breaks = scales::date_breaks("6 hour"), date_labels = "%H:%M", expand = expansion(mult = c(0,0)) # timezone = "America/Los_Angeles" ) + scale_y_continuous(expand = expansion(mult = c(0,0))) + base_theme + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = 10), axis.line.x = element_blank(), # axis.ticks.x = element_blank(), panel.grid = element_blank(), panel.background = element_rect(fill = alpha("grey", 0.2)), plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")) + facet_grid(rows = vars(week), scales = "free_y") plot_cgm2 # plot_cgm2 + # geom_smooth(aes(x = accurate_time, # y = value), # method = "loess", # se = FALSE, # span = 0.05, # color = "red", # data = temp_data_cgm) plot_cgm3 = ggplot() + geom_rect( mapping = aes( xmin = start_time, xmax = end_time, ymin = -Inf, ymax = Inf ), fill = "lightyellow", data = day_night_df %>% dplyr::filter(day == "2019-05-01"), show.legend = FALSE ) + geom_line(aes(x = time, y = value, group = week, color = week), data = temp_data_cgm, show.legend = TRUE) + labs(y = "Continuous glucose monitoring", x = "") + scale_color_manual(values = week_color) + scale_x_datetime( breaks = scales::date_breaks("2 hour"), date_labels = "%H:%M", expand = expansion(mult = c(0,0)) ) + scale_y_continuous(expand = expansion(mult = c(0,0))) + base_theme + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size = 10), axis.line.x = element_blank(), legend.position = "top", # axis.ticks.x = element_blank(), panel.grid = element_blank(), panel.background = element_rect(fill = alpha("grey", 0.2)), plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")) + guides(color = guide_legend(nrow = 1)) plot_cgm3 # ggsave(plot_cgm1, filename = "plot_cgm1.pdf", width = 14, height = 3) # ggsave(plot_cgm2, filename = "plot_cgm2.pdf", width = 7, height = 14) # ggsave(plot_cgm3, filename = "plot_cgm3.pdf", width = 14, height = 7)

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/man/parameters.Rd

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jkennel/aquifer

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parameters.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/parameters.R \name{parameters} \alias{parameters} \title{parameters} \usage{ parameters(frequency, period, omega, alpha_w, storage_aquifer, storage_confining, specific_yield, transmissivity_aquifer, diffusivity_vadose, diffusivity_aquifer, diffusivity_confining, thickness_vadose, thickness_aquifer, thickness_confining, height_water, radius_well, radius_casing, loading_efficiency, attenuation, inverse, gravity) } \arguments{ \item{frequency}{the frequency in cycles per time (example units: cycles per day)} \item{period}{the period of signal (example units: days)} \item{omega}{the angular frequency \eqn{2 \pi \omega} (example units: radians / time)} \item{alpha_w}{the dimensionless frequency} \item{storage_aquifer}{the aquifer specific storage} \item{storage_confining}{the confining layer specific storage} \item{specific_yield}{the specific yield at the water table} \item{transmissivity_aquifer}{the aquifer transmissivity} \item{diffusivity_vadose}{pneumatic diffusivity of vadose zone} \item{diffusivity_aquifer}{aquifer diffusivity} \item{diffusivity_confining}{confining layer diffusivity} \item{thickness_vadose}{the thickness of the vadose zone} \item{thickness_aquifer}{the aquifer thickness} \item{thickness_confining}{the confining layer thickness} \item{height_water}{the depth from the water table} \item{radius_well}{the radius at the screened portion of the well} \item{radius_casing}{the radius at the location of the water level} \item{loading_efficiency}{static loading efficiency} \item{attenuation}{the attenuation factor of the capillary fringe} \item{inverse}{if true water level follows the inverse of a barometric pressure change} \item{gravity}{the acceleration due to gravity} } \description{ parameters }

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##### Algoritmo genético ###### source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/crearpob.R") source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/fitnessind.R") source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/fitnesspob.R") source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/seleccionarind.R") source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/cruzarind.R") source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/mutarind.R") optimizar_ga <- function( funcion_objetivo, n_variables, nmax, miu, escenario, des, optimizacion, limite_inf = NULL, limite_sup = NULL, n_poblacion = 20, n_generaciones = 50, elitismo = 0.1, prob_mut = 0.5, distribucion = "aleatoria", media_distribucion = 1, sd_distribucion = 1, min_distribucion = -1, max_distribucion = 1, metodo_seleccion = "ruleta", metodo_cruce = "uniforme", parada_temprana = FALSE, rondas_parada = NULL, tolerancia_parada = NULL, verbose = 1, ...) { #prob_mut_total=c(0.05,0.1,0.15,0.2,0.25) #a<-1 # ARGUMENTOS # ============================================================================= # funcion_objetivo: nombre de la función que se desea optimizar. Debe de haber # sido definida previamente. # n_variables: longitud de los individuos. # optimizacion: "maximizar" o "minimizar". Dependiendo de esto, la relación # del fitness es directamente o indirectamente proporcional al # valor de la función. # limite_inf: vector con el límite inferior de cada variable. Si solo se # quiere imponer límites a algunas variables, emplear NA para # las que no se quiere acotar. # limite_sup: vector con el límite superior de cada variable. Si solo se # quiere imponer límites a algunas variables, emplear NA para # las que no se quieren acotar. # n_poblacion: número total de individuos de la población. # n_generaciones: número total de generaciones creadas. # elitismo: porcentaje de mejores individuos de la población actual que # pasan directamente a la siguiente población. # prob_mut: probabilidad que tiene cada posición del individuo de mutar. # distribucion: distribución de la que obtener el factor de mutación. Puede # ser: "normal", "uniforme" o "aleatoria". # media_distribucion: media de la distribución si se selecciona distribucion="normal". # sd_distribucion: desviación estándar de la distribución si se selecciona # distribucion="normal". # min_distribucion: mínimo la distribución si se selecciona distribucion="uniforme". # max_distribucion: máximo la distribución si se selecciona distribucion="uniforme". # metodo_seleccion: método para establecer la probabilidad de selección. Puede # ser: "ruleta", "rank" o "tournament". # metodo_seleccion: método para cruzar los individuos. Puede ser: "uniforme", # "punto_simple". # parada_temprana: si durante las últimas "rondas_parada" generaciones la diferencia # absoluta entre mejores individuos no es superior al valor de # "tolerancia_parada", se detiene el algoritmo y no se crean # nuevas generaciones. # rondas_parada: número de generaciones consecutivas sin mejora mínima para que # se active la parada temprana. # tolerancia_parada: valor mínimo que debe tener la diferencia de generaciones # consecutivas para considerar que hay cambio. # verbose: Nivel de detalle para que se imprima por pantalla el # resultado de cada paso del algoritmo (0, 1, 2) # RETORNO # ============================================================================= # La función devuelve una lista con 5 elementos: # fitness: una lista con el fitness del mejor individuo de cada # generación. # mejores_individuos: una lista con la combinación de predictores del mejor # individuo de cada generación. # mejor_individuo: combinación de predictores del mejor individuo encontrado # en todo el proceso. # diferencia_abs: una lista con la diferencia absoluta entre el fitness # del mejor individuo de generaciones consecutivas. # df_resultados: un dataframe con todos los resultados anteriores. start_time <- Sys.time() # COMPROBACIONES INICIALES # ---------------------------------------------------------------------------- # Si se activa la parada temprana, hay que especificar los argumentos # rondas_parada y tolerancia_parada. if (isTRUE(parada_temprana) & (is.null(rondas_parada) | is.null(tolerancia_parada)) ) { stop(paste( "Para activar la parada temprana es necesario indicar un valor", "de rondas_parada y de tolerancia_parada." )) } # ESTABLECER LOS LÍMITES DE BÚSQUEDA SI EL USUARIO NO LO HA HECHO # ---------------------------------------------------------------------------- if (is.null(limite_sup) | is.null(limite_inf)) { warning(paste( "Es altamente recomendable indicar los límites dentro de los", "cuales debe buscarse la solución de cada variable.", "Por defecto se emplea: [-10^3, 10^3]." )) } if (any( is.null(limite_sup), is.null(limite_inf), any(is.na(limite_sup)), any(is.na(limite_inf)) )) { warning(paste( "Los límites empleados por defecto cuando no se han definido son:", " [-10^3, 10^3]." )) cat("\n") } # Si no se especifica limite_inf, el valor mínimo que pueden tomar las variables # es 1. if (is.null(limite_inf)) { limite_inf <- rep(x = 1, times = n_variables) } # Si no se especifica limite_sup, el valor máximo que pueden tomar las variables # es 100. if (is.null(limite_sup)) { limite_sup <- rep(x = 100, times = n_variables) } # Si los límites no son nulos, se reemplazan aquellas posiciones NA por el valor # por defecto 1 y 100. if (!is.null(limite_inf)) { limite_inf[is.na(limite_inf)] <- 1 } if (!is.null(limite_sup)) { limite_sup[is.na(limite_sup)] <- 100 } # ALMACENAMIENTO DE RESULTADOS # ---------------------------------------------------------------------------- # Por cada generación se almacena, la población, el mejor individuo, su fitness, # y la diferencia absoluta respecto a la última generación. poblaciones <- vector(mode = "list", length = n_generaciones) resultados_fitness <- vector(mode = "list", length = n_generaciones) resultados_individuo <- vector(mode = "list", length = n_generaciones) diferencia_abs <- vector(mode = "list", length = n_generaciones) # ITERACIÓN DE POBLACIONES # ---------------------------------------------------------------------------- for (i in 1:n_generaciones) { if (verbose %in% c(1,2)) { cat("-------------------", "\n") cat("Generación:", paste0(i, "\\", n_generaciones), "\n") cat("-------------------", "\n") } if (i == 1) { # CREACIÓN DE LA POBLACIÓN INICIAL # ------------------------------------------------------------------------ poblacion <- crear_poblacion( n_poblacion = n_poblacion, n_variables = n_variables, nmax = nmax, miu = miu, escenario = escenario, des = des, limite_inf = limite_inf, limite_sup = limite_sup, verbose = verbose %in% c(2) ) } poblacion<-round(poblacion,3) # CALCULAR FITNESS DE LOS INDIVIDUOS DE LA POBLACIÓN # -------------------------------------------------------------------------- fitness_ind_poblacion <- calcular_fitness_poblacion( poblacion = poblacion, funcion_objetivo = funcion_objetivo, optimizacion = optimizacion, verbose = verbose %in% c(2) ) # SE ALMACENA LA POBLACIÓN Y SU MEJOR INDIVIDUO # -------------------------------------------------------------------------- poblaciones[[i]] <- poblacion fitness_mejor_individuo <- max(fitness_ind_poblacion) mejor_individuo <- poblacion[which.max(fitness_ind_poblacion), ] resultados_fitness[[i]] <- fitness_mejor_individuo resultados_individuo[[i]] <- mejor_individuo # SE CALCULA LA DIFERENCIA ABSOLUTA RESPECTO A LA GENERACIÓN ANTERIOR # -------------------------------------------------------------------------- # La diferencia solo puede calcularse a partir de la segunda generación. if (i > 1) { diferencia_abs[[i]] <- abs(resultados_fitness[[i - 1]] - resultados_fitness[[i]]) } # NUEVA POBLACIÓN # -------------------------------------------------------------------------- nueva_poblacion <- matrix( data = NA, nrow = nrow(poblacion), ncol = ncol(poblacion) ) # ELITISMO # -------------------------------------------------------------------------- # El elitismo indica el porcentaje de mejores individuos de la población # actual que pasan directamente a la siguiente población. De esta forma, se # asegura que, la siguiente generación, no sea nunca inferior. if (elitismo > 0) { n_elitismo <- ceiling(nrow(poblacion) * elitismo) posicion_n_mejores <- order(fitness_ind_poblacion, decreasing = TRUE) posicion_n_mejores <- posicion_n_mejores[1:n_elitismo] nueva_poblacion[1:n_elitismo, ] <- poblacion[posicion_n_mejores, ] } else { n_elitismo <- 0 } # CREACIÓN DE NUEVOS INDIVIDUOS POR CRUCES # -------------------------------------------------------------------------- for (j in (n_elitismo + 1):nrow(nueva_poblacion)) { # Seleccionar parentales indice_parental_1 <- seleccionar_individuo( vector_fitness = fitness_ind_poblacion, metodo_seleccion = metodo_seleccion, verbose = verbose %in% c(2) ) indice_parental_2 <- seleccionar_individuo( vector_fitness = fitness_ind_poblacion, metodo_seleccion = metodo_seleccion, verbose = verbose %in% c(2) ) parental_1 <- poblacion[indice_parental_1, ] parental_2 <- poblacion[indice_parental_2, ] # Cruzar parentales para obtener la descendencia descendencia <- cruzar_individuos( parental_1 = parental_1, parental_2 = parental_2, metodo_cruce = metodo_cruce, verbose = verbose %in% c(2), escenario = escenario ) # Mutar la descendencia descendencia <- mutar_individuo( individuo = descendencia, prob_mut = prob_mut, limite_inf = limite_inf, limite_sup = limite_sup, distribucion = distribucion, media_distribucion = media_distribucion, sd_distribucion = sd_distribucion, min_distribucion = min_distribucion, max_distribucion = max_distribucion, verbose = verbose %in% c(2), escenario = escenario ) nueva_poblacion[j, ] <- descendencia } # if (poblacion==nueva_poblacion ){ # prob_mut=prob_mut_total[a] # print(prob_mut) # if (a<length(prob_mut_total) ){ # a=a+1 # } #} poblacion <- round(nueva_poblacion,3) # CRITERIO DE PARADA # -------------------------------------------------------------------------- # Si durante las últimas n generaciones, la diferencia absoluta entre mejores # individuos no es superior al valor de tolerancia_parada, se detiene el # algoritmo y no se crean nuevas generaciones. if (parada_temprana && (i > rondas_parada)) { ultimos_n <- tail(unlist(diferencia_abs), n = rondas_parada) if (all(ultimos_n < tolerancia_parada)) { cat( "Algoritmo detenido en la generacion", i, "por falta cambio mínimo de", tolerancia_parada, "durante", rondas_parada, "generaciones consecutivas.", "\n" ) break() } } } # IDENTIFICACIÓN DEL MEJOR INDIVIDUO DE TODO EL PROCESO # ---------------------------------------------------------------------------- indice_mejor_individuo_global <- which.max(unlist(resultados_fitness)) mejor_fitness_global <- resultados_fitness[[indice_mejor_individuo_global]] mejor_individuo_global <- resultados_individuo[[indice_mejor_individuo_global]] # Se identifica el valor de la función objetivo para el mejor individuo. if (optimizacion == "maximizar") { mejor_valor_global <- mejor_fitness_global } else { mejor_valor_global <- -1*mejor_fitness_global } # RESULTADOS # ---------------------------------------------------------------------------- # Para crear el dataframe se convierten las listas a vectores del mismo tamaño. resultados_fitness <- unlist(resultados_fitness) diferencia_abs <- c(NA, unlist(diferencia_abs)) # Si hay parada temprana, algunas generaciones no se alcanzan: Se eliminan sus # posiciones de las listas de resultados resultados_individuo <- resultados_individuo[!sapply(resultados_individuo, is.null)] poblaciones <- poblaciones[!sapply(poblaciones, is.null)] # Para poder añadir al dataframe la secuencia variables, se concatenan. variables <- sapply( X = resultados_individuo, FUN = function(x) { paste(x, collapse = ", ") } ) df_resultados <- data.frame( generacion = seq_along(resultados_fitness), fitness = resultados_fitness, predictores = variables, diferencia_abs = diferencia_abs ) resultados <- list( mejor_individuo_global = mejor_individuo_global, mejor_valor_global = mejor_valor_global, mejor_fitness_por_generacion = resultados_fitness, mejor_individuo_por_generacion = resultados_individuo, diferencia_abs = diferencia_abs, df_resultados = df_resultados, poblaciones = poblaciones, funcion_objetivo = funcion_objetivo ) end_time <- Sys.time() # INFORMACIÓN ALMACENADA EN LOS ATRIBUTOS # ---------------------------------------------------------------------------- attr(resultados, "class") <- "optimizacion_ga" attr(resultados, 'fecha_creacion') <- end_time attr(resultados, 'duracion_optimizacion') <- paste( difftime(end_time, start_time, "secs"), "secs" ) attr(resultados, 'optimizacion') <- optimizacion attr(resultados, 'lim_inf') <- limite_inf attr(resultados, 'lim_sup') <- limite_sup attr(resultados, 'n_poblacion') <- n_poblacion attr(resultados, 'generaciones') <- i attr(resultados, 'valor_variables') <- mejor_individuo_global attr(resultados, 'mejor_fitness') <- mejor_fitness_global attr(resultados, 'optimo_encontrado') <- mejor_valor_global attr(resultados, 'n_poblacion') <- n_poblacion attr(resultados, 'elitismo') <- elitismo attr(resultados, 'prob_mut') <- prob_mut attr(resultados, 'metodo_seleccion') <- metodo_seleccion attr(resultados, 'metodo_cruce') <- metodo_cruce attr(resultados, 'parada_temprana') <- parada_temprana attr(resultados, 'rondas_parada') <- rondas_parada attr(resultados, 'tolerancia_parada') <- tolerancia_parada # INFORMACIÓN DEL PROCESO (VERBOSE) # ---------------------------------------------------------------------------- if (verbose %in% c(1,2)) { cat("-----------------------", "\n") cat("Optimización finalizada", "\n") cat("-----------------------", "\n") cat("Fecha finalización =", as.character(Sys.time()), "\n") cat("Duración selección = ") print(difftime(end_time, start_time)) cat("Número generaciones =", i, "\n") cat("Límite inferior =", paste(limite_inf, collapse = ", "), "\n") cat("Límite superior =", paste(limite_sup, collapse = ", "), "\n") cat("Optimización =", optimizacion,"\n") cat("Óptimo encontrado =", mejor_valor_global,"\n") cat("Valor variables =", mejor_individuo_global, "\n") cat("\n") } return(resultados) } print.optimizacion_ga <- function(obj){ # Función print para objetos optimizacion_ga cat("----------------------------------------------", "\n") cat("Resultados optimización por algoritmo genético", "\n") cat("----------------------------------------------", "\n") cat("Fecha creación =", attr(obj, 'fecha_creacion'), "\n") cat("Duración selección = ", attr(obj, 'duracion_optimizacion'), "\n") cat("Número generaciones =", attr(obj, 'generaciones'), "\n") cat("Límite inferior =", attr(obj, 'lim_inf'), "\n") cat("Límite superior =", attr(obj, 'lim_sup'), "\n") cat("Optimización =", attr(obj, 'optimizacion'), "\n") cat("Óptimo encontrado =", attr(obj, 'optimo_encontrado'), "\n") cat("Valor variables =", attr(obj, 'valor_variables'), "\n") cat("Función objetivo =", "\n") cat("\n") print(obj$funcion_objetivo) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sublinks.R \name{add_clusters} \alias{add_clusters} \title{Add gene clusters} \usage{ add_clusters(x, parent_track_id, ...) } \description{ Add gene clusters }

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library(DMRMark) ### Name: MakeGSoptions ### Title: Encapsulate prior parameters and Gibbs Sampler (GS) control ### parameters ### Aliases: MakeGSoptions ### ** Examples # MakeGSoptions opts <- MakeGSoptions()

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test_conversions <- function(){ p <- matrix(runif(20), nc=2) integerRepresentation <- as.integer(-1000*log(1-p)) int2 <- p2i(p) checkTrue(all.equal(integerRepresentation, int2)) } test_oligoSnpSet <- function(){ data(oligoSetExample) checkTrue(validObject(as(oligoSet, "SnpSet2"))) } test_makeFeatureRanges <- function(){ data(oligoSetExample) gr <- makeFeatureGRanges(featureData(oligoSet), genome=genomeBuild(oligoSet)) checkTrue(validObject(gr)) gr2 <- makeFeatureGRanges(oligoSet) checkIdentical(gr, gr2) } ##test_RangedDataHMM2GRanges <- function(){ ## if(require(VanillaICE)){ ## data(hmmResults, package="VanillaICE") ## checkTrue(validObject(as(hmmResults, "GRanges"))) ## obj <- as(hmmResults, "GRangesList") ## checkTrue(validObject(obj)) ## checkEquals(names(obj), unique(sampleNames(hmmResults))) ## } ##}

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#Question A loopvec1 <- 5:7 loopvec2 <- 9:6 foo <- matrix(NA, length(loopvec1), length(loopvec2)) for(i in 1:length(loopvec1)) { foo[i,] <- loopvec1[i] * loopvec2 } #Question B chars <- c("Peter","Homer","Lois","Stewie","Maggie","Bart") num_vals <- rep(NA, times=length(chars)) for (i in 1:length(chars)) { num_vals[i] <- switch(EXPR=chars[i],Homer=12,Marge=34,Bart=56,Lisa=78,Maggie=90,NA) } #Question C #i mylist <- list( aa=c(3.4, 1), bb=matrix(1:4, 2, 2), cc=matrix(c(T, T, F, T, F, F), 3, 2), dd="string here", ee=list(c("hello", "you"), matrix(c("hello", "there"))), ff=matrix(c("red", "green", "blue", "yellow")) ) #ii mylist <- list("tricked you", as.vector(matrix(1:6, 3, 2))) #iii mylist <- list( list(1,2,3), list(c(3,2), 2), list(c(1, 2), matrix(c(1,2))), rbind(1:10, 100:91) ) matrix_count <- 0 for (member in mylist) { if (is.matrix(member)) { matrix_count <- matrix_count + 1 } else if (is.list(member)) { for (submember in member) { if (is.matrix(submember)) { matrix_count <- matrix_count + 1 } } } }

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# YAPAY SİNİR AĞLARI ### İlk önce bütün girdileri temizleyelim rm(list = ls()) ## Kütüphaneler library(caret) library(tidyverse) library(AppliedPredictiveModeling) library(pls) #kismi en kucuk kareler ve pcr icin library(elasticnet) library(broom) #tidy model icin library(glmnet) library(MASS) library(ISLR) library(PerformanceAnalytics) library(funModeling) library(Matrix) library(kernlab) #svm library(e1071) #svm icin library(rpart) #cart icin library(pgmm) #olive data seti icin library(dslabs) library(rpart.plot) #rpart gorsel icin library(partykit) #karar agaci gorseli icin library(ipred) #bagging icin library(randomForest) library(gbm) library(nnet) library(neuralnet) library(GGally) library(NeuralNetTools) #garson fonksiyonu icin library(FNN) library(dplyr) ## Verisetimizi alalım # http://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics dff <- read_table( file = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00243/yacht_hydrodynamics.data', col_names = c( 'longpos_cob', 'prismatic_coeff', 'len_disp_ratio', 'beam_draut_ratio', 'length_beam_ratio', 'froude_num', 'residuary_resist' ) ) glimpse(dff) summary(dff) profiling_num(dff) ggpairs(dff) chart.Correlation(dff, histogram = T, pch=19) olcekleme <- function(x) { (x - min(x)) / (max(x)-min(x)) } dff <- na.omit(dff) sapply(dff, FUN = olcekleme) train_indeks <- createDataPartition(dff$residuary_resist, p = 0.8, times = 1) head(train_indeks) train <- dff[train_indeks$Resample1, ] test <- dff[-train_indeks$Resample1, ] train_x <- train %>% dplyr::select(-residuary_resist) train_y <- train %>% dplyr::select(residuary_resist) test_x <- test %>% dplyr::select(-residuary_resist) test_y <- test %>% dplyr::select(residuary_resist) training <- data.frame(train_x, residuary_resist = train_y) names(training) # neuralnet kullanacağız ve bunun için formülün açıkça girilmesi gerekiyor. ysa_formul <- residuary_resist ~ longpos_cob + prismatic_coeff + len_disp_ratio + beam_draut_ratio + length_beam_ratio + froude_num #bağımlı ve bağımsız değilkenlerle oluşan formülü yazdık neuralnet(formula = ysa_formul, data = training) plot(neuralnet(formula = ysa_formul, data = training, hidden = c(2,1), stepmax = 100), rep="best") mynn <- nnet( residuary_resist ~ longpos_cob + prismatic_coeff + len_disp_ratio + beam_draut_ratio + length_beam_ratio + froude_num, data = training, size = 2, decay = 1.0e-5, maxit = 5000 ) ysa_formul <- residuary_resist ~ longpos_cob + prismatic_coeff + len_disp_ratio + beam_draut_ratio + length_beam_ratio + froude_num ysa1 <- neuralnet(ysa_formul, data = training) plot(ysa1) ysa1$result.matrix

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# Name : est.R0.AR # Desc : Estimation of basic Reproduction Number using Attack Rate method # (derived from SIR model), as presented by Dietz. # Date : 2011/11/09 # Author : Boelle, Obadia ############################################################################### # Function declaration est.R0.AR <- function#Estimate R0 from attack rate of an epidemic ### Estimate R0 from attack rate of an epidemic. ##details<< For internal use. Called by est.R0. ##details<< In the simple SIR model, the relation between R0 and the Attack Rate is in the form \eqn{R0 = -ln((1-AR)/S0) / (AR - (1-S0))}. ##note<< This is the implementation of the formula by Dietz (1993). ##references<<Dietz, K. "The Estimation of the Basic Reproduction Number for Infectious Diseases." Statistical Methods in Medical Research 2, no. 1 (March 1, 1993): 23-41. (AR=NULL, ##<< Attack rate as a percentage from total population incid=NULL, ##<< Sum of incident cases, possibly in the form of a vector of counts. pop.size=NULL, ##<< Population size in which the incident cases were observed. ##details<< If the population size is provided, the variance of R0 is estimated using the delta method. ## The hypothesis are that of homogeneous mixing, no more transmission (epidemic ended), no change in transmission or interventions during the epidemic. This estimate may be correct in closed populations, and may be less valid in other cases. S0=1, ##<< Initial proportion of the population considered susceptible. ##details<< The correction for incomplete susceptibility is based on the SIR model equations. checked=FALSE, ##<< Internal flag used to check whether integrity checks were ran or not. ... ##<< parameters passed to inner functions ) # Code { # Various class and integrity checks if (checked == FALSE) { integrity.checks(epid, t, GT=NULL, begin=NULL, end=NULL, date.first.obs=NULL, time.step=NULL, AR, S0, methods="AR") } if (!is.null(incid)) { epid <- check.incid(incid) } else { epid <- NULL } #Required : either (AR, incidence) or (AR, pop.size) to start simulation if (is.null(AR) & any(c(is.null(incid),is.null(pop.size)))) { stop("Either 'AR' alone or both 'AR / incid' and 'pop.size' must be provided") } #If Attack Rate is not provided, it's computed as sum(incid)/pop.size if (is.null(AR)) { #if incid provided as a series of incident cases, first sum if (length(incid) > 1) { incid = sum(incid) } if (any(c(incid,pop.size) <= 0 )){ stop(paste("'incid'=",incid," and 'pop.size'=",pop.size," must be nonnegative")) } if (pop.size < incid){ stop(paste("'pop.size'=",pop.size," must be greater than 'incid'=",incid)) } #Actual AR is now computed AR <- incid/pop.size } #AR could also be provided else { #Obviously AR is between 0 and 1 if (AR <=0 | AR >= 1) { stop(paste("'AR'=",AR," must be between 0 and 1")) } if (is.null(pop.size)) { pop.size <- NA } } #R0 is derived from Attack Rate based on SIR model (see Dietz) R0.from.AR = function(AR, S0) {-log((1-AR)/S0)/(AR - (1-S0))} R0 = R0.from.AR(AR,S0) ##details<< CI is computed for the attack rate considering the population size (\eqn{CI(AR) = AR +/- 1.96*sqrt(AR*(1-AR)/n)}), ## and so the CI for the reproduction number is computed with this extreme values. CI95 <- c(R0.from.AR(AR-1.96*sqrt(AR *(1-AR)/pop.size),S0),R0.from.AR(AR+1.96*sqrt(AR *(1-AR)/pop.size),S0)) # variance of R0 is estimated using Delta method. var.R0 <- ((-((-1 + AR + S0)/(-1 + AR)) + log((1 - AR)/S0))/(-1 + AR + S0)^2) * AR *(1-AR)/pop.size return(structure(list(epid=epid, R=R0, var=var.R0, conf.int = CI95, begin.nb=1, end.nb=length(incid), AR=AR, method="Attack Rate", method.code="AR"),class="R0.R")) ##value<< ## A list with components: ## \item{epid}{The vector of incidence, after being correctly formated by check.incid. Used only by plot.fit.} ## \item{R}{The estimate of the reproduction ratio.} ## \item{conf.int}{The 95% confidence interval for the R estimate.} ## \item{AR}{Original attack rate.} ## \item{begin.nb}{First date of incidence record. Used only by plot.fit.} ## \item{end.nb}{Last date of incidence record. Used only by plot.fit.} ## \item{method}{Method used for the estimation.} ## \item{method.code}{Internal code used to designate method.} }

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#' A function for quickly transposing data.table objects #' #' This function allows you transpose data.table objects very rapidly, whilst maintaining control of row and column names. #' @param dt The data.table object you wish to transpose. #' @param transCol The name of the column that you wish to pivot on. Values in this column will become the new colnames. #' @param rowID This will be the name of the new rownames column #' @keywords data.table #' @export #' @examples #' transDT() transDT <- function(dt, transCol, rowID){ newRowNames <- colnames(dt) newColNames <- dt[, transCol, with = F] transposedDt <- transpose(dt[, !colnames(dt) %in% transCol, with = F]) colnames(transposedDt) <- unlist(newColNames) transposedDt[, rowID] <- newRowNames[newRowNames != transCol] return(transposedDt) }

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#' MSE data format #' #' The function \code{MSEdata()} transforms an existing dataframe to the "MSE" format, #' ensuring it contains a "count" column and that the other columns refer to #' inclusion (1) or exclusion (0) on a set of lists. #' #' Zero counts of unobserved capture patterns are added and duplicates capture patterns #' are aggregated. #' #' @param data Original MSE dataframe. It should contain a column named "count" #' with the observed counts of capture patterns, as well as columns representing #' the different lists, as follows: #'\preformatted{ c1 c2 count #' 0 1 7 #' 1 0 3 #' 1 1 4} #' #' @seealso \code{\link{plotMSE}} #' @export MSEdata <- function(data) { assert(inherits(data, "data.frame")) # Validate count column assert("count" %in% names(data), msg = "A column named 'count' should be specified.") assert(is.numeric(data$count), all(data$count >= 0), all((data$count %% 1) == 0), msg = "Count column should only contain non-negative integers.") # Validate other columns listnames = base::setdiff(names(data), "count") for (list in listnames) { assert(is.numeric(data[,list, drop=TRUE])) assert(all(data[,list, drop=TRUE] %in% c(0,1)), msg="List columns can only contain zeros and ones.") } data = clean_MSE_data(data) attr(data, "class") <- c("MSEdata", attr(data, "class")) return(data) } #' Standardize MSE data format #' #' @param data MSE dataframe to be cleaned up. #' #' @importFrom dplyr %>% clean_MSE_data <- function(data) { nlists = ncol(data) - 1 data = data %>% group_by_at(vars(-count)) %>% count(wt=count, name="count") %>% ungroup() # Binary table with all combinations of zeros and ones X = eval(parse(text= paste0("table(", paste0(rep("c(0,1)", nlists), collapse=","), ")") )) %>% as.data.frame.table %>% map_dfc(as.numeric) - 1 # Removing the count for unobserved cases and removing superfluous column X = X[2:nrow(X), 1:nlists] X = data.frame(integer.base.b(1:(2^nlists - 1), 2)) # Match column names of the data to those of the binary matrix listnames = setdiff(names(data), "count") colnames(X) = listnames # Join the binary table with the observed counts result = left_join(X, data, by=listnames) # Reorder observations o1 = order(rowApply(result, function(x) paste0(x, collapse=""))) result = result[rev(o1),] o2 = order(rowSums(result[, listnames])) result = result[o2,] # Set NA counts to zero result[is.na(result[,"count"]), "count"] = 0 rownames(result) = 1:nrow(result) return(result) } #' Inheritance check #' @param data MSE dataframe. is.MSEdata <- function(data) { inherits(data, "MSEdata") } #' Get list names #' #' @param mse_data object of class `MSEdata`. #' @return names of the MSE lists. #' #' @export list.names <- function(mse_data) { assert(is.MSEdata(mse_data)) return(base::setdiff(names(mse_data), "count")) } #' Set list names #' #' @param mse_data MSE dataframe. #' @param value list of names. #' `list.names<-` <- function(mse_data, value) { assert(is.MSEdata(mse_data)) assert(length(value) == ncol(mse_data)-1) colnames(mse_data)[colnames(mse_data) != "count"] = value mse_data } #' Number of observed cases #' #' @param mse_data MSE dataframe. nobs.MSEdata <- function(mse_data) { assert(is.MSEdata(mse_data)) return(sum(mse_data$count)) } #' Number of lists #' @param mse_data MSE dataframe. nlists <- function(mse_data) { assert(is.MSEdata(mse_data)) return(length(list.names(mse_data))) } #' @param mse_data MSE dataframe. #' @param lists lists to omi. omit <- function(mse_data, lists) { assert(is.MSEdata(mse_data)) cols = setdiff(names(mse_data), lists) return(MSEdata(mse_data[, cols])) } #' Merge lists #' #' @param mse_data MSEdata object from which to merge lists. #' @param ... names of the lists to be merged. #' merge <- function(mse_data, ...) { assert(is.MSEdata(mse_data)) args = list(...) data = MSEdata(mse_data) for (lists in args) { data[paste(lists, collapse="-")] <- 1*(rowSums(data[,lists]) > 0) } data = data[, setdiff(names(data), unlist(args))] return(MSEdata(data)) }

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#' Imputation with ARIMA Model #' @param individualDataset individual dataset #' @return The dataset after imputed by ARIMA Model #' @example impu_arima(aList(clean(dataset))[[1]]) #' @export # install.packages("forecast") # library("forecast") impu_arima <- function(individualDataset) { impu <- naInsert(individualDataset) t <- impu[,1] x0 <- x <- zoo(unfactor(impu[,3]), t) fit <- auto.arima(x) kr <- KalmanRun(x, fit$model) id.na <- which(is.na(x)) for (i in id.na) { x[i] <- fit$model$Z %*% kr$states[i,] } plot(x0, xlab = "DisplayTime", ylab = "GlucoseValue") points(t[id.na], x[id.na], col = "red", pch = 20) # print(t[id.na]) # print(x[id.na]) return(x) }

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geolevel_get_empty_geometry.R

# empty geometry ---------------------------------------------------------- #' Get empty geometry instances #' #' Get the instances of the data table that do not have associated geometry for #' the specified geometry type. #' #' @param gl A `geolevel` object. #' @param geometry A string, type of geometry of the layer. #' #' @return A `tibble`. #' #' @family level definition functions #' @seealso #' #' @examples #' library(tidyr) #' library(sf) #' #' us_state_point <- #' coordinates_to_geometry(layer_us_state, #' lon_lat = c("intptlon", "intptlat")) #' #' state <- #' geolevel(name = "state", #' layer = layer_us_state, #' key = c("geoid")) %>% #' add_geometry(layer = us_state_point) #' #' empty_geometry_instances <- state %>% #' get_empty_geometry_instances(geometry = "point") #' #' @export get_empty_geometry_instances <- function(gl, geometry = NULL) { UseMethod("get_empty_geometry_instances") } #' @rdname get_empty_geometry_instances #' @export get_empty_geometry_instances.geolevel <- function(gl, geometry = NULL) { stopifnot(geometry %in% names(gl$geometry)) if (is.null(geometry)) { geometry <- names(gl$geometry)[1] } gl$data[!(gl$data[[1]] %in% gl$geometry[[geometry]][[1]]), ] }

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setwd("E:\\MegaSync\\MEGAsync\\R\\tryByself") data <- read.table("babies.csv", header = TRUE, sep = ",") # Missing compentsation. for(i in 1:7){ n_null <- is.na(data[,i]) n_mean <- mean(data[,i], na.rm = TRUE) data[n_null,i] <- n_mean } # Slipt to Train and Test. n = 0.3*nrow(data) n_test = sample(1:nrow(data),n) train = data[-n_test,] test = data[n_test,] # Modeling. library(rpart) baby.tree=rpart(bwt~. ,data=train) baby.tree plot(baby.tree) text(baby.tree , cex=.6) # Prediction for train predicted <- predict(baby.tree,data = train) yTest = train$bwt train.MAPE <- mean(abs(yTest-predicted)/yTest) cat("MAPE(train)=",train.MAPE*100,"%\n") # Prediction for test predicted <- predict(baby.tree,newdata = test) yTest = test$bwt test.MAPE <- mean(abs(yTest-predicted)/yTest) cat("MAPE(test)=",test.MAPE*100,"%\n")

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model.R

# Take pixels from map viewport and produce bluesky input model_inputs <- function(file, data, name, size, type, tz) { # Convert all times to local time as specified by user data <- mutate(data, StartTime = lubridate::with_tz(StartTime, tz)) hourly <- get_hourly_data(data) profile <- get_diurnal_profile(hourly, name, tz) daily <- create_bluesky_daily(hourly, name, size, type, tz) zfile <- zip_files(file, daily, profile, hourly, name) } # Get Hourly Profile redux (The S2 version using FRE and per pixel profiles) get_hourly_data <- function(df) { # Count valid power values by location - need at least 2 to interpolate, otherwise use # the minimum value of 75 valids <- df %>% dplyr::group_by(lon, lat) %>% dplyr::summarise(ValidCount = sum(is.finite(PM25))) invalids <- dplyr::filter(valids, ValidCount < 2) valids <- dplyr::filter(valids, ValidCount >= 2) hourly <- df %>% dplyr::inner_join(valids, by = c("lon", "lat")) %>% dplyr::group_by(lon, lat) %>% dplyr::mutate(Interpolated = imputeTS::na_interpolation(Power), InterpolatedPM = imputeTS::na_interpolation(PM25), Hour = lubridate::round_date(StartTime, unit = "hour")) %>% dplyr::group_by(lat, lon, Hour) %>% dplyr::summarise(Power = mean(Interpolated, na.rm = TRUE), PM25 = sum(InterpolatedPM, na.rm = TRUE), Count = n()) %>% dplyr::filter(is.finite(Power)) %>% dplyr::mutate(FRE = Power * 3600) # MW * s = MJ hourly_invalids <- df %>% dplyr::inner_join(invalids, by = c("lon", "lat")) %>% dplyr::group_by(lon, lat) %>% dplyr::mutate(Hour = lubridate::round_date(StartTime, unit = "hour")) %>% dplyr::group_by(lat, lon, Hour) %>% dplyr::summarise(Power = 75, PM25 = 5, Count = n()) %>% dplyr::filter(is.finite(Power)) %>% dplyr::mutate(FRE = Power * 3600) # MW * s = MJ hourly <- dplyr::bind_rows(hourly, hourly_invalids) %>% dplyr::ungroup() %>% dplyr::mutate(Day = lubridate::floor_date(Hour, unit = "days")) } get_diurnal_profile <- function(hourly, name, tz) { # Create daily fraction of total FRE - currently the profile applies to the entire event profile <- hourly %>% dplyr::group_by(Hour) %>% dplyr::summarise(HourlyFRE = sum(FRE, na.rm = TRUE)) %>% dplyr::mutate(Day = lubridate::floor_date(Hour, unit = "days")) daily <- profile %>% dplyr::group_by(Day) %>% dplyr::summarise(DailyFRE = sum(HourlyFRE)) profile <- inner_join(profile, daily, by = "Day") %>% dplyr::mutate(FractionOfDay = HourlyFRE / DailyFRE, Fire = name, LocalDay = strftime(Day, format = "%Y-%m-%d"), LocalHour = strftime(Hour, format = "%Y-%m-%d %H:00", tz = tz)) %>% dplyr::select(LocalDay, LocalHour, FractionOfDay, Fire) # Fill in missing hours with zeroes days <- dplyr::select(profile, LocalDay) %>% dplyr::distinct() %>% .$LocalDay h <- seq.int(0, 23) all_hours <- paste0(" ", sprintf("%02d", h), ":00") complete_set <- tidyr::crossing(days, all_hours) %>% dplyr::mutate(LocalHour = paste0(days, all_hours), Fire = name) %>% dplyr::select(LocalDay = days, LocalHour, Fire) profile <- dplyr::left_join(complete_set, profile, by = c("LocalDay", "LocalHour", "Fire")) %>% dplyr::mutate(FractionOfDay = dplyr::if_else(is.na(FractionOfDay), 0, FractionOfDay)) } create_bluesky_daily <- function(df, fire_name, final_area, type, tz) { # Convert hourly FRE to per pixel daily area total_FRE <- sum(df$FRE, na.rm = TRUE) daily <- df %>% dplyr::mutate(Day = lubridate::floor_date(Hour, "days")) %>% dplyr::group_by(lon, lat, Day) %>% dplyr::summarise(FRE_Daily = sum(FRE, na.rm = TRUE)) %>% dplyr::mutate(Fraction = FRE_Daily / total_FRE, area = Fraction * final_area) # Create an id for each location locs <- daily %>% dplyr::ungroup() %>% dplyr::select(lon, lat) %>% dplyr::distinct() %>% dplyr::mutate(id = dplyr::row_number()) # Convert to bluesky fire_locations format daily %>% dplyr::inner_join(locs, by = c("lon", "lat")) %>% dplyr::mutate(id = paste(fire_name, id, sep = "_"), event_id = fire_name, fire_type = type, date_time = strftime(Day, format = "%Y%m%d0000%z", tz = tz), date_time = paste0(stringr::str_sub(date_time, 1, 15), ":00")) %>% dplyr::select(id, Day, event_id, fire_type, date_time, latitude = lat, longitude = lon, area) } ## Create three csv files for each day, and zip them up zip_files <- function(file, points, profile, hourly, name) { dir.create(t_dir <- tempfile()) days <- points %>% dplyr::ungroup() %>% dplyr::select(Day) %>% dplyr::distinct() %>% .$Day bluesky_files <- function(day, points, profile, hourly, name, temp_dir) { # filenames based on fire name and timestamp points_name <- paste0(temp_dir, "/", "fire_locations_", strftime(day, format = "%Y%m%d_"), name, ".csv") profile_name <- paste0(temp_dir, "/", name, "_diurnal_profile_localtime_", strftime(day, format = "%Y%m%d"), ".csv") hourly_name <- paste0(temp_dir, "/", name, "_hourly_localtime_", strftime(day, format = "%Y%m%d"), ".csv") # subset data by day points <- dplyr::filter(points, Day == day) profile <- dplyr::filter(profile, LocalDay == as.character(day)) hourly <- dplyr::filter(hourly, Day == day) readr::write_csv(points, points_name) readr::write_csv(profile, profile_name) readr::write_csv(hourly, hourly_name) } # Add a summary of acres and pm2.5 (in tons) per day areas <- points %>% group_by(Day) %>% summarise(Area_acres = sum(area)) by_day <- hourly %>% group_by(Day) %>% summarise(PM25_tons = sum(PM25) / 907.185) %>% inner_join(areas, by = "Day") %>% mutate(TonsPerAcre = PM25_tons / Area_acres) by_day_name <- paste0(t_dir, "/", "daily_totals_", name, ".csv") readr::write_csv(by_day, by_day_name) purrr::walk(days, bluesky_files, points, profile, hourly, name, t_dir) tar(file, t_dir, compression = "gzip") return(t_dir) }

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profile_most_probable_kmer.R

source("/home/giri/Downloads/MTech Thesis/coursera programs/find_probability_and_best_probabilty.R") profile_most_probable_kmer = function(text, k, profile_matrix){ probability = 0 pmp_kmer = 0 for (i in 1:(nchar(text)-k+1)) { kmer = substr(text, i, i+k-1) pp = find_probability(kmer, profile_matrix) if(pp > probability){ probability = pp pmp_kmer = kmer } } if(pmp_kmer == 0) pmp_kmer = substr(text, 1, k) return(pmp_kmer) } dna_set = c("TCGGGGGTTTTT","CCGGTGACTTAC","ACGGGGATTTTC","TTGGGGACTTTT","AAGGGGACTTCC","TTGGGGACTTCC","TCGGGGATTCAT","TCGGGGATTCCT", "TAGGGGAACTAC","TCGGGTATAACC") Greedy_or_Laplace = 0 result = Scope(dna_set, Greedy_or_Laplace) text = "AAGAATCAGTCA" k = 6 # profile_matrix = rbind(c(0,0,0),c(0,0,1),c(1,1,0),c(0,0,0)) pmp_kmer = profile_most_probable_kmer(text, k, result[[3]]) print(pmp_kmer)

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/data/genthat_extracted_code/likelihoodExplore/examples/likcauchy.Rd.R

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likcauchy.Rd.R

library(likelihoodExplore) ### Name: likcauchy ### Title: Cauchy Log Likelihood Function ### Aliases: likcauchy ### ** Examples likcauchy(x = rcauchy(n = 2))

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dungeon_i10-m10-u5-v0.pddl_planlen=106.R

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/R-code/RNA-Seq/Radigoraphic_groups/Progression_vs_No_Progression/Progression.R

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shrumin/Rheumatoid_Arthritis---Analysis-and-visualisation-of-autoantibody-profiles-of-rheumatoid-arthritis-

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Progression.R

setwd("~/Desktop/RA") dat <- read.csv(file="Progression_data.csv", row.names=1) #filtering and naming genes library(biomaRt) ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl") nonprotein=biomaRt::getBM(attributes = c("ensembl_transcript_id", "transcript_version", "ensembl_gene_id", "external_gene_name", "entrezgene_id", "description", "gene_biotype"), filters='biotype', values=c("rRNA"), mart = ensembl) #All protein coding genes t2g <- biomaRt::getBM(attributes = c("ensembl_transcript_id", "transcript_version", "ensembl_gene_id", "external_gene_name", "entrezgene_id", "description", "gene_biotype"), filters='biotype', values="protein_coding", mart = ensembl) #Ribosomal proteins ribo.proteins <- unique(t2g$ensembl_gene_id[grep("ribosomal protein", t2g$description)]) rRNA.proteins <- unique(nonprotein$ensembl_gene_id[grep("rRNA", nonprotein$biotype)]) #remove version from identifier rownames(dat) <- sub("\\.\\d+", "", rownames(dat)) #remove any ribosomal proteins from the RNA-Seq from the counts dat <- dat[!rownames(dat) %in% ribo.proteins,] dat <- dat[!rownames(dat) %in% rRNA.proteins,] #design exp table without acetylated colTable <- read.csv("Progression_exp_info.csv",row.names=1) #Deseq analysis library(DESeq2) dds <- DESeqDataSetFromMatrix(countData=dat,colData=colTable,design= ~ group) keep <- rowSums(counts(dds) >=10) >= 10 dds <- dds[keep,] dds <- DESeq(dds,test="LRT",reduced = ~1) resultsNames(dds) #results res_Progression<- results(dds,contrast=c("group","Progession","No_Progession")) res_Progression_sort=res_Progression[order(res_Progression$padj),] res_Progression_sig<- subset(res_Progression_sort,padj<0.05) res_Progression_sig$symbol <- t2g$external_gene_name[match(rownames(res_Progression_sig), t2g$ensembl_gene_id)] res_Progression_sig$entrezid <- t2g$entrezgene[match(rownames(res_Progression_sig), t2g$ensembl_gene_id)] res_Progression_sig=na.omit(res_Progression_sig) write.csv(res_Progression_sig, "sig_Progression_vs_No_Progression.csv", row.names=TRUE) #normalised count normalized_counts <- counts(dds, normalized=TRUE) write.csv(normalized_counts, file="normalized_counts_Progression.csv", quote=F, col.names=NA)

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sabank/ExData_Plotting1

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plot2.R

### This program describes the sequence of actions undertaken to plot data as scatter plot. ### It takes 3 arguments, start date, end date, variable column number. plot2 <- function(x="2007-02-01",y="2007-02-02",z=3){ ## defines path to and reads txt file and replace "?" by NA path <- file.path(getwd(),"03_data/household_power_consumption.txt") file <- read.table(path,header=TRUE,sep=";", na.strings ="?") ## coerce chr to date in variable 'Date' (i.e column 1) file$Date <- as.Date(file$Date, "%e/%m/%Y") ## subset rows between dates 'x' and 'y' data <- file[file$Date >= x & file$Date <= y,] ## convert chr to time in variable 'Time' (i.e column 2) data$Time <- as.POSIXct(paste(data$Date, as.character(data$Time))) ## prepare header for plot's title and labels # replace "_" by " " in columns name colnames(data)<-gsub("_"," ",colnames(data)) # split header name when reaching " ". Result is a 'list' of items composed of words. headername <- strsplit(colnames(data), " ") # casefolding to uppercase the 1st letter of each word in each item of the list for (i in 1:length(headername)){ # take 1st letter of each word l1 <- substring(headername[[i]],1,1) # take remaining letters of each word l2 <- substring(headername[[i]],2) # translate all 1st letters in upper case l1 <- toupper(l1) # concatenate strings l1 and l2 and replace each item of the list # paste0(...,collapse) equivalent to paste(...,sep="",collapse) headername[[i]] <- paste0(l1, l2, collapse=" ") } # update columns name colnames(data)<-headername ## plot data 'Global active power' (i.e 3rd column) as scatter plot type line plot(data$Time, data[,z], "l", xlab= "", ylab=paste(colnames(data[z]),"(kilowatts)"), font.axis = 1) ## export plot in png file in working directory dev.copy(png,"plot2.png", width = 480, height = 480) dev.off() }

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/R/get_node_attr_from_selection.R

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statwonk/DiagrammeR

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get_node_attr_from_selection.R

#' Get node attributes based on a selection of nodes #' @description From a graph object of class \code{dgr_graph}, get node #' attribute properties for nodes available in a selection. #' @param graph a graph object of class \code{dgr_graph} that is created #' using \code{create_graph}. #' @examples #' \dontrun{ #' library(magrittr) #' #' # Create a simple graph #' nodes <- #' create_nodes(nodes = c("a", "b", "c", "d"), #' type = "letter", #' label = TRUE, #' value = c(3.5, 2.6, 9.4, 2.7)) #' #' edges <- #' create_edges(from = c("a", "b", "c"), #' to = c("d", "c", "a"), #' rel = "leading_to", #' color = c("pink", "blue", "red")) #' #' graph <- #' create_graph(nodes_df = nodes, #' edges_df = edges) #' #' # Select nodes "a" and "c" in the graph and get the node #' # attributes for that selection #' graph %>% select_nodes(nodes = c("a", "c")) %>% #' get_node_attr_from_selection() #' #> nodes type label value #' #> 1 a letter a 3.5 #' #> 3 c letter c 9.4 #' } #' @return a node data frame. #' @export get_node_attr_from_selection get_node_attr_from_selection <- function(graph){ if (is.null(graph$selection$nodes)){ stop("There is no selection of nodes available.") } nodes_df <- get_node_attr(graph, graph$selection$nodes) return(nodes_df) }

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/1 linear regression plots Wk2.R

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wrona-42067898/Linear-Regression

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2020-04-15T01:21:56.400491

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1 linear regression plots Wk2.R

###Linear Regression 2 discrete variables### library(statsr) library(dplyr) library(ggplot2) data(mlb11) #Ask whether variable x predicts variable y (y is response variable) #Scatter plot with linear model overlayed ggplot(data = mlb11, aes(x=runs, ?=new_onbase)) + geom_point() + stat_smooth(method = "lm", se = FALSE) #Save the linear model information in a variable, can be accessed with summary() my_lm <- lm(runs ~ new_onbase, data = mlb11) #Check 1) Plot of the residuals, are they uniformly di?tributed? ggplot(data=my_lm, aes(x=.fitted, y=.resid)) + geom_point() + geom_hline(yintercept=0, linetype="dashed") + xlab("Fitted values") + ylab("Residuals") #Check 2) Is the histogram of residuals approximately normal? ggplot(data = my_lm, ae?(x = .resid)) + geom_histogram(binwidth = 25) + xlab("Residuals") #Check 3) Does QQ plot suggest a linear relationship? ggplot(data = my_lm, aes(sample = .resid)) + stat_qq() ######################################################## summary(my_lm) #new_obs R2=0.9349 #new_slug R2=0.8969 #new_onbase R2=0.8491 #Example of comparing 4 different variables to see the best predictor of runs summary(lm(runs ~ at_bats, data = mlb11))$r.squared summary(lm(runs ~ hits, data = mlb11))$r.squared summary(lm(runs?~ wins, data = mlb11))$r.squared summary(lm(runs ~ bat_avg, data = mlb11))$r.squared

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/app.R

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ajay-aggarwal01/capStoneProject

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2020-06-23T03:20:54.595736

2019-08-01T01:46:44

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app.R

# # This is a Shiny web application. You can run the application by clicking # the 'Run App' button above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # library(shiny) # Define UI for application that draws a histogram ui <- fluidPage( titlePanel("Predict Next Word"), tabsetPanel( type='tab', tabPanel("App Main Page", sidebarLayout( sidebarPanel( helpText("Application Instruction:",br(), "Type some text into the text box under the \"Text Input your phrase here\" heading"), textInput('userInput',label="Input your phrase here:",value=""), #actionButton('goButton',"Guess!"), br(), helpText("Note:",br(), "The following predicted word will show up automatically as you input.")), mainPanel( h4("Here are the top 10 predictions:"), ##verbatimTextOutput('guess') verbatimTextOutput('guess') ## cat(paste(text1, text2, text3, sep="\n")) ##textOutput('guess') ) ) ), tabPanel( "Data Summary", h4("Introduction"), p("Application: Text Prediction Application."), p("The purpose of this project is to build a predictive text models. When someone types: 'I went to the' "), p("the keyboard presents then options for what the next word might be. "), h4("Text Prediction Model"), p("Prediction model will be based on backoff model in NLP. I have used 4-grams to calculate the probability of a word in text. Model will go back to a n-1 gram level to calculate the probabilities of finding a word with prob=0."), h3("Data Details"), imageOutput("datasummary") ), tabPanel("Exploratory Analysis", p("Exploratory Analysis of the data involves understanding the distribution of words and relationship between the words in the corpora. - Calculated the frequencies of words and word pairs - build figures and tables to understand variation in the frequencies of words and word pairs in the data"), h3("Unigram bar data analysis"), p("An n-gram consisting of a single item from a sequence of words in a text file"), p("Following displays the bar and word cloud of frequency distribution of a single words"), imageOutput("barngram1"), h3("Unigram wordcloud data analysis"), imageOutput("wcngram1"), h3("Bigram bar data analysis"), p("An n-gram consisting of a two item from a sequence of words in a text file"), p("Following displays the bar and word cloud of frequency distribution of a two words"), imageOutput("barngram2"), h3("Bigram wordcloud data analysis"), imageOutput("wcngram2"), h3("Triigram bar data analysis"), p("An n-gram consisting of a three item from a sequence of words in a text file"), p("Following displays the bar and word cloud of frequency distribution of a three words"), imageOutput("barngram3"), h3("Trigram wordcloud data analysis"), imageOutput("wcngram3"), h3("Quadgram bar data analysis"), p("An n-gram consisting of a four item from a sequence of words in a text file"), p("Following displays the bar and word cloud of frequency distribution of a four words"), imageOutput("barngram4"), h3("Quadgram wordcloud data analysis"), imageOutput("wcngram4"), h3("Pentagram bar data analysis"), p("An n-gram consisting of a five item from a sequence of words in a text file"), p("Following displays the bar and word cloud of frequency distribution of a five words"), imageOutput("barngram5"), h3("Bigram wordcloud data analysis"), imageOutput("wcngram5"), h5("For more information of exploratory analysis of dateset, please refer to my milestone document."), p(" "), p(""), a(p("LINK"), href="http://rpubs.com/ajay_jalan/508765") ), hr(), h4("Author: Ajay Aggarwal :-)", p(""), p(""), a(p("Github Repo."), href="https://github.com/ajay-aggarwal01/capStoneProject") ) ) ) isValid <- function(input) { if (length(input) == 0) FALSE else if (length(input[grep("^\\W+$", input, perl = TRUE)])) FALSE else if (length(input[grep("^\\d+$", input, perl = TRUE)])) FALSE else if (length(input) == 1 && input[1] == "") FALSE else if (length(input) == 1 && input[1] != "") TRUE else FALSE } library(shiny) ##setwd("D:/Work/mystuff/Education/DataScience/DataScienceCapstone") source("W3_Assignment.R") # Define server logic required to draw a histogram server <- function(input, output) { print("Request received!") reactiveInputHandler1 <- reactive( { if (isValid(input$inputText)) return(as.character(input$inputText)) else return("<Please use a valid input>") } ) output$datasummary <- renderImage( { return(list( src = "dataSummary.png", contentType = "image/png", width = 600, height = 400, alt = "Face" )) }, deleteFile = FALSE) output$barngram1 <- renderImage( { return(list( src = "bar_ngram1.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$wcngram1 <- renderImage( { return(list( src = "wc_ngram1.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$barngram2 <- renderImage( { return(list( src = "bar_ngram2.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$wcngram2 <- renderImage( { return(list( src = "wc_ngram2.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$barngram3 <- renderImage( { return(list( src = "bar_ngram3.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$wcngram3 <- renderImage( { return(list( src = "wc_ngram3.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$barngram4 <- renderImage( { return(list( src = "bar_ngram4.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$wcngram4 <- renderImage( { return(list( src = "wc_ngram4.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$barngram5 <- renderImage( { return(list( src = "bar_ngram5.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) output$wcngram5 <- renderImage( { return(list( src = "wc_ngram5.png", contentType = "image/png", width = 200, height = 200, alt = "Face" )) }, deleteFile = FALSE) #Display user's input output$otext<- renderText(reactiveInputHandler1()) output$otext<-renderPrint( {as.character(input$input_text)}) dataInput <- reactive( { as.character(nextWord(input$userInput)$nextword)[1:10] ##sizeL <- length(x) ##print(sizeL) ##resultString <- "" ##currentString <-"" ##for(i in sizeL){ ##currentString <- paste(i,x[i], sep = " ") ##resultString <- paste(resultString, currentString, sep = "\n") ##} ##return(resultString) } ) output$guess <- renderPrint({dataInput() } ) } # Run the application shinyApp(ui = ui, server = server) # Application title ##titlePanel("Old Faithful Geyser Data"), # Sidebar with a slider input for number of bins ##sidebarLayout( ##sidebarPanel( ##sliderInput("bins", ## "Number of bins:", ## min = 1, ## max = 50, ## value = 30) ## ), # Show a plot of the generated distribution ## mainPanel( ##plotOutput("distPlot") ##) ##) ##output$distPlot <- renderPlot({ # generate bins based on input$bins from ui.R ##x <- faithful[, 2] ##bins <- seq(min(x), max(x), length.out = input$bins + 1) # draw the histogram with the specified number of bins ##hist(x, breaks = bins, col = 'darkgray', border = 'white') ##})

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davesteps/insertPipe

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hello.R

insertPipe <- function() { rstudioapi::insertText(" %>% \n") } insertFun <- function() { rstudioapi::insertText("<- function(){\n}") }

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HenrikBengtsson/R.graphics

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resetOptions.Device.Rd

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % Device.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{resetOptions.Device} \alias{resetOptions.Device} \alias{Device.resetOptions} \alias{resetOptions.Device} \alias{resetOptions,Device-method} \title{Reset the current default options for a given device type} \description{ Reset the current default options for a given device type. Resets the options for a given device type, e.g. bitmap, pictex and postscript. } \synopsis{resetOptions.Device(static, deviceType=c("bitmap", "pictex", "postscript"), ...)} \usage{Device$resetOptions(deviceType=c("bitmap", "pictex", "postscript"), ...)} \arguments{ \item{deviceType}{A \code{\link[base]{character}} string.} \item{...}{Not used.} } \value{Returns nothing.} \author{Henrik Bengtsson (\url{http://www.braju.com/R/})} \seealso{ \code{\link[R.graphics:getOptions.Device]{*getOptions}()} and \code{\link[R.graphics:setOptions.Device]{*setOptions}()}. } \keyword{dplot} \keyword{internal} \keyword{methods}

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Leukemia.Rd

\name{Leukemia} \alias{Leukemia} \docType{data} \title{Responses to Treatment for Leukemia} \description{ Treatment results for leukemia patients } \format{ A data frame with 51 observations on the following 9 variables. \tabular{rl}{ \code{Age} \tab {Age at diagnosis (in years)}\cr \code{Smear} \tab {Differential percentage of blasts}\cr \code{Infil} \tab {Percentage of absolute marrow leukemia infiltrate}\cr \code{Index} \tab {Percentage labeling index of the bone marrow leukemia cells}\cr \code{Blasts} \tab {Absolute number of blasts, in thousands}\cr \code{Temp} \tab {Highest temperature of the patient prior to treatment, in degrees Fahrenheit}\cr \code{Resp} \tab {\code{1}=responded to treatment or \code{0}=failed to respond}\cr \code{Time} \tab {Survival time from diagnosis (in months)}\cr \code{Status} \tab {\code{0}=dead or \code{1}=alive}\cr } } \details{ A study involved 51 untreated adult patients with acute myeloblastic leukemia who were given a course of treatment, after which they were assessed as to their response. } \source{ Data come from Statistical Analysis Using S-Plus (Brian S. Everitt; first edition 1994, Chapman & Hall). } \keyword{datasets}

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/BCdata-class-methods.R \name{getBackbone} \alias{getBackbone} \title{Accessing the Barcode Backbone slot of a BCdat objects.} \usage{ getBackbone(object) } \arguments{ \item{object}{a BCdat object.} } \value{ A character string. } \description{ Accessing the Barcode Backbone slot of a BCdat objects. } \examples{ data(BC_dat) getBackbone(BC_dat) }

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# it recommended that you put the below code inside your shinyServer() function so that when PortableChrome Closes it will also close the underlying R session session$onSessionEnded(function() { q("no") })

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# DSS Coursera.org # Exploratory Data Analysis # Week 4 # Programming Assignment # Data source: EPA air pollution data - fine particle pollution. # Unwrapping and loading data. library(ggplot2) unzip('exdata-data-NEI_data.zip') # The below variables are used per instructed. NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") # Exploration of the raw data are not coded here. # Instead please take a look at the CodeBook.md. # Question to address: # How have emissions from motor vehicles changed in Baltimore (1999 ~ 2008)? # Step 1: Subset the data regarding 'coal combustion'. # Browse thru data, and target the column 'Short.Name' in SCC. MV <- grep('[mM]otor', SCC$Short.Name) # Find words of motor. MV <- SCC$SCC[MV] # Get source code. dataMV <- subset(NEI, SCC %in% MV & fips == '24510') # Step 2: Create a png file to write-in. png('Plot5.png', width = 960, height = 960) # Step 3: Plot the data: # First look at the distribution, without summing up. g1 <- ggplot(dataMV, aes(year, Emissions)) g1 <- g1 + geom_point(size = 4) + labs(x = 'Year', y = 'Emissions', title = 'PM2.5 by Motor Vehicles in Baltimore (1999 ~ 2008)') # Note there are 2 distinct 'outliers' - way too high compared to others. # While we can't remove them, we want to look at the features of the rest points. g2 <- ggplot(dataMV[dataMV$Emissions<5, ], aes(year, Emissions)) # Note 5 is arbitrary - any lines between 0.2~10 works! g2 <- g2 + geom_point(size = 4) + labs(x = 'Year', y = 'Emissions', title = "Removing 'Outliers'") # Now, let's look at the sum plots with and without the big numbers. aggr <- aggregate(Emissions ~ year, dataMV, sum) g1s <- ggplot(aggr, aes(year, Emissions)) g1s <- g1s + geom_line(col = 'blue', lwd = 2) + labs(x = 'Year', y = 'Total Emissions', title = 'Summary of Annual Sums') aggr2 <- aggregate(Emissions ~ year, dataMV[dataMV$Emissions < 5, ], sum) g2s <- ggplot(aggr2, aes(year, Emissions)) g2s <- g2s + geom_line(col = 'blue', lwd = 2) + labs(x = 'Year', y = 'Total Emissions', title = "Summary of Annual Sums without 'Outliers'") # Co-plot with gridExtra package: cowplot::plot_grid(g1, g1s, g2, g2s, ncol = 2, nrow = 2) # Step 4: Close the device. dev.off()

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fit_first_stage_elasticity.R \name{bootstrap_fs_perc_change} \alias{bootstrap_fs_perc_change} \title{Bootstrap SE Percentage Change Models} \usage{ bootstrap_fs_perc_change(DT, form, y, x_main, x_int, B, quiet) } \arguments{ \item{DT}{a data.table} \item{form}{fomula, first stage formula} \item{y}{character vector, name of response variable(s) without months suffix} \item{x_main}{character (default = "first_mo"), name of instrument} \item{x_int}{character vector, names of variables to interact instrument with} \item{B}{integer, number of bootstrap samples to use when calculating standard errors} \item{quiet}{logical (default = FALSE), if TRUE then does not print progress} } \value{ data.table with standard errors } \description{ Bootstrap SE Percentage Change Models }

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# parallel-job.R # Runs a parallel job in R library(parallel) # Get number of cores from the SLURM_JOB_CPUS_PER_NODE enviromental variable num_cores <- as.integer(Sys.getenv("SLURM_JOB_CPUS_PER_NODE")) print(paste0("I have ", num_cores, " cores ready in R.")) # Run a parallel job print("Using the two cores in parallel.") print("Each core will sleep for 30 seconds and then print the time.") print("If the two printed times are the same then we know the jobs were run in parallel.") print("If the two printed times are 30 seconds apart then we know the jobs were run in serial or sequentially.") out_list <- mclapply(1:2, function(x) { Sys.sleep(30) paste0("Printed Time ", x, ": ", Sys.time()) }, mc.cores = num_cores) print(out_list)

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\name{adjustOne} \alias{adjustOne} \title{Adjust for confounding -- In one single experiment only} \usage{ adjustOne(dwide) } \arguments{ \item{dwide}{iTRAQ data in wide format.} } \description{ Simple code when only one iTRAQ-experiment has been performed. (Code not used anymore.) }

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# ca4_ni # Creates summarised data per practice, per month # Total items # For each file, crate equivalent summarised version # Unzips, processe and cleans-up the extracts # Init required libraries library("readr") library("tidyr") library("dplyr") library(PostcodesioR) library(sf) datapath <- "../../../data" datapath <- "~/courses/dissertation/data" atlas_path <- paste(datapath, "atlas", sep = "/") atlas_path atlas_data <- paste(atlas_path, "atlas_data.censored.csv", sep = "/") #atlas_data <- paste(atlas_path, "atlas_data_lat_long.csv", sep = "/") outdir <- atlas_path output_data <- paste(outdir, "atd_lat_long.csv", sep = "/") atlas_data data_in <- read.csv(atlas_data ) str(data_in) # Check for NA values colSums(is.na(data_in)) # Convert lat/long to UK post code # Deal with non UK possibilities? # rev_geo # create postcode finder for lat/long # lat_long_chk <- function(long_in, lat_in) lat_long_chk <- function(x) { long_in = x["X" ] lat_in = x["Geog"] # rev_geo <- reverse_geocoding(long_in, lat_in ) str(long_in) str(lat_in) rev_geo <- reverse_geocoding(long_in, lat_in , 1, 10, TRUE) str(rev_geo) postcode <- rev_geo[[1]]$postcode[1] str(postcode) return(postcode) } # end function str(data_in) data_sub <- data_in[data_in$G_INCL == "1", ] data_sub$postcode <- apply(data_sub, 1, lat_long_chk ) str(data_sub$postcode) # write.csv(file= output_data, x=data_sub, quote=TRUE, row.names = TRUE) ############################# postcode for atlas data to link to SOA postcode_api_lookup <- function(x) { # str(x) find_code <- x["Postcode" ] # The codes should already be cleaned up ret_code <- "NOTFOUND" tryCatch( { foundcode <- postcode_lookup(find_code) ret_code <- foundcode [c("longitude", "latitude", "postcode", "eastings", "northings")] } , error=function(cond) { ret_code <- "NOTFOUND" }, warning=function(cond) { ret_code <- "NOTFOUND" } ) return(ret_code) } # end function # Get the specific atlas data main_path <- "~/courses/dissertation/data" datapath <- "atlas" datapath <- paste(main_path, datapath, sep = "/") datapath atlas_data <- paste(datapath, "atd_lat_long.csv", sep = "/") clusters_in <- read.csv(atlas_data) names(clusters_in)[names(clusters_in) == 'postcode'] <- 'Postcode' head(clusters_in) clusters_in$Postcode <- as.character(clusters_in$Postcode) # lat_long_data <- do.call(rbind.data.frame, lat_long_data) clusters_in$Postcode <- gsub(' ', '',clusters_in$Postcode) lat_long_data <- apply(clusters_in, 1, postcode_api_lookup) lat_long_data lat_long_data <- do.call(rbind.data.frame, lat_long_data) lat_long_data$postcode <- gsub(' ', '',lat_long_data$postcode) lat_long_data colnames(lat_long_data) <- c("longitude", "latitude", "Postcode", "eastings", "northings") lat_long_data <- subset(lat_long_data, !duplicated(lat_long_data$Postcode)) lat_long_data # docs_in <- cbind(docs_in, lat_long_data) # docs_in clusters_in$Postcode <- gsub(' ', '', clusters_in$Postcode) clusters_in <- merge(x = clusters_in, y = lat_long_data, by.x = "Postcode", by.y = "Postcode") # import the SOA polygon shape file main_path <- "~/courses/dissertation/data" datapath <- "portal_data" datapath <- paste(main_path, datapath, sep = "/") shape_file <- "SOA2011_Esri_Shapefile_0/SOA2011.shp" shape_file <- paste(datapath, shape_file, sep = "/") shape_file aoi_boundary_HARV <- st_read(shape_file) class(aoi_boundary_HARV) st_geometry_type(aoi_boundary_HARV) crs_in <- st_crs(aoi_boundary_HARV) crs_in class(aoi_boundary_HARV) st_bbox(aoi_boundary_HARV) aoi_boundary_HARV pts <- clusters_in[c("eastings", "northings", "PID")] colnames(pts) <- c("x", "y", "PID") pts coordinates(pts) <- ~ x + y class(pts) pts <- st_as_sf(pts) plot(pts) st_crs(pts) pts <- st_set_crs(pts, crs_in) spdf <- as_Spatial(aoi_boundary_HARV) # convert points() pts_sp <- as_Spatial(pts) class(pts_sp) over_results <- over(pts_sp, spdf) over_results clusters_in <- cbind(clusters_in, over_results) clusters_in <- droplevels(clusters_in) # Check all practices have an SOA assigned colSums(is.na(clusters_in)) datapath <- "atlas" datapath <- paste(main_path, datapath, sep = "/") datapath clusters_soa_file <- "clusters_SOA.csv" clusters_soa_file <- paste(datapath, clusters_soa_file, sep = "/") # write.csv(file=clusters_soa_file, x=clusters_in, quote=TRUE, row.names = FALSE) clusters_in$SOA_CODE <- as.character(clusters_in$SOA_CODE) clusters_in$Cluster_Grp <- "2" clusters_in$Cluster_Grp[clusters_in$Structure == "NI_III"] <- "1" clusters_in$Cluster_Grp[clusters_in$Structure == "NI_II"] <- "1" datapath <- "processed_data" datapath <- paste(main_path, datapath, sep = "/") datapath grouped_clusters_file <- "grouped_clusters.csv" grouped_clusters_file <- paste(datapath, grouped_clusters_file, sep = "/") grouped_clusters_file # write.csv(file=grouped_clusters_file, x=clusters_in, quote=TRUE, row.names = FALSE) ################## # SA data main_path <- "~/courses/dissertation/data" datapath <- "portal_data" datapath <- paste(main_path, datapath, sep = "/") shape_file <- "SA2011_Esri_Shapefile_0/SA2011.shp" shape_file <- paste(datapath, shape_file, sep = "/") shape_file aoi_sa_data <- st_read(shape_file) class(aoi_sa_data) st_geometry_type(aoi_sa_data) crs_in <- st_crs(aoi_sa_data) crs_in st_bbox(aoi_sa_data) aoi_sa_data # Need the cluster points data datapath <- "atlas" datapath <- paste(main_path, datapath, sep = "/") datapath clusters_soa_file <- "clusters_SOA.csv" clusters_soa_file <- paste(datapath, clusters_soa_file, sep = "/") clusters_in <- read.csv(clusters_soa_file) pts <- clusters_in[c("eastings", "northings", "PID")] colnames(pts) <- c("x", "y", "PID") pts coordinates(pts) <- ~ x + y class(pts) pts <- st_as_sf(pts) plot(pts) st_crs(pts) pts <- st_set_crs(pts, crs_in) spdf <- as_Spatial(aoi_sa_data) # convert points() pts_sp <- as_Spatial(pts) class(pts_sp) over_results <- over(pts_sp, spdf) over_results clusters_in <- cbind(clusters_in, over_results) clusters_in <- droplevels(clusters_in) # Check all practices have an SOA assigned colSums(is.na(clusters_in)) # write out clusters with SA datapath <- "atlas" datapath <- paste(main_path, datapath, sep = "/") datapath clusters_sa_file <- "clusters_SA.csv" clusters_sa_file <- paste(datapath, clusters_sa_file, sep = "/") # write.csv(file=clusters_sa_file, x=clusters_in, quote=TRUE, row.names = FALSE) ################################### EEE

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dbListTables(mydb) ?dbFetch #step 1: pull every person sqlStatementall <- " select pr.person_id, pr.year_of_birth, pr.month_of_birth, pr.day_of_birth, pr.race_source_value as race, pr.ethnicity_source_value from person pr " rs <- dbSendQuery(mydb,sqlStatementall) fetch(rs, n=-1) dbClearResult(rs) #step 1.b : query cohorts - groups of patients predefined sqlStatementcoh <- " select cd.* from cohort_definition cd " rs <- dbSendQuery(mydb,sqlStatementcoh) fetch(rs, n=-1) dbClearResult(rs) #step 2 : select outcomes sqlStatementout <- " select cd.* , ca.* , cs.* , co.* , pr.person_id, pr.year_of_birth, pr.month_of_birth, pr.day_of_birth, pr.race_source_value as race, pr.ethnicity_source_value from cohort_definition cd join cohort_attribute ca on ca.cohort_definition_id = cd.cohort_definition_id join concept cs on ca.value_as_concept_id = cs.concept_id left join condition_occurrence co on co.condition_concept_id = cs.concept_id left join person pr on pr.person_id = co.person_id where cd.cohort_definition_name='ASCVD' " rs <- dbSendQuery(mydb,sqlStatementout) fetch(rs, n=-1) dbClearResult(rs) #step 2 : select various potential predictors sqlStatementpreds <- " select cd.* , ca.* , cs.* , co.* , pr.person_id, pr.year_of_birth, pr.month_of_birth, pr.day_of_birth, pr.race_source_value as race, pr.ethnicity_source_value from cohort_definition cd join cohort_attribute ca on ca.cohort_definition_id = cd.cohort_definition_id join concept cs on ca.value_as_concept_id = cs.concept_id left join condition_occurrence co on co.condition_concept_id = cs.concept_id left join person pr on pr.person_id = co.person_id where cd.cohort_definition_name in ('Diabetes - ICD10CM','Hypertension - ICD10CM',' Anti-Hypertensive Pharmacologic Therapy - RxNORM') " rs <- dbSendQuery(mydb,sqlStatementpreds) fetch(rs, n=-1) dbClearResult(rs) #step 3 : create unique dataset with dummy variables and case statements + retain original codes sqlStatementdata <- " select pr.person_id, pr.year_of_birth, pr.month_of_birth, pr.day_of_birth, pr.race_source_value as race, pr.ethnicity_source_value, case when o.person_ID ne . then 1 else 0 END as outcome, o.OUTCOME_NAME, o.ASCVD_code, case when from person pr LEFT JOIN (select co.person_id, cd.cohort_definition_name as OUTCOME_NAME, cs.concept_code as ASCVD_code from cohort_definition cd join cohort_attribute ca on ca.cohort_definition_id = cd.cohort_definition_id join concept cs on ca.value_as_concept_id = cs.concept_id left join condition_occurrence co on co.condition_concept_id = cs.concept_id where cd.cohort_definition_name='ASCVD' and pr.person_ID ne .) out o on o.person_ID=pr.person_ID LEFT JOIN (select from cohort_definition cd join cohort_attribute ca on ca.cohort_definition_id = cd.cohort_definition_id join concept cs on ca.value_as_concept_id = cs.concept_id left join condition_occurrence co on co.condition_concept_id = cs.concept_id where cd.cohort_definition_name in ('Diabetes - ICD10CM','Hypertension - ICD10CM',' Anti-Hypertensive Pharmacologic Therapy - RxNORM')) "

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library(jtools) #install.packages('pscl') library(pscl) #install.packages("broom") library(broom) #install.packages("ggstance") library(ggstance) #install.packages("effects") #library(effects) #install.packages("lattice") #install.packages("caret") library(caret) #install.packages("e1071") library(e1071) str(data) #Creating a single variable that can show the absolute value of the delay, instead of having two variable. data$EffectiveDelay<-abs(data$Departure.Delay.in.Minutes-data$Arrival.Delay.in.Minutes) #Removing the two variables data$Departure.Delay.in.Minutes<-NULL data$Arrival.Delay.in.Minutes<-NULL #Converting the dependent variable into a categorical variable data$Sat<-replicate(length(data$Satisfaction),0) data$Sat[data$Satisfaction>3]<-1 data$Sat<-as.factor(data$Sat) str(data) #Removing the continuous version of the dependent variable data<-data[,-c(1)] #Removing few more variablle, that wouldn't play a significant role in determining the dependent variable data$Airline.Code<-NULL data$Airline.Name<-NULL data$Orgin.City<-NULL data$Origin.State<-NULL data$Destination.City<-NULL data$Destination.State<-NULL data$Day.of.Month<-NULL data$Flight.date<-NULL data$Flight.time.in.minutes<-NULL data$Flight.Distance<-NULL data$EffectiveDelay<-NULL data$Eating.and.Drinking.at.Airport<-NULL data$X..of.Flight.with.other.Airlines<-NULL data$Shopping.Amount.at.Airport <-NULL #SPlitting the data into training and test data. rand<-sample(1:dim(data)[1]) cutpoint2_3<-floor(2*dim(data)[1]/3) cutpoint2_3 traindata<-data[rand[1:cutpoint2_3],] testdata<-data[rand[(cutpoint2_3+1):dim(data)[1]],] #Training the logistic regression model model<-glm(Sat~., family=binomial(link="logit"),data=traindata) summary(model) #K-Fold Cross Validation ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE) mod_fit <- train(Sat ~., data=traindata, method="glm", family="binomial", trControl = ctrl, tuneLength = 5) #Predicting the dependent variable pred = predict(mod_fit, newdata=testdata) #Confusion Matrix confusionMatrix(data=pred, testdata$Sat) "Unlike linear regression with ordinary least squares estimation, there is no R2 statistic which explains the proportion of variance in the dependent variable that is explained by the predictors. However, there are a number of pseudo R2 metrics that could be of value. Most notable is McFadden’s R2, which is defined as 1−[ln(LM)/ln(L0)] where ln(LM) is the log likelihood value for the fitted model and ln(L0) is the log likelihood for the null model with only an intercept as a predictor. The measure ranges from 0 to just under 1, with values closer to zero indicating that the model has no predictive power." pR2(model) #Much more elaborate summary of the model with t-statistic value and other stats summ(model, confint = TRUE, digits = 6) #Plotting the estimate of the coefficients. (Directionality) ploty<-plot_summs(model, scale=TRUE)

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#-----------------a-------------------- x = c(9,32,18,15,26) y = c(10,20,21,16,22) reg = lm(y~x) #Regresion estimada que relaciona y con x summary(reg) 0.11719 <=0.05 #------------------b---------------- x1 = x^2 reg2 = lm(y~x+x1) summary(reg2) b00 = reg2$coefficients[1]#Coeficente b0 b11 = reg2$coefficients[2]#Coeficente b1 b22 = reg2$coefficients[3]#Coeficente b2 yest1 = b00+b11*x+b22*x1 #---------------------c-------------- yest1 = b00+b11*x+b22*x1 yest1 =-8.10139 + 2.41271*(20) - 0.04797*(20)^2 print (yest1)

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Cascade_confidence.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datasets.R \docType{data} \name{Cascade_confidence} \alias{Cascade_confidence} \alias{net_confidence} \alias{net_confidence_.5} \alias{net_confidence_thr} \title{Confidence indices} \format{ A \code{network.confidence} object with four slots : \describe{ \item{network.confidence}{The confidence matrix} \item{name}{Names of the variables (genes)} \item{F}{F array, see Cascade for more details} \item{time_pt}{Repeated measurements} \item{cv.subjects}{Logical. Was crossvalidation carried out subjectwise?} } An object of class \code{network.confidence} of length 1. An object of class \code{network.confidence} of length 1. } \usage{ net_confidence net_confidence_.5 net_confidence_thr } \description{ Result for confidence indices derivation using the Cascade package } \keyword{datasets}

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internalGenerics.Rd

\name{internalGenerics} \alias{internalGenerics} \alias{distribution} \alias{samplesize} \alias{samplesize<-} \title{Internal: Common Generics 'distribution' and 'samplesize', 'samplesize<-'} \description{ In order to be able to use packages \pkg{distrSim} and \pkg{distrMod} resp. \pkg{RobAStBase} independently, it is necessary to import the respective generic from a prior package, i.e., \pkg{distr}. } \usage{ distribution(object) samplesize(object, ...) x <- samplesize(object, value) } \arguments{ \item{object}{ the first argument to dispatch on in the actual methods. } \item{value}{ the value to be assigned. } \item{\dots}{ additional arguments for function \code{samplesize}. } } \author{ Peter Ruckdeschel \email{peter.ruckdeschel@uni-oldenburg.de}} \keyword{internal}

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negbinomial_mixture.r

full_ngb_mixed_model = """ data { int<lower=0> N; #samples real<lower=0> mean1; # mean counts inferred real<lower=0> tau1; int<lower=0> y[N]; } parameters{ real<lower=0> mean2; real<lower=0> tau2; real<lower=0> alpha; real<lower=0> beta; real<lower=0,upper=1> pi; } model { pi ~ Beta(alpha, beta); for( i in 1:N){ target += log_mix(pi,neg_binomial_2_lpmf(y[i] | mean1,tau1),neg_binomial_2_lpmf(y[i] | mean2,tau2)); } } """ library("rstan") # observe startup messages rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores()) data = list(N=N,mean1=mean,tau1=dispersion,y=mixed) #compiles the model stan_model <- stan_model(model_code =full_ngb_mixed_model) #fit via HMC fit <- sampling(stan_model, data=data, par=c("pi","mean2","tau2"), iter = 1000, chains = 3, thin=1) summary(fit) traceplot(fit) library("bayesplot") library("ggplot2") posterior <-as.matrix(extract(fit)$pi) colnames(posterior)<-paste("pi",1:N) plot_title <- ggtitle("Posterior distributions with medians and 80% intervals") mcmc_areas(posterior, point_est="mean",prob = 0.8) + plot_title

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Spherical.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CovarianceFunctions.R \name{Spherical} \alias{Spherical} \title{Spherical Covariance Function} \usage{ Spherical(r, params = list(sigma = 1, theta = 1)) } \arguments{ \item{r}{Vector or matrix of separation distances} \item{params}{A list of parameters with default \code{list(sigma=1,theta=1)}. Parameters may be supplied as vectors of length equal to \code{length(r)}.} } \value{ A vector of matrix the same size as \code{r} containing corresponding values of the Spherical covariance function. } \description{ Functional form of the Spherical covariance function. } \details{ Function that returns the value of the Spherical covariance function. } \author{ Jethro Browell, \email{jethro.browell@glasgow.ac.uk} } \keyword{Covariance} \keyword{Function}

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GraficoTrabalhaAreaEvasao.r

GraficoTrabalhaAreaEvasao <- function(trabalhaAreaEvasaodf) { # ============================================================================== # CONSULTA - QUANTIDADE DE EVADIDOS QUE TRABALHAM NA ÁREA DE EVASÃO # ============================================================================== quantidadeTrabalhaNaArea <- trabalhaAreaEvasaodf %>% select(Trabalha) %>% group_by(Trabalha) %>% summarise(Quantidade = n()) X <- c("TRABALHA NA ÁREA DO CURSO DE EVADIDO?") SIM <- quantidadeTrabalhaNaArea[, 2][2, ] NAO <- quantidadeTrabalhaNaArea[, 2][1, ] quantidadeTrabalhaNaArea <- data.frame(X, SIM, NAO) names(quantidadeTrabalhaNaArea) <- c("X", "SIM", "NAO") # ============================================================================== # GRÁFICO DE BARRAS EMPILHADO # ============================================================================== plot_ly(quantidadeTrabalhaNaArea, x = ~X, source = "sourceTrabalhaNaArea" ) %>% add_trace( type = "bar", y = ~NAO, name = "NAO", hoverinfo = "text", text = ~paste(NAO, "evadido(s)"), textfont = list(color = '#FFFFFF', size = 14), textposition = "inside", marker = list( color = c("rgb(163, 21, 16)"), line = list(color = c("rgb(99, 9, 9)"), width = 2) ), width = .5 ) %>% add_trace( type = "bar", y = ~SIM, name = "SIM", hoverinfo = "text", text = ~paste(SIM, "evadido(s)"), textfont = list(color = '#FFFFFF', size = 14), textposition = "inside", marker = list( color = c("rgb(35, 101, 131)"), line = list(color = c("rgb(13, 60, 81)"), width = 2) ), width = .5 ) %>% layout(yaxis = list(title = "Quantidade de evadidos"), xaxis = list(title = ""), showlegend = T, barmode = "stack") }

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stat_qq.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat-qq.r \name{stat_qq} \alias{geom_qq} \alias{stat_qq} \title{Calculation for quantile-quantile plot.} \usage{ stat_qq(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_qq(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) } \arguments{ \item{mapping}{The aesthetic mapping, usually constructed with \code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set at the layer level if you are overriding the plot defaults.} \item{data}{A layer specific dataset - only needed if you want to override the plot defaults.} \item{geom}{The geometric object to use display the data} \item{position}{The position adjustment to use for overlapping points on this layer} \item{...}{other arguments passed on to \code{\link{layer}}. This can include aesthetics whose values you want to set, not map. See \code{\link{layer}} for more details.} \item{distribution}{Distribution function to use, if x not specified} \item{dparams}{Additional parameters passed on to \code{distribution} function.} \item{na.rm}{If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link{borders}}.} } \description{ Calculation for quantile-quantile plot. } \section{Aesthetics}{ \Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "qq")} } \section{Computed variables}{ \describe{ \item{sample}{sample quantiles} \item{theoretical}{theoretical quantiles} } } \examples{ \donttest{ df <- data.frame(y = rt(200, df = 5)) p <- ggplot(df, aes(sample = y)) p + stat_qq() p + geom_point(stat = "qq") # Use fitdistr from MASS to estimate distribution params params <- as.list(MASS::fitdistr(df$y, "t")$estimate) ggplot(df, aes(sample = y)) + stat_qq(distribution = qt, dparams = params["df"]) # Using to explore the distribution of a variable ggplot(mtcars) + stat_qq(aes(sample = mpg)) ggplot(mtcars) + stat_qq(aes(sample = mpg, colour = factor(cyl))) } }

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as_numeric_dot_default.R

#' Default Numeric Object Coercer #' #' @description by default, as.numeric works fine for coercing objects into numeric objects. #' #' @param x an object #' #' @details just a wrapper for as.numeric really #' #' @return x as a numeric object #' @export #' #' @examples #' as_numeric("4") #' as_numeric(4) #' as_numeric(TRUE) as_numeric.default <- function(x) { print(as.numeric(x)) }

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test-load-data.R

context("Parse CSV data") with_mock_api({ test_that("We can read a CSV file coded with IDs", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-data.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE) expect_type(d,"list") expect_is(d,"data.frame") d_names<-c("dataElement","period","orgUnit","categoryOptionCombo","attributeOptionCombo","value") expect_identical(names(d),d_names) })}) with_mock_api({ test_that("We can read a headerless CSV file coded with IDs", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-data-no-header.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE, csv_header = FALSE) expect_type(d,"list") expect_is(d,"data.frame") d_names<-c("dataElement","period","orgUnit","categoryOptionCombo","attributeOptionCombo","value") expect_identical(names(d),d_names) expect_equal(NROW(d),5) })}) with_mock_api({ test_that("We can error when mechanisms are not coded properly", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_warning(d2Parser(filename=test_config("test-data-bad-mechs.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE, csv_header = FALSE)) })}) context("Parse JSON data") with_mock_api({ test_that("We can read a JSON file coded with IDs", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-json.json"), type="json", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE) expect_type(d,"list") expect_is(d,"data.frame") d_names<-c("dataElement","period","orgUnit","categoryOptionCombo","attributeOptionCombo","value") expect_identical(names(d),d_names) })}) with_mock_api({ test_that("We can error when the JSON attributes are not correct", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_error(d2Parser(filename=test_config("test-json-bad-attributes.json"), type="json", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE),"JSON attributes must be one of the following") })}) context("Parse XML data") with_mock_api({ test_that("We can read an XML file coded with IDs", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-xml.xml"), type="xml", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE) expect_type(d,"list") expect_is(d,"data.frame") d_names<-c("dataElement","period","orgUnit","categoryOptionCombo","attributeOptionCombo","value") expect_identical(names(d),d_names) })}) with_mock_api({ test_that("We can error when the XML attributes are not correct", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_error(d2Parser(filename=test_config("test-xml-bad-attributes.xml"), type="xml", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE),"XML attributes must be one of the following") })}) context("Can error on a wrong file type") with_mock_api({ test_that("We can create an error on a bad file type", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_error(d2Parser(filename=test_config("test-xml.xml"), type="foo", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE)) })}) context("Can error on a wrong period identifier") with_mock_api({ test_that("We can create an error on a file with a bad period", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-data-bad-periods.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE) expect_error(checkPeriodIdentifiers(d)) })}) context("Can return bad mechanism/period association") with_mock_api({ test_that("We can create an warning for an invalid/mechanism period association", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-data-bad-periods-mechanisms.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE) expect_warning(bad_mechs<-checkMechanismValidity(d,organisationUnit="KKFzPM8LoXs", return_violations=TRUE),"Invalid mechanisms found!") expect_type(bad_mechs,"list") expect_is(bad_mechs,"data.frame") bad_mechs_names<-c("attributeOptionCombo","period","startDate","endDate","periodType","code","startDate_mech","endDate_mech","is_valid") expect_setequal(names(bad_mechs),bad_mechs_names) })}) context("Can warn on bad mechanism/period associations") with_mock_api({ test_that("We can create an warning for an invalid/mechanism period association", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") d<-d2Parser(filename=test_config("test-data-bad-periods-mechanisms.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE) expect_warning(bad_mechs<-checkMechanismValidity(d,organisationUnit="KKFzPM8LoXs", return_violations=FALSE),"Invalid mechanisms found!") expect_null(bad_mechs) })}) context("Can error on an invalid orgunit UID") with_mock_api({ test_that("We can create an error for an invalid organisation unit identifier", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_warning(foo<-d2Parser(filename=test_config("test-data-bad-ou-uid.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE), "The following org unit identifiers could not be found:SiuNE0ywCW4") expect_false(foo$is_valid) expect_type(foo,"list") })}) context("Can error on an invalid data element UID") with_mock_api({ test_that("We can create an error for an invalid data element identifier", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_warning(foo<-d2Parser(filename=test_config("test-data-bad-de-uid.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE), "The following data element identifiers could not be found:SiuNE0ywCW4") expect_false(foo$is_valid) expect_type(foo,"list") })}) context("Can error on an invalid attribute option combo UID") with_mock_api({ test_that("We can create an error for an invalid attribute option combo identifier", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_warning(foo<-d2Parser(filename=test_config("test-data-bad-acoc-uid.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE), "The following attribute option combo identifiers could not be found:SiuNE0ywCW4") expect_false(foo$is_valid) expect_type(foo,"list") })}) context("Can warn on a missing values") with_mock_api({ test_that("Can warn on a missing data value", { config <- LoadConfigFile(test_config("test-config.json")) options("maxCacheAge"=NULL) expect_type(config,"list") expect_warning(foo<-d2Parser(filename=test_config("test-data-missing-value.csv"), type="csv", organisationUnit = "KKFzPM8LoXs", dataElementIdScheme = "id", orgUnitIdScheme = "id", idScheme = "id", invalidData = FALSE), "1 rows are incomplete. Please check your file to ensure its correct.") })})

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track_circos.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/draw_circos_plot.R \name{track_circos} \alias{track_circos} \title{Drawing circos track} \usage{ track_circos(data, track_label, bg.col = "white", ylim = c(0, 1.025 * max(data[[3]]) + 0.01), top.track = FALSE, point.size = 0.1, color.point = "black", sector.names = NULL, sector.titles.expand = 1.3, sectors = NULL) } \arguments{ \item{data}{Generated by load_data_files function} \item{track_label}{Label for track} \item{bg.col}{background color(default: "white")} \item{top.track}{to check whether is the top track (defaul: FALSE)} \item{point.size}{point size (default: 0.1)} \item{color.point}{point color (default: "black")} \item{sector.names}{sector names (default: NULL)} } \description{ Drawing circos track }

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Flight_duration.R

#' Calculate flight duration. #' #' @param arrival_time Arrival time at the destination. #' @param arrival_tz Timezone of the destination. #' @param departure_time Departure time at the origin. #' @param departure_tz Timezone of the origin. #' @return Time elapsed between the departure time and arrival time in the timezone of the destination. #' @examples #' flight_duration("2017-08-20 10:30", "Asia/Hong_Kong", "2017-08-19 09:00", "Africa/Johannesburg") #' flight_duration("2017-08-20 10:30", "Asia/Hong_Kong") #' @export options(warn = -1) flight_duration <- function(arrival_time, arrival_tz, departure_time = Sys.time(), departure_tz = Sys.timezone()) { DepTime <- format(as.POSIXct(departure_time, tz = departure_tz), tz = arrival_tz, usetz = TRUE) ArrTime <- format(as.POSIXct(arrival_time, tz = arrival_tz), tz = arrival_tz, usetz = TRUE) as.POSIXct(ArrTime) - as.POSIXct(DepTime) }

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require(rtracklayer) require(xlsx) source("comp.fun.R") source("comp.plot.fun.R") dirw = file.path(Sys.getenv("misc3"), 'comp.plot.gene') fl = file.path(dirw, 'loci.xlsx') tl = read.xlsx(fl, sheetIndex = 1, header = T, stringsAsFactors = F) fid = file.path(Sys.getenv("misc3"), "comp.ortho.hm", "01.ids.tbl") tid = read.table(fid, header = T, sep = "\t", as.is = T) fds = file.path(Sys.getenv("misc3"), "comp.ortho.hm", "12.score.tbl") tds = read.table(fds, header = T, sep = "\t", as.is = T) fg = file.path(Sys.getenv("genome"), "HM101", "51.gtb") tg = read.table(fg, header = T, sep = "\t", as.is = T) fr = file.path(Sys.getenv("misc3"), "comp.og/05.clu/32.tbl") tr = read.table(fr, header = T, sep = "\t", as.is = T) orgs = sapply(strsplit(tr$id, split="-"), "[", 1) gids = sapply(strsplit(tr$id, split="-"), "[", 2) tr = cbind(tr, org = orgs, gid = gids) ##### source("comp.plot.fun.R") i = 1 chr = tl$chr[i]; beg = tl$beg[i]; end = tl$end[i] gidxs = which(tg$chr == chr & tg$beg >= beg & tg$end <= end) fn = sprintf("%s/fig%03d.pdf", dirw, i) CairoPDF(file = fn, width = 7, height = res$ht/72, bg = 'transparent') grid.newpage() grid.draw(res$grobs) dev.off()

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/hate_crime_EDA.R

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hate_crime_EDA.R

#Hate Crime EDA library(tidyverse) library(urbnmapr) library(fivethirtyeight) hc <- fivethirtyeight::hate_crimes #Joining fivethirtyeight data with mapping data state <- urbnmapr::states names(state)[names(state) == "state_name"] <- "state" #Changing state_name to state to match hc data hc_state <- left_join(state, hc, by = "state") #Midwest states midwest <- c("Illinois", "Indiana", "Iowa", "Kansas", "Michigan", "Minnesota", "Missouri", "Nebraska", "North Dakota", "Ohio", "South Dakota", "Wisconsin") hc_viz <- hc_state %>% filter(state == midwest) %>% #Filter down to Midwest States ggplot(aes(long, lat, group = group, fill = avg_hatecrimes_per_100k_fbi)) + scale_fill_continuous(high = "#ff0000", low = "#ffdab9")+ geom_polygon(color = "#ffffff") + coord_map(projection = "albers", lat0 = 39, lat1 = 45) + theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), legend.position = "bottom", legend.background = element_rect(size=0.5, linetype="solid", colour ="black"), plot.title = element_text(face = "bold", hjust = .5), plot.caption = element_text(face = "italic", hjust = 1.45)) + labs(fill = "Average Hate Crimes per 100,000 people", caption = "Based on data from the FBI aggregated by fivethirtyeight") + ggtitle("Average Hate Crimes in the Midwest 2010-2015") hc_viz

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sr-ch12.R

## sr-ch12.R ## Chapter 12 - Multilevel Models ## Remember features of each clusteer in the data as they learn about all ## the clusters. ## (1) Improved estimates for repeat sampling - when more than one observation ## arises from the same indiv, loc, or time, then traditional single-level ## models either maximally underfit or underfit the data. ## (2) Improved estimates for imbalance in sampling - when some indiv, loc, or ## time are sampled more than others, multilevel models cope with differing ## uncertainty across these clusters. This prevents over-sampled clusters from ## unfairly dominating inference. ## (3) Estimates of variation. If RQ include variation among indiv or other grps ## in data , then multilevel models help bc the model variation explicitly. ## (4) Avoid averaging, retain variation - pre-averaging data to construct ## variables can be dangerous bc it removes variation. ## 12.1 Multilevel tadpoles library(rethinking) data(reedfrogs) d <- reedfrogs str(d) head(d) ## VARYING INTERCEPTS MODEL - multilevel model in which we simultaneously estimate both an intercept for ## each tank and the variation among tanks ## learn the prior that is common to all the modeled intercepts ## make the tank cluster variable d$tank <- 1:nrow(d) ## fit m12.1 <- map( alist( surv ~ dbinom(density, p), logit(p) <- a_tank[tank], a_tank[tank] ~ dnorm(0,5) ), data = d ) precis(m12.1, depth = 2) ## HYPERPARAMETERS - parameters for parameters m12.2 <- map2stan( alist( surv ~ dbinom(density, p), logit(p) <- a_tank[tank], a_tank[tank] ~ dnorm(a, sigma), a ~ dnorm(0,1), sigma ~ dcauchy(0,1) ), data = d, iter = 4000, chains = 4 ) compare(m12.1, m12.2) ## POOLING - each tank provides information that can be used to improve the estimates ## for all of the other tanks post <- extract.samples(m12.2) plot(NULL, xlim=c(-3,4), ylim=c(0,0.35), xlab="log-odds survive", ylab="density") for (i in 1:100) { curve(dnorm(x, post$a[i], post$sigma[i]), add = T, col=col.alpha("black", 0.2)) } # sample imaginary tank from post dist sim_tanks <- rnorm(8000, post$a, post$sigma) dens(logistic(sim_tanks), xlab = "probability survive") ## 12.2 Varying effects and the underfitting/overfitting trade-off ## Varying intercepts are just regularized estimates but adaptively regularized by ## estimating how diverse the clusters are while estimating the features of each ## cluster ## (1) complete pooling - assume that the population of the ponds is invariant, ## the same as estimating a common intercept for all ponds. ## (2) no pooling - assume each pond tells us nothing about any other pond ## (3) partial pooling - using an adaptive regularizing prior ## 12.2.1 The model ## multilevel binomial model with ponds instead of tanks ## 12.2.2 Assign values to the parameters a <- 1.4 sigma <- 1.5 nponds <- 60 ni <- as.integer(rep(c(5,10,25,35),each=15)) a_pond <- rnorm(nponds, mean=a, sd=sigma) dsim <- data.frame(pond=1:nponds, ni=ni, true_a=a_pond) head(dsim) tail(dsim) ## simulate survival process dsim$si <- rbinom(nponds,prob=logistic(dsim$true_a), size = dsim$ni) ## 12.2.4 Compute the no-pooling estimates dsim$p_nopool <- dsim$si/dsim$ni ## 12.2.5 Compute the partial-pooling estimates m12.3 <- map2stan( alist( si ~ dbinom(ni, p), logit(p) <- a_pond[pond], a_pond[pond] ~ dnorm(a, sigma), a ~ dnorm(0,1), sigma ~ dcauchy(0,1) ), data = dsim, iter=1e4, warmup = 1000 ) precis(m12.3, depth = 2) ## 60 estimated intercept parameters estimated.a_pond <- as.numeric(coef(m12.3)[1:60]) dsim$p_partpool <- logistic(estimated.a_pond) dsim$p_true <- logistic(dsim$true_a) nopool_error <- abs(dsim$p_nopool - dsim$p_true) partpool_error <- abs(dsim$p_partpool - dsim$p_true) plot(1:60, nopool_error, xlab="pond", ylab="abs error", col=rangi2, pch=16) points(1:60, partpool_error) ## 12.3 More than one type of cluster library(rethinking) data(chimpanzees) d <- chimpanzees str(d) head(d) d$recipient <- NULL #get rid of NAs m12.4 <- map2stan( alist( pulled_left ~ dbinom(1,p), logit(p) <- a + a_actor[actor] + (bp + bpC*condition)*prosoc_left, a_actor[actor] ~ dnorm(0,sigma_actor), a ~ dnorm(0,10), bp ~ dnorm(0,10), bpC ~ dnorm(0,10), sigma_actor ~ dcauchy(0,1) ), data = d, chains = 2, cores = 3, iter = 5000, warmup = 1000 ) plot(m12.4) precis(m12.4) post <- extract.samples(m12.4) total_a_actor <- sapply(1:7, function(actor) post$a + post$a_actor[,actor]) round(apply(total_a_actor,2,mean),2) ## 12.3.2 Two types of cluster (add block) ## fit model that uses both actor and block d$block_id < d$block # name `block` is reserved by stan m12.5 <- map2stan( alist( pulled_left ~ dbinom(1, p), logit(p) <- a + a_actor[actor] + a_block[block_id] + (bp + bpc*condition)*prosoc_left, a_actor[actor] ~ dnorm(0, sigma_actor), a_block[block_id] ~ dnorm(0, sigma_block), c(a,bp, bpc) ~ dnorm(0,10), sigma_actor ~ dcauchy(0,1), sigma_block ~ dcauchy(0,1) ), data = d, warmup = 1000, iter = 6000, chains = 1, cores = 1 ) ## 12.4 Multilevel posterior predictions ## MODEL CHECKING - compare the sample to the posterior predictions of the fit model ## producing implied predictions from a fit model is helpful for understanding ## what the model means. INFORMATION CRITERIA (like DIC & WAIC) provide simple estimates ## of out-of-sample model accuracy, like the KL divergence. IC provide rough measure of ## a model's flexibility and therefore overfitting risk. ## in chimpanzees there are 7 unique actors - these are clusters. precis(m12.4, depth=2) ## the whole pt of partial pooling is to shrink estimates towards the grand mean chimp <- 2 d.pred <- list( prosoc_left = c(0,1,0,1), condition = c(0,0,1,1), actor = rep(chimp, 4) ) d.pred link.m12.4 <- link(m12.4, data = d.pred) pred.p <- apply(link.m12.4, 2, mean) pred.p.PI <- apply(link.m12.4, 2, PI) par(mfrow=c(1,1)) plot(0,0,type='n', xlab="prosoc_left/condition", ylab="proportion pulled left", ylim=c(0,1),xaxt="n", xlim=c(1,4)) axis(1,at=1:4, labels = c("0/0","1/0", "0/1","1/1")) p <- by(d$pulled_left, list(d$prosoc_left, d$condition, d$actor), mean) for (chimp in 1:7) { lines(1:4, as.vector(p[,,chimp]), col=rangi2, lwd = 1.5) } lines(1:4, pred.p) shade(pred.p.PI, 1:4) post <- extract.samples(m12.4) str(post) dens(post$a_actor[,5]) p.link <- function(prosoc_left, condition, actor) { logodds <- with(post, a + a_actor[,actor] + (bp +bpC * condition)*prosoc_left ) return(logistic(logodds)) } ## compute predictions prosoc_left <- c(0,1,0,1) condition <- c(0,0,1,1) pred.raw <- sapply(1:4, function(i) p.link(prosoc_left[i], condition[i], 2)) pred.p <- apply(pred.raw, 2, mean) pred.p.PI <- apply(pred.raw, 2, PI) ## 12.4.2 Posterior prediction for new clusters ## often the particular clusters in the sample are not of any enduring interest - in the ## chimpanzee data, for examoke, we'd like to make inferences about the population, so ## the actor intercepts are not of interest. ## imagine leaving out one of the clusters when you fit the data. Use the a and sigma_actor ## parameters because they descrive the population of actors. ## how to construct posterior predictions for a now, previously unobserved average actor. ## by average, I mean a chimp with an intercept exactly at the mean a. library(rethinking) data(chimpanzees) d <- chimpanzees str(d) head(d) d$recipient <- NULL #get rid of NAs m12.4 <- map2stan( alist( pulled_left ~ dbinom(1,p), logit(p) <- a + a_actor[actor] + (bp + bpC*condition)*prosoc_left, a_actor[actor] ~ dnorm(0,sigma_actor), a ~ dnorm(0,10), bp ~ dnorm(0,10), bpC ~ dnorm(0,10), sigma_actor ~ dcauchy(0,1) ), data = d, chains = 2, cores = 3, iter = 5000, warmup = 1000 ) plot(m12.4) precis(m12.4) d.pred <- list( prosoc_left = c(0,1,0,1), condition = c(0,0,1,1), actor = rep(2,4) ) ## replace varying interceot samples w zeros ## 1000 samples by 7 actors a_actor_zeros <- matrix(0,1000,7) ## fire up link link.m12.4 <- link(m12.4, n = 1000, data=d.pred, replace = list(a_actor=a_actor_zeros)) ## summarize & plot pred.p.mean <- apply(link.m12.4, 2, mean) pred.p.PI <- apply(link.m12.4, 2, PI, prob=0.8) par(mfrow=c(1,1)) plot(0,0,type="n",xlab="prosoc_left/condition", ylab="proportion pulled left", ylim=c(0,1),xaxt="n",xlim=c(1,4)) axis(1, at = 1:4, labels=c("0/0","1/0", "0/1", "1/1")) lines(1:4, pred.p.mean) shade(pred.p.PI, 1:4) ## to show variation among actors use sigma_alpha in calculation ## replace varying intercept samples with simulations post <- extract.samples(m12.4) a_actor_sims <- rnorm(7000, 0, post$sigma_actor) a_actor_sims <- matrix(a_actor_sims, 1000, 7) ## pass simulated intercepts into link link.m12.4 <- link(m12.4, n = 1000, data = d.pred, replace = list(a_actor=a_actor_sims)) ## summarize & plot pred.p.mean <- apply(link.m12.4, 2, mean) pred.p.PI <- apply(link.m12.4, 2, PI, prob=0.8) par(mfrow=c(1,1)) plot(0,0,type="n",xlab="prosoc_left/condition", ylab="proportion pulled left", ylim=c(0,1),xaxt="n",xlim=c(1,4)) axis(1, at = 1:4, labels=c("0/0","1/0", "0/1", "1/1")) lines(1:4, pred.p.mean) shade(pred.p.PI, 1:4) ## simulate a new actor from the estimated population of actors and then ## computes probabilities of pulling the left lever for each of the 4 treatments post <- extract.samples(m12.4) sim.actor <- function(i) { sim_a_actor <- rnorm(1,0,post$sigma_actor[i]) P <- c(0,1,0,1) C <- c(0,0,1,1) p <- logistic( post$a[i] + sim_a_actor + (post$bp[i] + post$bpC[i]*C)*P ) return(p) } plot(0,0,type="n",xlab="prosoc_left/condition", ylab="proportion pulled left", ylim=c(0,1),xaxt="n",xlim=c(1,4)) axis(1, at = 1:4, labels=c("0/0","1/0", "0/1", "1/1")) # plot 50 simulated actors for (i in 1:50) lines(1:4,sim.actor(i), col=col.alpha("black", 0.5)) ## 12.4.3 Focus and multilevel prediction ## multilevel models contain parameters with different FOCUS - i.e. which ## level of the model the parameter makes direct predictions for. ## (1) when retrodicting the sample, the parameters that describe the population ## of clusters do not influence prediction directly. These population parameters ## are called HYPERPARAMETERS, as they are parameters for parameters and they have ## their effects during estimation by shrinking the varying effect parameters ## towards a common mean. ## (2) the same is true when forecasting a new observation for a cluster that was ## present in the sample. ## (3) when we wish to forecast for some new (unseen) cluster, we need the ## hyperparameters as they tell us how to forecast a new cluster by generating ## a distribution of new per-cluster intercepts. ## over-dispersed Poisson model library(rethinking) data(Kline) d <- Kline str(d) d$logpop <- log(d$population) d$society <- 1:10 m12.6 <- map2stan( alist( total_tools ~ dpois(mu), log(mu) <- a + a_society[society] + bp*logpop, a ~ dnorm(0,10), bp ~ dnorm(0,1), a_society[society] ~ dnorm(0,sigma_society), sigma_society ~ dcauchy(0,1) ), data = d, iter=4000, chains=3 ) ## to see the general trend that the model expects we need to simulate ## counterfactual societies using hyperparameters alpha and sigma_society post <- extract.samples(m12.6) d.pred <- list( logpop = seq(from=6, to=14, length.out=30), society = rep(1,30) ) a_society_sims <- rnorm(20000,0,post$sigma_society) a_society_sims <- matrix(a_society_sims, 2000, 10) link.m12.6 <- link(m12.6, n=2000, data = d.pred, replace = list(a_society=a_society_sims)) plot(d$logpop, d$total_tools, col=rangi2, pch=16, xlab="log population", ylab="total tools") mu.median <- apply(link.m12.6, 2, median) lines(d.pred$logpop, mu.median) mu.PI <- apply(link.m12.6, 2, PI, prob=.97) shade(mu.PI, d.pred$logpop) mu.PI <- apply(link.m12.6, 2, PI, prob=.89) shade(mu.PI, d.pred$logpop) mu.PI <- apply(link.m12.6, 2, PI, prob=.67) shade(mu.PI, d.pred$logpop)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deflated.Sharpe.R \name{deflatedSharpe} \alias{deflatedSharpe} \alias{SharpeRatio.deflated} \alias{.deflatedSharpe} \title{Calculate a Deflated Sharpe Ratio using number of trials and portfolio moments} \usage{ deflatedSharpe( portfolios, ..., strategy = NULL, trials = NULL, audit = NULL, env = .GlobalEnv ) .deflatedSharpe( sharpe, nTrials, varTrials, skew, kurt, numPeriods, periodsInYear = 252 ) } \arguments{ \item{portfolios}{string name of portfolio, or optionally a vector of portfolios, see DETAILS} \item{...}{any other passtrhrough parameters} \item{strategy}{optional strategy specification that would contain more information on the process, default NULL} \item{trials}{optional number of trials,default NULL} \item{audit}{optional audit environment containing the results of parameter optimization or walk forward, default NULL} \item{env}{optional environment to find market data in, if required.} \item{sharpe}{candidate (annualized) Sharpe Ratio} \item{nTrials}{numeric number or trials} \item{varTrials}{variance of Sharpe ratios of the trials} \item{skew}{skewness of the candidate} \item{kurt}{non-excess kurtosis} \item{numPeriods}{total periods in the backtest} \item{periodsInYear}{number of periods in a year, default 252 (daily)} } \value{ a \code{data.frame} containing: \itemize{ \item{original observed Sharpe ratio} \item{deflated Sharpe ratio} \item{p-value of the deflated Sharpe ratio} \item{number of trials used for adjustment} } this object may change in the future, and may be classed so that we can include more information } \description{ Per Bailey and Lopex de Prado (2014), construct a Deflated Sharpe Ratio and associated p-value based on an observed Sharpe ratio and information drawn from a series of trials (e.g. parameter optimization or other strategies tried before the candidate strategy) } \references{ Bailey, David H, and Marcos Lopez de Prado. 2014. "The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality." Journal of Portfolio Management 40 (5): 94-107. http://www.davidhbailey.com/dhbpapers/deflated-sharpe.pdf https://quantstrattrader.wordpress.com/2015/09/24/ } \seealso{ \code{\link{SharpeRatio.haircut}} } \author{ Brian G. Peterson Ilya Kipnis, Brian G. Peterson }

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#' Plot comparing the test study to the GWAS catalog #' #' Make a plot comparing signed Z scores, or effect allele frequency, between the test dataset and the GWAS catalog, in order to identify effect allele meta data errors #' #' @param dat the test dataset of interest #' @param beta name of the column containing the SNP effect size #' @param se name of the column containing the standard error for the SNP effect size. #' @param plot_type compare Z scores or effect allele frequency? For comparison of Z scores set plot_type to "plot_zscores". For comparison of effect allele frequency set to "plot_eaf". Default is set to "plot_zscores" #' @param trait the trait of interest #' @param efo_id ID for trait of interest in the experimental factor ontology #' @param efo trait of interest in the experimental factor ontology #' @param gwas_catalog_ancestral_group restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian") #' @param force_all_trait_study_hits force the plot to include GWAS hits from the outcome study if they are not in the GWAS catalog? This should be set to TRUE only if dat is restricted to GWAS hits for the trait of interest. This is useful for visualising whether the outcome/trait study has an unusually larger number of GWAS hits, which could, in turn, indicate that the summary statistics have not been adequately cleaned. #' @param exclude_palindromic_snps should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands. #' @param distance_threshold distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog. #' @param legend include legend in plot. Default TRUE #' @param Title plot title #' @param Title_size_subplot size of title #' @param Ylab label for Y axis #' @param Xlab label for X axis #' @param Title_xaxis_size size of x axis title #' @param return_dat if TRUE, the dataset used to generate the plot is returned to the user and no plot is made. #' #' @return plot #' @export make_plot_gwas_catalog<-function(dat=NULL,plot_type="plot_zscores",efo_id=NULL,efo=NULL,trait=NULL,gwas_catalog_ancestral_group=c("European","East Asian"),legend=TRUE,Title="Comparison of Z scores between test dataset & GWAS catalog",Title_size_subplot=10,Ylab="Z score in test dataset",Xlab="Z score in GWAS catalog",Title_xaxis_size=10,force_all_trait_study_hits=FALSE,exclude_palindromic_snps=TRUE,beta="lnor",se="lnor_se",distance_threshold=25000,return_dat=TRUE){ Dat.m<-compare_effect_to_gwascatalog(dat=dat,beta=beta,se=se,efo_id=efo_id,efo=efo,trait=trait,force_all_trait_study_hits=force_all_trait_study_hits,exclude_palindromic_snps=exclude_palindromic_snps,distance_threshold=distance_threshold) Dat.m[Dat.m$Z_scores=="high conflict",] Names<-grep("beta",names(Dat.m)) Names2<-grep("effect",names(Dat.m)) Names3<-grep("eaf",names(Dat.m)) Names4<-grep("ances",names(Dat.m)) Dat.m$rsid[Dat.m$Z_scores=="high conflict"] Dat.m[Dat.m$Z_scores=="high conflict",c(Names,Names2,Names3,Names4)] # head(Dat.m) # Dat.m[Dat.m$Z_scores=="high conflict",c("z.y","z.x")] Dat.m$Z_scores[Dat.m$Z_scores=="high conflict"]<-"red" Dat.m$Z_scores[Dat.m$Z_scores=="moderate conflict"]<-"blue" Dat.m$Z_scores[Dat.m$Z_scores=="no conflict"]<-"black" labels_colour<-unique(Dat.m$Z_scores) values_colour<-unique(Dat.m$Z_scores) Dat.m$plot_x<-Dat.m$z.x Dat.m$plot_y<-Dat.m$z.y Dat.m$colour<-Dat.m$Z_scores Name<-"Effect size conflict" if(plot_type=="plot_eaf") { Dat.m<-Dat.m[!is.na(Dat.m$eaf.x),] Dat.m$EAF[Dat.m$EAF=="high conflict"]<-"red" Dat.m$EAF[Dat.m$EAF=="moderate conflict"]<-"blue" Dat.m$EAF[Dat.m$EAF=="no conflict"]<-"black" labels_colour<-unique(Dat.m$EAF) values_colour<-unique(Dat.m$EAF) Dat.m$plot_x<-Dat.m$eaf.x Dat.m$plot_y<-Dat.m$eaf.y Dat.m$colour<-Dat.m$EAF Name<-"EAF conflict" Ylab="EAF in outcome study" Xlab="EAF in GWAS catalog" Title="Comparison of EAF between test dataset and GWAS catalog" } if(return_dat) return(Dat.m) labels_colour[labels_colour == "red"]<-"high" if(force_all_trait_study_hits & any(Dat.m$z.x ==0)) { labels_colour[labels_colour == "high"]<-"high or not\npresent in GWAS catalog" } labels_colour[labels_colour == "blue"]<-"moderate" labels_colour[labels_colour == "black"]<-"none" Pos<-order(values_colour) values_colour<-values_colour[Pos] labels_colour<-labels_colour[Pos] ancestry1<-Dat.m$ancestral_group labels_shape<-unique(ancestry1)[order(unique(ancestry1))] values_shape<-labels_shape values_shape[values_shape == "European"]<-15 values_shape[values_shape == "East Asian"]<-16 values_shape<-as.numeric(values_shape) # values_shape<-c(16,15,17,18) if(is.null(Title)){ Title<-paste0(unique(dat$study)," | " ,unique(dat$ID) , " | EFO: ", efo) } Subtitle<-paste0(Dat.m$outcome," | ",Dat.m$population) if(legend){ Plot<-ggplot2::ggplot(Dat.m) + ggplot2::geom_point(ggplot2::aes(x=plot_x, y=plot_y,colour=colour,shape=ancestry1)) +ggplot2::ggtitle(Title) +ggplot2::labs(y= Ylab, x =Xlab,subtitle=Subtitle) + ggplot2::theme(plot.title = ggplot2::element_text(size = Title_size_subplot, face = "plain"), )+ ggplot2::theme(axis.title=ggplot2::element_text(size=Title_xaxis_size),plot.subtitle = ggplot2::element_text(size = 8))+ ggplot2::scale_shape_manual(name = "GWAS catalog ancestry", labels = labels_shape, # labels = unique(ancestry1)[order(unique(ancestry1))], # labels = c("European","East Asian"), values = values_shape) + # values = 1:length(Shape2)) + ggplot2::scale_colour_manual(name=Name, labels=labels_colour, values=values_colour)+ ggplot2::theme(legend.title=ggplot2::element_text(size=8))+ ggplot2::theme(legend.text=ggplot2::element_text(size=8)) } if(!legend){ Plot<-ggplot2::ggplot(Dat.m) + ggplot2::geom_point(ggplot2::aes(x=plot_x, y=plot_y,colour=colour,shape=ancestry1)) +ggplot2::ggtitle(Title) +ggplot2::labs(y= Ylab, x =Xlab,subtitle=Subtitle) + ggplot2::theme(plot.title = ggplot2::element_text(size = Title_size_subplot, face = "plain"))+ ggplot2::theme(axis.title=ggplot2::element_text(size=Title_xaxis_size))+ ggplot2::scale_shape_manual(name = "GWAS catalog ancestry", labels = labels_shape, values = values_shape) + ggplot2::scale_colour_manual(name=Name, labels=labels_colour, values=values_colour)+ ggplot2::theme(legend.title=ggplot2::element_text(size=8), legend.text=ggplot2::element_text(size=8),plot.subtitle = ggplot2::element_text(size = 8), legend.position = "none") } # ggplot2::scale_colour_manual(name="Z score conflict", # labels=unique(Z_scores)[order(unique(Z_scores))] , # values=unique(Z_scores)[order(unique(Z_scores))]) # ggplot2::scale_colour_manual(name="Z score conflict", # labels=c("none", "moderate","high"), # values=c("black","blue", "red")) return(Plot) } #' Compare the genetic effect sizes in the test dataset to the GWAS catalog #' #' Compare the direction of effects and effect allele frequency between the test dataset and the GWAS catalog, in order to identify effect allele meta data errors #' #' @param dat the test dataset of interest #' @param beta name of the column containing the SNP effect size #' @param se name of the column containing the standard error for the SNP effect size. #' @param trait the trait of interest #' @param efo_id ID for trait of interest in the experimental factor ontology #' @param efo trait of interest in the experimental factor ontology #' @param gwas_catalog_ancestral_group restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian") #' @param force_all_trait_study_hits force the comparison to include GWAS hits from the test dataset if they are not in the GWAS catalog? This should be set to TRUE only if dat is restricted to GWAS hits for the trait of interest. This is useful for visualising whether the test trait study has an unusually larger number of GWAS hits, which could, in turn, indicate analytical issues with the summary statistics #' @param exclude_palindromic_snps should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands. #' @param distance_threshold distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog. #' #' @return dataframe #' @export compare_effect_to_gwascatalog<-function(dat=NULL,efo=NULL,efo_id=NULL,trait=NULL,beta=NULL,se=NULL,gwas_catalog_ancestral_group=c("European","East Asian"),exclude_palindromic_snps=TRUE,force_all_trait_study_hits=FALSE,distance_threshold=distance_threshold) { # exclude the MAF 1k ref set. Causes problems if you force inclusion of SNPs missing from the GWAS catalog utils::data("refdat_1000G_superpops",envir =environment()) snps_exclude<-unique(refdat_1000G_superpops$SNP) dat<-dat[!dat$rsid %in% snps_exclude,] if(beta=="lnor") { if(!"lnor" %in% names(dat)) stop("name of beta column set to lnor but there is no column with that name") } if(!beta %in% names(dat)) stop(paste0("beta column not found. Check you correctly specified the name of the beta column")) if(!se %in% names(dat)) stop(paste0("se column not found. Check you correctly specified the name of the se column")) if(is.null(efo) & is.null(efo_id) & is.null(trait)) stop("you must specify either efo, efo_id or trait") gwas_catalog<-gwas_catalog_hits2(efo=efo,efo_id=efo_id,trait=trait) message_trait<-paste(c(efo,efo_id,trait),collapse="/") Dat.m<-merge(gwas_catalog,dat,by="rsid") if(all(is.na(Dat.m$effect_allele.x))) stop(paste0("associations for ",message_trait," were found but all effect alleles are missing in the GWAS catalog. Therefore no comparison of effect size direction can be made")) Dat.m<-Dat.m[!is.na(Dat.m$effect_allele.x),] Dat.m<-Dat.m[nchar(Dat.m$effect_allele.y)==1,] Dat.m<-Dat.m[nchar(Dat.m$other_allele)==1,] Alleles<-paste0(Dat.m$effect_allele.y,Dat.m$other_allele) if(exclude_palindromic_snps) { Dat.m<-Dat.m[!Alleles %in% c("AT","TA","GC","CG"),] } if(!is.null(gwas_catalog_ancestral_group)) { # c("European","East Asian") Dat.m<-Dat.m[Dat.m$ancestral_group %in% gwas_catalog_ancestral_group,] } # Dat.m1<-Dat.m # Dat.m<-Dat.m1 Dat.m<-harmonise_effect_allele(dat=Dat.m,beta=beta) Pos<-Dat.m$effect_allele.x!=Dat.m$effect_allele.y if(any(Pos)) { Dat.m1<-Dat.m[Pos,] Dat.m2<-Dat.m[!Pos,] Dat.m1<-flip_strand(dat=Dat.m1,allele1_col="effect_allele.x") # Dat.m1$effect_allele.x # Dat.m1$effect_allele.y # Dat.m1[,c("effect_allele.x","effect_allele.y","other_allele","rsid")] Dat.m<-rbind(Dat.m1,Dat.m2) } Pos<-Dat.m$effect_allele.x!=Dat.m$effect_allele.y if(any(Pos)) { Dat.m<-harmonise_effect_allele(dat=Dat.m,beta=beta) } Pos<-Dat.m$effect_allele.x!=Dat.m$effect_allele.y if(any(Pos)) { stop("effect alleles not fully harmonised") # Dat.m[Pos,c("rsid","Effect.Allele.x","Effect.Allele.y","Other.Allele")] } Dat.m$z.y<-Dat.m[,beta]/Dat.m[,se] # Dat.m$z.x<-Dat.m$beta_gc/Dat.m$se_gc # Dat.m$z.y<-Dat.m$lnor.y/Dat.m$se.y # Dat.m$z.x<-Dat.m$lnor.x/Dat.m$se.x # head(Dat.m[,c("p.x","z.x","p.y","z.y")]) # max(Dat.m$p.x) # dim(Dat.m) # Ylab<-"" # Xlab<-"" if("pmid" %in% names(dat)) { gwas_studies<-gwasrapidd::get_studies(study_id=unique(Dat.m$study_id )) Publications<-gwas_studies@publications Publications<-Publications[!duplicated(Publications$study_id),] Dat.m<-merge(Dat.m,Publications,by="study_id") } #identifty eaf conflicts # ancestry2<-Dat.m$ancestral_group Dat.m$EAF<-"no conflict" Dat.m$EAF[is.na(Dat.m$eaf.x)]<-NA # EAF<-rep("black",nrow(Dat.m)) Pos1<-which(Dat.m$eaf.x<0.5 & Dat.m$eaf.y>0.5 | Dat.m$eaf.x>0.5 & Dat.m$eaf.y<0.5) Dat.m$EAF[Pos1]<-"moderate conflict" Pos2<-which(Dat.m$eaf.x<0.40 & Dat.m$eaf.y>0.60 | Dat.m$eaf.x>0.60 & Dat.m$eaf.y<0.40) Dat.m$EAF[Pos2]<-"high conflict" Pos3<-which(Dat.m$pmid==Dat.m$pubmed_id) Pos4<-Pos1[Pos1 %in% Pos3] Dat.m$EAF[Pos4]<-"high conflict" #if there is a moderate eaf conflict (eaf close to 0.5) but both datasets are from the same study, then the conflict is upgraded to high # if(plot_type=="plot_zscores"){ if(force_all_trait_study_hits) { gc_list<-find_hits_in_gwas_catalog(gwas_hits=dat$rsid,trait=trait,efo=efo,efo_id=efo_id,distance_threshold=distance_threshold) if(length(gc_list$not_in_gc)>0) { # if(any(!dat$rsid %in% gwas_catalog$rsid)){ # dat$rsid[!dat$rsid %in% gwas_catalog$rsid] dat2<-dat[dat$rsid %in% gc_list$not_in_gc,] #the snps not in the GWAS catalog. Genomic coordinates for SNPs associated with trait/efo in the GWAS catalog did not overlap with these SNPs (including +/- 250 kb) Dat.m2<-merge(gwas_catalog,dat2,by="rsid",all.y=TRUE) Dat.m2$z.y<-Dat.m2[,beta]/Dat.m2[,se] Dat.m2$z.x<-0 # Dat.m$plot_x Dat.m2$ancestral_group<-unique(dat$population) Names<-names(Dat.m)[!names(Dat.m) %in% names(Dat.m2)] for(i in 1:length(Names)){ Dat.m2[,Names[i]]<-NA } # Dat.m3<-Dat.m Dat.m<-rbind(Dat.m,Dat.m2) } } Dat.m$Z_scores<-"no conflict" # Z_scores<-rep("black",nrow(Dat.m)) Dat.m$Z_scores[which(sign(Dat.m$z.y) != sign(as.numeric(Dat.m$z.x)))]<-"moderate conflict" Dat.m$Z_scores[which(sign(Dat.m$z.y) != sign(as.numeric(Dat.m$z.x)) & abs(Dat.m$z.y) >= 3.890592 & abs(Dat.m$z.x) >= 3.890592 )]<-"high conflict" # Z score of 3.890592 = 2 sided p value of 0.0001 Dat.m$Z_scores[which(Dat.m$pmid==Dat.m$pubmed_id & sign(Dat.m$z.y) != sign(as.numeric(Dat.m$z.x)))]<-"high conflict" #if the signs are different but Z.x and Z.y come from the same study, then there is a clear incompatability if(force_all_trait_study_hits){ Dat.m$Z_scores[Dat.m$z.x==0]<-"high conflict" #these SNPs are not in the GWAS catalog } # Z_scores[which(sign(Dat.m$z.y) != sign(as.numeric(Dat.m$z.x)) & abs(Dat.m$z.y) >= 4.891638 & abs(Dat.m$z.x) >= 4.891638 )]<-"red" return(Dat.m) } harmonise_effect_allele<-function(dat=NULL,beta=beta){ Pos<-which(dat$effect_allele.x!=dat$effect_allele.y) beta.y<-dat[,beta][Pos]*-1 dat[,beta][Pos]<-beta.y oa<-dat$effect_allele.y[Pos] ea<-dat$other_allele[Pos] dat$effect_allele.y[Pos]<-ea dat$other_allele[Pos]<-oa eaf<-1-dat$eaf.y[Pos] dat$eaf.y[Pos]<-eaf return(dat) } #' Are hits in the GWAS catalog? #' #' Identify GWAS hits in the test dataset and see if they overlap with GWAS hits in the GWAS catalog. #' #' @param gwas_hits the "GWAS hits" in the test dataset (e.g. SNP-trait associations with P<5e-8) #' @param trait the trait of interest #' @param efo_id ID for trait of interest in the experimental factor ontology #' @param efo trait of interest in the experimental factor ontology #' @param distance_threshold distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog. #' #' @return list #' @export find_hits_in_gwas_catalog<-function(gwas_hits=NULL,trait=NULL,efo=NULL,efo_id=NULL,distance_threshold=25000){ utils::data("refdat_1000G_superpops",envir =environment()) snps_exclude<-unique(refdat_1000G_superpops$SNP) gwas_hits<-gwas_hits[!gwas_hits %in% snps_exclude] ensembl<-get_positions_biomart(gwas_hits=gwas_hits) if(!is.null(efo)) efo<-trimws(unlist(strsplit(efo,split=";"))) if(!is.null(efo_id)) efo_id<-trimws(unlist(strsplit(efo_id,split=";"))) if(!is.null(trait)) trait<-trimws(unlist(strsplit(trait,split=";"))) gwas_variants<-get_gwas_associations(reported_trait=trait,efo_trait=efo,efo_id=efo_id) # gwas_variants<-gwasrapidd::get_variants(efo_trait = efo,efo_id=efo_id,reported_trait=trait) if(class(unlist(gwas_variants)) == "character") { if(nrow(gwas_variants)==0) { warning(paste("search returned 0 variants from the GWAS catalog")) } } if(is.null(trait) & is.null(efo) & is.null(efo_id)) { genomic_range<-list(chromosome=as.character(ensembl$chr_name),start=ensembl$chrom_start - distance_threshold,end=ensembl$chrom_start + distance_threshold) gwas_variants<-gwasrapidd::get_variants(genomic_range=genomic_range) gwas_variants<-data.frame(gwas_variants@variants) ens.m<-merge(ensembl,gwas_variants,by.x="chr_name",by.y="chromosome_name",all.x=TRUE) Pos<-abs(ens.m$chrom_start.x-ens.m$chrom_start.y)<distance_threshold # Pos<-which(ens.m$chromosome_position>ens.m$bp_minus & ens.m$chromosome_position<ens.m$bp_plus) gwashit_in_gc<-unique(ens.m$refsnp_id[Pos]) gwashit_notin_gc<-unique(ens.m$refsnp_id[!ens.m$refsnp_id %in% gwashit_in_gc]) return(list("not_in_gc"=gwashit_notin_gc,"in_gc"=gwashit_in_gc)) } if(!(is.null(trait) & is.null(efo) & is.null(efo_id))){ # for now use ensembl/biomart to determine positions for GWAS catalog and test variants. Both are in GRCh38 so could also use GWAS catalog positions for GWAS catalog variats (maybe this would be faster too) but there is the risk that the reference build could diverge over time between biomart/ensembl and GWAS catalog. might update this so that chromosome positions could be based on GWAS catalog instead # if(positions_biomart) # { # gwas_variants<-data.frame(gwas_variants@variants) # gwas_hits %in% gwas_variants@variants$variant_id # ensembl2<-get_positions_biomart(gwas_hits=unique(gwas_variants$variant_id)) ensembl2<-get_positions_biomart(gwas_hits=gwas_variants@risk_alleles$variant_id) # } gwashit_in_gc<-NA if(any(ensembl$chr_name %in% ensembl2$chr_name)) { gwashit_notin_gc<-ensembl$refsnp_id[!ensembl$chr_name %in% ensembl2$chr_name] ens.m<-merge(ensembl,ensembl2,by="chr_name") # ens.m[which(ens.m$refsnp_id.x =="rs12239737"),c("chrom_start.x","chrom_start.y")] Test<-any(abs(ens.m$chrom_start.x-ens.m$chrom_start.y)<distance_threshold) if(Test) { Pos<-abs(ens.m$chrom_start.x-ens.m$chrom_start.y)<distance_threshold # Pos<-ens.m$chrom_start.x>ens.m$bp_minus.y & ens.m$chrom_start.x<ens.m$bp_plus.y gwashit_in_gc<-unique(ens.m$refsnp_id.x[Pos]) ens.m<-ens.m[!ens.m$refsnp_id.x %in% gwashit_in_gc,] Pos<-abs(ens.m$chrom_start.x-ens.m$chrom_start.y)<distance_threshold # Pos<-ens.m$chrom_start.x>ens.m$bp_minus.y & ens.m$chrom_start.x<ens.m$bp_plus.y gwashit_notin_gc<-c(gwashit_notin_gc,unique(ens.m$refsnp_id.x[!Pos])) } if(!Test) { gwashit_notin_gc<-c(gwashit_notin_gc,unique(ens.m$refsnp_id.x)) } }else{ gwashit_notin_gc<-unique(ensembl$refsnp_id) gwashit_in_gc<-NA } return(list("not_in_gc"=gwashit_notin_gc,"in_gc"=gwashit_in_gc)) } } get_positions_biomart<-function(gwas_hits=NULL){ # library(biomaRt) # Get chromosomal positions and genes names from ENSEMBL. Should be build 38. Version object contains version ID for genome build used Mart <- biomaRt::useMart(host="www.ensembl.org", biomart="ENSEMBL_MART_SNP",dataset="hsapiens_snp") Version<-biomaRt::listDatasets(Mart)[ biomaRt::listDatasets(Mart)$dataset=="hsapiens_snp","version"] message(paste0("Using ",Version," of human genome from ensembl for genomic coordinates")) Attr<-biomaRt::listAttributes(Mart) ensembl<-biomaRt::getBM(attributes=c("refsnp_id","chr_name","chrom_start"),filters="snp_filter",values=gwas_hits,mart=Mart) ensembl<-ensembl[order(ensembl$refsnp_id),] ensembl<-ensembl[nchar(ensembl$chr_name)<3,] ensembl$chr_name<-as.numeric(ensembl$chr_name) # ensembl$bp_minus<-ensembl$chrom_start - bp_down # ensembl$bp_plus<-ensembl$chrom_start + bp_up return(ensembl) } #' Flag conflicts with the GWAS catalog #' #' Flag conflicts with the GWAS catalog through comparison of reported effect alleles and reported effect allele frequency. #' #' @param dat the test dataset of interest #' @param beta name of the column containing the SNP effect size #' @param se name of the column containing the standard error for the SNP effect size. #' @param trait the trait of interest #' @param efo_id ID for trait of interest in the experimental factor ontology #' @param efo trait of interest in the experimental factor ontology #' @param gwas_catalog_ancestral_group restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian") #' @param exclude_palindromic_snps should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands. #' #' @return list #' @export flag_gc_conflicts<-function(dat=NULL,beta="lnor",se="lnor_se",efo=NULL,trait=NULL,efo_id=NULL,gwas_catalog_ancestral_group=c("European","East Asian"),exclude_palindromic_snps=TRUE){ gc_dat<-compare_effect_to_gwascatalog(dat=dat,efo=efo,trait=trait,efo_id=efo_id,beta=beta,se=se,gwas_catalog_ancestral_group=gwas_catalog_ancestral_group,exclude_palindromic_snps=exclude_palindromic_snps) effect_size_conflict<-gc_dat$Z_scores gc_conflicts<-c("high conflict","moderate conflict","no conflict") es_conflicts_list<-lapply(1:length(gc_conflicts),FUN=function(x) length(effect_size_conflict[which(effect_size_conflict==gc_conflicts[x])])) total<-length(which(!is.na(gc_dat$Z_scores))) es_conflicts_list<-c(es_conflicts_list,total) names(es_conflicts_list)<-c(gc_conflicts,"n_snps") eaf_conflicts<-gc_dat$EAF eaf_conflicts_list<-lapply(1:length(gc_conflicts),FUN=function(x) length(eaf_conflicts[which(eaf_conflicts==gc_conflicts[x])])) total<-length(which(!is.na(gc_dat$EAF))) eaf_conflicts_list<-c(eaf_conflicts_list,total) names(eaf_conflicts_list)<-c(gc_conflicts,"n_snps") # gc_ancestries<-paste(unique(gc_dat$ancestral_group),collapse="; ") all_conflicts_list<-list("effect_size_conflicts"=es_conflicts_list,"eaf_conflicts"=eaf_conflicts_list) return(all_conflicts_list) }

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simple.imputer.Rd

% Generated by roxygen2 (4.0.1): do not edit by hand \name{simple.imputer} \alias{simple.imputer} \title{simple.imputer} \usage{ simple.imputer(df) } \arguments{ \item{df}{Data frame for all data including missing and non-missing values} } \description{ I use really simple models to predict missing values. For continuous variables I use linear regression and for categorical variables I use logistic regression. } \examples{ df <- data.frame(A=c(1,2,3,1,2), B=as.factor(c(1,2,1,3,NA)), C=c(1.1, 3.5, NA, 3, NA)) df.imputed <- simple.imputer(df) } \keyword{imputation,} \keyword{impute,} \keyword{imputer}

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leoyuchuan/MachineLearning

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r

MLProject_Evaluate.R

library(h2o) library(data.table) library(Metrics) options(warn = -1) ###### Read Data Path ###### args = commandArgs(trailingOnly = TRUE) if(length(args)<1){ stop("Please provide path of training data & testing data") } path = paste(dirname(file.path(args[1])), "/", sep = "") ###### Random Forest ###### ###### Initialize h2o cluster/ Build model/ Predict ###### invisible(h2o.init(nthreads = -1, max_mem_size = '4g')) trainData = h2o.importFile(paste(path, 'pp_train.csv', sep = '')) testData = h2o.importFile(paste(path, 'pp_test.csv', sep = '')) model <- h2o.randomForest(y=7, x=1:6, training_frame = trainData, ntrees = 100, mtries = -1, max_depth = 3, nfolds = 10) pred.train <- as.data.frame(h2o.predict(model, trainData)) pred.test <- as.data.frame(h2o.predict(model, testData)) ###### Shut down h2o cluster ###### mse.train = h2o.performance(model)@metrics$MSE mse.test = h2o.performance(model, testData)@metrics$MSE invisible(h2o.shutdown(prompt = FALSE)) rm(testData, trainData, model) ###### Evaluate MAP@12 For Training Data ###### actual.train = fread(paste(path, 'pp_train.csv', sep = '')) actual.train = cbind(actual.train, pred.train) rm(pred.train) invisible(gc()) setkey(actual.train, 'predict') pred.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.train,"display_id") actual.train = actual.train[which(actual.train$clicked==1),] actual.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.train, 'display_id') actual.train = actual.train[pred.train, on='display_id'] actual.train[, c('i.ad_id'):=NULL] actual = strsplit(actual.train$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.train$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.train, pred.train) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Training Data is %.4f\n", "Random Forest", mse.train) str = paste(str, sprintf("%s: Mean Average Precision @12 For Training Data is %.4f\n", "Random Forest", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) ###### Evaluate MAP@12 For Testing Data ###### actual.test = fread(paste(path, 'pp_test.csv', sep = '')) actual.test = cbind(actual.test, pred.test) rm(pred.test) invisible(gc()) setkey(actual.test, 'predict') pred.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.test,"display_id") actual.test = actual.test[which(actual.test$clicked==1),] actual.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.test, 'display_id') actual.test = actual.test[pred.test, on='display_id'] actual.test[, c('i.ad_id'):=NULL] actual = strsplit(actual.test$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.test$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.test, pred.test) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Testing Data is %.4f\n", "Random Forest", mse.test) str = paste(str, sprintf("%s: Mean Average Precision @12 For Testing Data is %.4f\n", "Random Forest", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) rm(mse.test, mse.train) ###### Regression ###### ###### Initialize h2o cluster/ Build model/ Predict ###### invisible(h2o.init(nthreads = -1, max_mem_size = '4g')) trainData = h2o.importFile(paste(path, 'pp_train.csv', sep = '')) testData = h2o.importFile(paste(path, 'pp_test.csv', sep = '')) model <- h2o.glm(y=7, x=1:6, training_frame = trainData, family = "gaussian", nfolds = 10) pred.train <- as.data.frame(h2o.predict(model, trainData)) pred.test <- as.data.frame(h2o.predict(model, testData)) ###### Shut down h2o cluster ###### mse.train = h2o.performance(model)@metrics$MSE mse.test = h2o.performance(model, testData)@metrics$MSE invisible(h2o.shutdown(prompt = FALSE)) rm(testData, trainData, model) ###### Evaluate MAP@12 For Training Data ###### actual.train = fread(paste(path, 'pp_train.csv', sep = '')) actual.train = cbind(actual.train, pred.train) rm(pred.train) invisible(gc()) setkey(actual.train, 'predict') pred.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.train,"display_id") actual.train = actual.train[which(actual.train$clicked==1),] actual.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.train, 'display_id') actual.train = actual.train[pred.train, on='display_id'] actual.train[, c('i.ad_id'):=NULL] actual = strsplit(actual.train$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.train$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.train, pred.train) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Training Data is %.4f\n", "Regression", mse.train) str = paste(str, sprintf("%s: Mean Average Precision @12 For Training Data is %.4f\n", "Regression", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) ###### Evaluate MAP@12 For Testing Data ###### actual.test = fread(paste(path, 'pp_test.csv', sep = '')) actual.test = cbind(actual.test, pred.test) rm(pred.test) invisible(gc()) setkey(actual.test, 'predict') pred.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.test,"display_id") actual.test = actual.test[which(actual.test$clicked==1),] actual.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.test, 'display_id') actual.test = actual.test[pred.test, on='display_id'] actual.test[, c('i.ad_id'):=NULL] actual = strsplit(actual.test$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.test$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.test, pred.test) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Testing Data is %.4f\n", "Regression", mse.test) str = paste(str, sprintf("%s: Mean Average Precision @12 For Testing Data is %.4f\n", "Regression", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) rm(mse.test, mse.train) ###### Gradient Boosting ###### ###### Initialize h2o cluster/ Build model/ Predict ###### invisible(h2o.init(nthreads = -1, max_mem_size = '4g')) trainData = h2o.importFile(paste(path, 'pp_train.csv', sep = '')) testData = h2o.importFile(paste(path, 'pp_test.csv', sep = '')) model <- h2o.gbm(y=7, x=1:6, training_frame = trainData, ntrees = 100, max_depth = 4, sample_rate = 0.8, nfolds = 10) pred.train <- as.data.frame(h2o.predict(model, trainData)) pred.test <- as.data.frame(h2o.predict(model, testData)) ###### Shut down h2o cluster ###### mse.train = h2o.performance(model)@metrics$MSE mse.test = h2o.performance(model, testData)@metrics$MSE invisible(h2o.shutdown(prompt = FALSE)) rm(testData, trainData, model) ###### Evaluate MAP@12 For Training Data ###### actual.train = fread(paste(path, 'pp_train.csv', sep = '')) actual.train = cbind(actual.train, pred.train) rm(pred.train) invisible(gc()) setkey(actual.train, 'predict') pred.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.train,"display_id") actual.train = actual.train[which(actual.train$clicked==1),] actual.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.train, 'display_id') actual.train = actual.train[pred.train, on='display_id'] actual.train[, c('i.ad_id'):=NULL] actual = strsplit(actual.train$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.train$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.train, pred.train) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Training Data is %.4f\n", "Gradient Boosting", mse.train) str = paste(str, sprintf("%s: Mean Average Precision @12 For Training Data is %.4f\n", "Gradient Boosting", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) ###### Evaluate MAP@12 For Testing Data ###### actual.test = fread(paste(path, 'pp_test.csv', sep = '')) actual.test = cbind(actual.test, pred.test) rm(pred.test) invisible(gc()) setkey(actual.test, 'predict') pred.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.test,"display_id") actual.test = actual.test[which(actual.test$clicked==1),] actual.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.test, 'display_id') actual.test = actual.test[pred.test, on='display_id'] actual.test[, c('i.ad_id'):=NULL] actual = strsplit(actual.test$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.test$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.test, pred.test) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Testing Data is %.4f\n", "Gradient Boosting", mse.test) str = paste(str, sprintf("%s: Mean Average Precision @12 For Testing Data is %.4f\n", "Gradient Boosting", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) rm(mse.test, mse.train) ###### Deep Learning ###### ###### Initialize h2o cluster/ Build model/ Predict ###### invisible(h2o.init(nthreads = -1, max_mem_size = '4g')) trainData = h2o.importFile(paste(path, 'pp_train.csv', sep = '')) testData = h2o.importFile(paste(path, 'pp_test.csv', sep = '')) model <- h2o.deeplearning(y=7, x=1:6, training_frame = trainData, hidden = c(20,20), epochs = 20, activation = 'Rectifier', nfolds = 10) pred.train <- as.data.frame(h2o.predict(model, trainData)) pred.test <- as.data.frame(h2o.predict(model, testData)) ###### Shut down h2o cluster ###### mse.train = h2o.performance(model)@metrics$MSE mse.test = h2o.performance(model, testData)@metrics$MSE invisible(h2o.shutdown(prompt = FALSE)) rm(testData, trainData, model) ###### Evaluate MAP@12 For Training Data ###### actual.train = fread(paste(path, 'pp_train.csv', sep = '')) actual.train = cbind(actual.train, pred.train) rm(pred.train) invisible(gc()) setkey(actual.train, 'predict') pred.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.train,"display_id") actual.train = actual.train[which(actual.train$clicked==1),] actual.train <- actual.train[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.train, 'display_id') actual.train = actual.train[pred.train, on='display_id'] actual.train[, c('i.ad_id'):=NULL] actual = strsplit(actual.train$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.train$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.train, pred.train) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Training Data is %.4f\n", "Deep Learning", mse.train) str = paste(str, sprintf("%s: Mean Average Precision @12 For Training Data is %.4f\n", "Deep Learning", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) ###### Evaluate MAP@12 For Testing Data ###### actual.test = fread(paste(path, 'pp_test.csv', sep = '')) actual.test = cbind(actual.test, pred.test) rm(pred.test) invisible(gc()) setkey(actual.test, 'predict') pred.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] invisible(gc()) setkey(pred.test,"display_id") actual.test = actual.test[which(actual.test$clicked==1),] actual.test <- actual.test[,.(ad_id=paste(rev(ad_id),collapse=" ")),by=display_id] setkey(actual.test, 'display_id') actual.test = actual.test[pred.test, on='display_id'] actual.test[, c('i.ad_id'):=NULL] actual = strsplit(actual.test$ad_id, " ") actual = lapply(actual, as.integer) predicted = strsplit(pred.test$ad_id, " ") predicted = lapply(predicted, as.integer) rm(actual.test, pred.test) MAP12 = mapk(12, actual, predicted) str = sprintf("%s: Mean Squared Error For Testing Data is %.4f\n", "Deep Learning", mse.test) str = paste(str, sprintf("%s: Mean Average Precision @12 For Testing Data is %.4f\n", "Deep Learning", MAP12), sep = "") cat(str) write(str, file = paste(path, "output.log", sep = ''), append = TRUE) rm(actual, predicted, MAP12, str) rm(mse.test, mse.train)

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/R/MaxPrecip.R

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nburola/climateimpacts

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MaxPrecip.R

#' Function to find the greatest recorded precipitation values in a daily rainfall dataset for a particular location during the given period #' #' #' @param precip precipitation in inches/day #' @param station describes the number or name of the precipitation gauge or station #' @return the greatest recorded rainfall value MaxPrecip = function(precip, station = "Cachuma") { precip_subset <- precip %>% dplyr::mutate(year_month_day = lubridate::parse_date_time(date, "ymd"), year = lubridate::year(date), month = lubridate::month(date), day = lubridate::day(date), rainfall = as.numeric(rainfall)) %>% dplyr::filter(station == "station") %>% dplyr::select(rain) precip_subset_max <- apply(precip_subset, MARGIN = 2, FUN = max) return(list(Station = station, Max_Precip = precip_subset_max)) }

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diagt.o.Rd

\name{diagt.o} \alias{diagt.o} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Gia tri cua xet nghiem } \description{ Gia tri cua xet nghiem khi biet ket qua xet nghiem (nhi gia) va benh (nhi gia) Su dung khi muon ket hop gia tri cua nhieu xet nghiem thanh mot bang } \usage{ diagt.o(D, btest, digits = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{btest}{Ket qua xet nghiem co ket qua nhi gia} \item{D}{ Tinh trang benh theo xet nghiem tieu chuan vang} \item{ci}{ Co ghi nhan khoang tin cay ra hay khong } } \author{Do Van Dung <dovandzung@gmail.com>} \examples{ data(lact) estat(lact$vmn,lact$lact.dnt,cutoff=0.5) diagt(lact$vmn,lact$lact.dnt>3.14) diagt.o(lact$vmn,lact$lact.dnt>3.14) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ ~kwd1 } \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line

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/DRHMCcodes/lingauss_statespace/kalman_filters.R

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kalman_filters.R

require(CIPlib) require(stats) posterior_kernel <- function(lam_x,lam_y,omega,y){ T <- length(y); phi <- tanh(CIP_AR1_psi(omega,T)$psi) sigmav <- exp(-0.5*lam_x); sigmay <- exp(-0.5*lam_y); margstd <- sigmav/sqrt(1.0-phi^2); # kalman filter aa <- 0.0; PP <- margstd^2; ll <- 0.0 for (t in 1:T){ et <- y[t] - aa; Dt <- PP + sigmay^2 ll <- ll - 0.5*log(Dt) - 0.5*et^2/Dt; Kt <- (phi*PP)/Dt; aa <- phi*aa + Kt*et; Lt <- phi - Kt; Jt <- sigmav; # - Kt*sigmay; PP <- phi*PP*Lt + sigmav*Jt; } return(ll) } post_kern_lx <- function(from,to,ng,lam_y,omega,y){ grid <- as.vector(seq(from=from,to=to,length.out=ng)) lkern <- 0*grid mu.1 <- 0*grid mu.T <- 0*grid sig.1 <- 0*grid sig.T <- 0*grid T <- length(y); phi <- tanh(CIP_AR1_psi(omega,T)$psi) sigmay <- exp(-0.5*lam_y); for(i in 1:ng){ lkern[i] <- posterior_kernel(grid[i],lam_y,omega,y) #sigmax <- exp(-0.5*grid[i]); #Sig <- toeplitz(sigmax^2/(1.0-phi^2)*(phi^(0:(T-1)))) #mu.post <- Sig%*%solve(Sig+sigmay^2*diag(1.0,T,T),y) #Sig.post <- Sig - Sig%*%solve(Sig+sigmay^2*diag(1.0,T,T),Sig) #mu.1[i] <- mu.post[1] #mu.T[i] <- mu.post[T] #sig.1[i] <- sqrt(Sig.post[1,1]) #sig.T[i] <- sqrt(Sig.post[T,T]) } wts <- exp(lkern-max(lkern)) wts <- wts/sum(wts) ret <- cbind(grid,wts,lkern)#,mu.1,sig.1,mu.T,sig.T) } post_kern_ly <- function(from,to,ng,lam_x,omega,y,pri_mean){ grid <- as.vector(seq(from=from,to=to,length.out=ng)) lkern <- 0*grid T <- length(y); phi <- tanh(CIP_AR1_psi(omega,T)$psi) for(i in 1:ng){ lkern[i] <- posterior_kernel(lam_x,grid[i],omega,y) - 0.5*(grid[i]-pri_mean)^2/(9.0) } wts <- exp(lkern-max(lkern)) wts <- wts/sum(wts) ret <- cbind(grid,wts,lkern) }

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/man/oshka-package.Rd

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oshka-package.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/oshka-package.R \docType{package} \name{oshka-package} \alias{oshka-package} \title{Recursive Quoted Language Expansion} \description{ Expands quoted language by recursively replacing any symbol that points to quoted language with the language it points to. The recursive process continues until only symbols that point to non-language objects remain. The resulting quoted language can then be evaluated normally. This differs from the traditional 'quote'/'eval' pattern because it resolves intermediate language objects that would interfere with evaluation. }

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server.R

# # This is the server logic of a Shiny web application. You can run the # application by clicking 'Run App' above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # library(shiny) # Define server logic required to get denomination shinyServer(function(input, output) { # Create a reactiveValues object where we can track some extra elements # reactively. #Show inputted denomination output$currentDenom<- renderText({ input$calc paste("Current Denomination is : ",isolate(input$den)) }) #Initialize reactive values calcdenom <- reactiveValues() #Calculations observe({ input$calc calcdenom$calculate_onet <- isolate({ (input$den %/% 1000) }) calcdenom$calculate_fiveh <- isolate({ ((input$den %% 1000) %/% 500) }) calcdenom$calculate_twoh <- isolate({ (((input$den %% 1000) %% 500) %/%200) }) calcdenom$calculate_oneh <- isolate({ ((((input$den %% 1000) %% 500) %%200) %/%100) }) calcdenom$calculate_fif <- isolate({ (((((input$den %% 1000) %% 500) %%200) %%100) %/% 50) }) calcdenom$calculate_twen <- isolate({ ((((((input$den %% 1000) %% 500) %%200) %%100) %% 50) %/% 20) }) calcdenom$calculate_ten <- isolate({ (((((((input$den %% 1000) %% 500) %%200) %%100) %% 50) %%20 ) %/% 10) }) calcdenom$calculate_five <- isolate({ ((((((((input$den %% 1000) %% 500) %%200) %%100) %% 50) %%20 ) %% 10) %/%5) }) calcdenom$calculate_one <- isolate({ (((((((((input$den %% 1000) %% 500) %%200) %%100) %% 50) %%20 ) %% 10) %%5) %/% 1) }) }) #Printing the output data output$onet<- renderText({ paste("1000 PHP bill: ",calcdenom$calculate_onet) }) output$fiveh<- renderText({ paste("500 PHP bill: ",calcdenom$calculate_fiveh) }) output$twoh<- renderText({ paste("200 PHP bill: ",calcdenom$calculate_twoh) }) output$oneh<- renderText({ paste("100 PHP bill: ",calcdenom$calculate_oneh) }) output$fif <- renderText(({ paste("50 PHP bill: ",calcdenom$calculate_fif) })) output$twen <- renderText(({ paste("20 PHP bill: ",calcdenom$calculate_twen) })) output$ten <- renderText(({ paste("10 coin/s: ",calcdenom$calculate_ten) })) output$five <- renderText(({ paste("5 coin/s: ",calcdenom$calculate_five) })) output$one <- renderText(({ paste("1 coin/s: ",calcdenom$calculate_one) })) })

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cachematrix.R

## The functions do more or less the same actions that the example does, only using the ## solve function instead of the mean function ## this function return a list of methods (get, setinverse and getinverse) that will be used ## by the cacheSolve function. ## ## The getInverse function return the cachedInvertedMatrix if it has been already calculated. makeCacheMatrix <- function(x = matrix()) { cachedInvertedMatrix <- NULL get <- function() {x} setinverse <- function(invertedMatrix) cachedInvertedMatrix <<- invertedMatrix getinverse <- function() cachedInvertedMatrix list(get = get, setinverse = setinverse, getinverse = getinverse) } ## This function uses the methods created in the makeCacheMatrix function in order to use or set the ## cachedInvertedMatrix instead of calculate all the time. In ths way the solve function will be applied only the ## first time you calculate the inverted value of a specific matrix. The subsequent times you need the same inverted ## matrix the cached value will be used cacheSolve <- function(x, ...) { invertedMatrix <- x$getinverse() if(!is.null(invertedMatrix)) { message("getting cached data") return(invertedMatrix) } data <- x$get() invertedMatrix <- solve(data, ...) x$setinverse(invertedMatrix) invertedMatrix }

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/NumberTheory/man/is.spsp.Rd

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michallbujak/NumberTheory

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refs/heads/master

2022-12-20T01:15:17.322787

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functions.R \name{is.spsp} \alias{is.spsp} \title{Strongly pseudo-prime} \usage{ is.spsp(a, n) } \arguments{ \item{a}{number to be checked} \item{n}{modulus} } \value{ boolean value } \description{ Check whether the number a is strongly pseudo-prime with respect to n }

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/statistics.R

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sandy149/analytics_01

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refs/heads/master

2020-03-26T20:06:33.788159

2018-08-21T17:44:26

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statistics.R

#Basic stats x <- ceiling(rnorm(10000, mean = 60, sd = 20)) # create data for normal distribution mean(x) median(x) #no mode function table(x) sort(table(x), decreasing = T ) library(modeest) mlv(x, method = 'shorth') quantile(x) quantile(x, seq(.1,1,by = .1)) #decile quantile(x, seq(0.01, 1, by = 0.01)) #percentile library(e1071) plot(density(x)) e1071::skewness(x) kurtosis(x) sd(x); var(x) cov(women$height, women$weight) cov(women$weight, women$height) #feq Table library(fdth) ftable1 = fdt(x) ftable1 stem(x)

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/R/fitArCo.R

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rmasini/ARCO

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116e8d00d1516ce714be061d1434a51f568fb6ce

refs/heads/master

2021-01-22T21:27:22.687936

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fitArCo.R

#' Estimates the ArCo using the model selected by the user #' #' Estimates the Artificial Counterfactual unsing any model supplied by the user, calculates the most relevant statistics and allows for the counterfactual confidence intervals to be estimated by block bootstrap. #' #' @details This description may be useful to clarify the notation and understand how the arguments must be supplied to the functions. #' \itemize{ #' \item{units: }{Each unity is indexed by a number between 1,...,n. They are for exemple: countries, states, municipalities, firms, etc.} #' \item{Variables: }{For each unity and for every time period t=1,...,T we observe q_i >= 1 variables. They are for example: GDP, inflation, sales, etc.} #' \item{Intervention: }{The intervention took place only in the treated unity at time t0=L0*T, where L0 is in (0,1).} #' } #' #' @param data A list of matrixes or dataframes of length q. Each matrix is T X n and it contains observations of a single variable for all units and all periods of time. Even in the case of a single variable (q=1), the matrix must be inside a list. #' @param fn The function used to estimate the first stage model. This function must receive only two arguments in the following order: X (independent variables), y (dependent variable). If the model requires additional arguments they must be supplied inside the function fn. #' @param p.fn The function used to estimate the predict using the first stage model. This function also must receive only two arguments in the following order: model (model estimated in the first stage), newdata (out of sample data to estimate the second stage). If the prediction requires additional arguments they must be supplied inside the function p.fn. #' @param treated.unity Single number indicating the unity where the intervention took place. #' @param t0 Single number indicating the intervention period. #' @param lag Number of lags in the first stage model. Default is 0, i.e. only contemporaneous variables are used. #' @param Xreg Exogenous controls. #' @param alpha Significance level for the delta. #' @param boot.cf Should bootstrap confidence intervals for the counterfactual be calculated (default=FALSE). #' @param R Number of bootstrap replications in case boot.cf=TRUE. #' @param l Block length for the block bootstrap. #' @param VCOV.type Type of covariance matrix for the delta. "iid" for standard covariance matrix, "var" or "varhac" to use prewhitened covariance matrix using VAR models, "varhac" selects the order of the VAR automaticaly and "nw" for Newey West. In the last case the user may select the kernel type and combine the kernel with the VAR prewhitening. For more details see Andrews and Monahan (1992). #' @param VCOV.lag Lag used on the robust covariance matrix if VCOV.type is different from "iid". #' @param bandwidth.kernel Kernel bandwidth. If NULL the bandwidth is automatically calculated. #' @param kernel.type Kernel to be used for VCOV.type="nw". #' @param VHAC.max.lag Maximum lag of the VAR in case VCOV.type="varhac". #' @param prewhitening.kernel If TRUE and VCOV.type="nw", the covariance matrix is calculated with prewhitening (default=FALSE). #' @return An object with S3 class fitArCo. #' \item{cf}{estimated counterfactual} #' \item{fitted}{In sample fitted values for the pre-treatment period.} #' \item{model}{A list with q estimated models, one for each variable. Each element in the list is the output of the fn function.} #' \item{delta}{The delta statistics and its confidence interval.} #' \item{data}{The data used.} #' \item{t0}{The intervention period used.} #' \item{treated.unity}{The treated unity used.} #' \item{boot.cf}{A list with the bootstrap result (boot.cf=TRUE) or logical FALSE (boot.cf=FALSE). In the first case, each element in the list refeers to one bootstrap replication of the counterfactual, i. e. the list length is R.} #' \item{call}{The matched call.} #' @keywords ArCo #' @export #' @import Matrix glmnet #' @importFrom stats cov embed qnorm #' @examples #' ############################# #' ## === Example for q=1 === ## #' ############################# #' data(data.q1) #' # = First unity was treated on t=51 by adding a constant equal 3 #' #' data=list(data.q1) # = Even if q=1 the data must be in a list #' #' ## == Fitting the ArCo using linear regression == ## #' # = creating fn and p.fn function = # #' fn=function(X,y){ #' return(lm(y~X)) #' } #' p.fn=function(model,newdata){ #' b=coef(model) #' return(cbind(1,newdata) %*% b) #' } #' #' ArCo=fitArCo(data = data,fn = fn, p.fn = p.fn, treated.unity = 1 , t0 = 51) #' #' ############################# #' ## === Example for q=2 === ## #' ############################# #' #' # = First unity was treated on t=51 by adding constants 15 and -10 #' # for the first and second variables #' #' data(data.q2) # data is already a list #' #' ## == Fitting the ArCo using the package glmnet == ## #' ## == Quadratic Spectral kernel weights for two lags == ## #' #' ## == Fitting the ArCo using the package glmnet == ## #' ## == Bartlett kernel weights for two lags == ## #' require(glmnet) #' set.seed(123) #' ArCo2=fitArCo(data = data.q2,fn = cv.glmnet, p.fn = predict,treated.unity = 1 , t0 = 51, #' VCOV.type = "nw",kernel.type = "QuadraticSpectral",VCOV.lag = 2) #' #' @references Carvalho, C., Masini, R., Medeiros, M. (2016) "ArCo: An Artificial Counterfactual Approach For High-Dimensional Panel Time-Series Data.". #' #' Andrews, D. W., & Monahan, J. C. (1992). An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator. Econometrica: Journal of the Econometric Society, 953-966. fitArCo=function (data, fn, p.fn, treated.unity, t0, lag = 0, Xreg = NULL, alpha = 0.05, boot.cf = FALSE, R = 100, l = 3,VCOV.type=c("iid","var","nw","varhac"),VCOV.lag=1,bandwidth.kernel=NULL,kernel.type=c("QuadraticSpectral","Truncated","Bartlett","Parzen","TukeyHanning"),VHAC.max.lag=5,prewhitening.kernel=FALSE) { VCOV.type=match.arg(VCOV.type) kernel.type=match.arg(kernel.type) if (boot.cf == TRUE) { if (R < 10) { stop("Minimum number of bootstrap samples is 10.") } } if (is.null(names(data))) { names(data) = paste("Variable", 1:length(data), sep = "") } for (i in 1:length(data)) { if (is.null(colnames(data[[i]]))) { colnames(data[[i]]) = paste("Unity", 1:ncol(data[[i]]), sep = "") } } for (i in 1:length(data)) { aux = length(unique(colnames(data[[i]]))) k = ncol(data[[i]]) if (aux < k) { colnames(data[[i]]) = paste("Unity", 1:ncol(data[[i]]), sep = "") } } if (length(data) == 1) { Y = matrix(data[[1]][, treated.unity], ncol = 1) X = data[[1]][, -treated.unity] X = as.matrix(X) colnames(X) = paste(names(data), colnames(data[[1]])[-treated.unity], sep = ".") }else { Y = Reduce("cbind", lapply(data, function(x) x[, treated.unity])) X = Reduce("cbind", lapply(data, function(x) x[, -treated.unity])) aux = list() for (i in 1:length(data)) { aux[[i]] = paste(names(data)[i], colnames(data[[i]])[-treated.unity], sep = ".") } colnames(X) = unlist(aux) } Y.raw = Y if (lag != 0) { aux1 = sort(rep(0:lag, ncol(X))) aux = paste(rep(colnames(X), lag + 1), "lag", aux1, sep = ".") X = embed(X, lag + 1) colnames(X) = aux Y = tail(Y, nrow(X)) } if (length(Xreg) != 0) { X = cbind(X, tail(Xreg, nrow(X))) } if (is.vector(Y)) { Y = matrix(Y, length(Y), 1) } T=nrow(X) y.fit = matrix(Y[1:(t0 - 1 - lag), ], ncol = length(data)) y.pred = matrix(Y[-c(1:(t0 - 1 - lag)), ], ncol = length(data)) x.fit = X[1:(t0 - 1 - lag), ] x.pred = X[-c(1:(t0 - 1 - lag)), ] save.cf = matrix(NA, nrow(y.pred), length(data)) save.fitted = matrix(NA, nrow(Y), length(data)) model.list = list() for (i in 1:length(data)) { model = fn(x.fit, y.fit[, i]) model.list[[i]] = model contra.fact = p.fn(model, x.pred) save.cf[, i] = contra.fact save.fitted[, i] = p.fn(model, X) } boot.list = FALSE if (boot.cf == TRUE) { serie = cbind(y.fit, x.fit) q = length(data) bootfunc = function(serie) { y.fit = serie[, 1:q] x.fit = serie[, -c(1:q)] if (is.vector(y.fit)) { y.fit = matrix(y.fit, ncol = 1) } save.cf.boot = matrix(NA, nrow(x.pred), q) for (i in 1:q) { model.boot = fn(x.fit, y.fit[, i]) contra.fact.boot = p.fn(model.boot, x.pred) save.cf.boot[, i] = contra.fact.boot } return(as.vector(save.cf.boot)) } boot.cf = boot::tsboot(serie, bootfunc, R = R, l = 3, sim = "fixed") boot.stat = boot.cf$t boot.list = list() for (i in 1:nrow(boot.stat)) { boot.list[[i]] = matrix(boot.stat[i, ], ncol = q) } } delta.aux = tail(Y.raw, nrow(save.cf)) - save.cf delta = colMeans(delta.aux) aux = matrix(0, T, length(data)) aux[(t0 - lag):nrow(aux), ] = 1 vhat = Y - (save.fitted + t(t(aux) * delta)) v1 = matrix(vhat[1:(t0 - lag - 1), ], ncol = length(data)) v2 = matrix(vhat[(t0 - lag):nrow(vhat), ], ncol = length(data)) t0lag=t0-lag sigmahat=T*switch(VCOV.type, iid = cov(v1)/(t0lag-1) + cov(v2)/(T-t0lag), var = VAR(v1,VCOV.lag)$LR/(t0lag-1) + VAR(v2,VCOV.lag)$LR/(T-t0lag), nw = neweywest(v1,NULL,kernel.type,prewhitening.kernel,VCOV.lag)/(t0lag-1) + neweywest(v2,NULL,kernel.type,prewhitening.kernel,VCOV.lag)/(T-t0lag), varhac = VARHAC(v1,VHAC.max.lag)/(t0lag-1) + VARHAC(v2,VHAC.max.lag)/(T-t0lag) ) w = sqrt(diag(sigmahat)) W = T * t(delta) %*% solve(sigmahat) %*% delta p.value = 1 - stats::pchisq(W, length(delta)) uI = delta + (w * qnorm(1 - alpha/2))/sqrt(T) lI = delta - (w * qnorm(1 - alpha/2))/sqrt(T) delta.stat = cbind(LB = lI, delta = delta, UB = uI) names(model.list) = names(data) colnames(save.cf) = names(data) rownames(save.cf) = tail(rownames(Y.raw), nrow(save.cf)) colnames(save.fitted) = names(data) rownames(save.fitted) = head(rownames(Y), nrow(save.fitted)) rownames(delta.stat) = names(data) save.fitted = head(save.fitted, nrow(save.fitted) - nrow(save.cf)) if (typeof(boot.list) == "list") { NAboot = Reduce(sum, boot.list) if (is.na(NAboot)) { warning("Some of the boostrap counterfactuals may have returned NA values. \n \n A possible cause is the number of observations being close the number of variables if the lm function was used.") } } result = list(cf = save.cf, fitted = save.fitted, model = model.list, delta = delta.stat, p.value = p.value, data = data, t0 = t0, treated.unity = treated.unity, boot.cf = boot.list, call = match.call()) class(result) = "fitArCo" return(result) }

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/R/plotting.R

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shaoyoucheng/monaLisa

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refs/heads/master

2023-08-19T06:05:17.036710

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plotting.R

#' @importFrom grDevices colorRampPalette #' @importFrom graphics axis hist lines par plot rect rug segments barplot matplot abline legend text #' @importFrom stats density dist hclust #' @importFrom S4Vectors isEmpty NULL #' @title Get colors by bin. #' #' @description Get colors for elements according to their bin. #' Colors are assigned to bins forming a gradient from \code{col1} #' to \code{col2} in the order of \code{levels{b}}. \code{col0} is assigned #' to the neutral bin (attribute \code{""}) if available. #' #' @param b A factor that groups elements into bins (typically the output of #' \code{\link{bin}}). #' @param col1 First color. #' @param col2 Second color. #' @param col0 Neutral color. #' #' @seealso \code{\link{bin}}. #' #' @return A character vector with colors for the elements in \code{b}. #' #' @examples #' set.seed(1) #' x <- rnorm(100) #' b <- bin(x, "equalN", nElements = 10) #' cols <- getColsByBin(b) #' #' @export getColsByBin <- function(b, col1 = c("#003C30", "#01665E", "#35978F", "#80CDC1", "#C7EAE5"), col2 = c("#F6E8C3", "#DFC27D", "#BF812D", "#8C510A", "#543005"), col0 = "#F5F5F5") { if (!is.factor(b)) { b <- factor(b, levels = unique(b)) b <- setZeroBin(b, NA) } if (!is.null(getZeroBin(b)) && !is.na(getZeroBin(b))) { bin0 <- getZeroBin(b) cols <- c(colorRampPalette(col1)(bin0 - 1L), "#AAAAAA33", colorRampPalette(col2)(nlevels(b) - bin0)) } else { nh <- round(nlevels(b) / 2) cols <- c(colorRampPalette(col1)(nh), colorRampPalette(col2)(nlevels(b) - nh)) } res <- cols[b] names(cols) <- levels(b) attr(res, "cols") <- cols return(res) } #' @title Histogram of binned elements. #' #' @description Plot a histogram of binned elements with binning information. #' #' @param x A numerical vector with the values used for binning. #' @param b A factor that groups elements of \code{x} into bins (typically the output of #' \code{\link{bin}}). #' @param breaks Controls the histogram breaks (passed to \code{hist(...)}). #' @param xlab Label for x-axis. #' @param ylab Label for y-axis. #' @param main Main title. #' @param legend If not \code{NULL}, draw a legend with binning information (will #' be passed to \code{legend(x=legend)} to control legend position). #' @param legend.cex A scalar that controls the text size in the legend relative #' to the current \code{par("cex")} (see \code{\link{legend}}). #' @param ... Further arguments passed to \code{\link{getColsByBin}}. #' #' @seealso \code{\link{getColsByBin}}, \code{\link[graphics]{hist}} #' #' @return Invisibly the return value of \code{hist(...)} that generated the plot. #' #' @examples #' set.seed(1) #' x <- rnorm(100) #' b <- bin(x, "equalN", nElements = 10) #' plotBinHist(x, b) #' #' @export plotBinHist <- function(x, b, breaks = 10 * nlevels(b), xlab = deparse(substitute(x, env = as.environment(-1))), ylab = "Frequency", main = "", legend = "topright", legend.cex = 1.0, ...) { .assertVector(x = b, type = "factor", len = length(x)) stopifnot("breaks" %in% names(attributes(b))) .assertScalar(x = legend.cex, type = "numeric", rngExcl = c(0, Inf)) cols <- getColsByBin(b, ...) binbreaks <- attr(b, "breaks") bincols <- attr(cols, "cols") h <- hist(x, breaks = breaks, plot = FALSE) par(mar = c(5, 4, 4 - if (main == "") 3 else 0, 2) + 0.1, cex = 1.25) ret <- hist(x, breaks = breaks, col = bincols[findInterval(h$mids, binbreaks, all.inside = TRUE)], xlab = xlab, ylab = ylab, main = main) pusr <- par('usr') segments(x0 = pusr[c(1,1)], y0 = pusr[c(4,3)], x1 = pusr[c(1,2)], y1 = pusr[c(3,3)]) rug(binbreaks, col = "black") if (!is.null(legend) && legend[1] != FALSE) legend(x = legend, legend = sprintf("%s : %d", levels(b), table(b)), fill = bincols, bty = "n", cex = legend.cex) invisible(ret) } #' @title Density plot of binned elements. #' #' @description Plot the density of binned elements with binning information. #' #' @param x A numerical vector with the values used for binning. #' @param b A factor that groups elements of \code{x} into bins (typically the output of #' \code{\link{bin}}). #' @param xlab Label for x-axis. #' @param ylab Label for y-axis. #' @param main Main title. #' @param legend If not \code{NULL}, draw a legend with binning information (will #' be passed to \code{legend(x=legend)} to control legend position). #' @param legend.cex A scalar that controls the text size in the legend relative #' to the current \code{par("cex")} (see \code{\link{legend}}). #' @param ... Further arguments passed to \code{\link{getColsByBin}}. #' #' @seealso \code{\link{getColsByBin}} #' #' @return Invisibly the return value of \code{density(x)} that generated the plot. #' #' @examples #' set.seed(1) #' x <- rnorm(100) #' b <- bin(x, "equalN", nElements = 10) #' plotBinDensity(x, b) #' #' @export plotBinDensity <- function(x, b, xlab = deparse(substitute(x, env = as.environment(-1))), ylab = "Density", main = "", legend = "topright", legend.cex = 1.0, ...) { .assertVector(x = b, type = "factor", len = length(x)) stopifnot("breaks" %in% names(attributes(b))) .assertScalar(x = legend.cex, type = "numeric", rngExcl = c(0, Inf)) cols <- getColsByBin(b, ...) binbreaks <- attr(b, "breaks") bincols <- attr(cols, "cols") par(mar = c(5, 4, 4 - if (main == "") 3 else 0, 2) + 0.1, cex = 1.25) ret <- density(x) plot(ret$x, ret$y, type = "l", col = "black", xlab = xlab, ylab = ylab, main = main, axes = FALSE) axis(1) axis(2) pusr <- par('usr') segments(x0 = pusr[c(1,1)], y0 = pusr[c(4,3)], x1 = pusr[c(1,2)], y1 = pusr[c(3,3)]) rug(binbreaks, col = "black") dx <- diff(ret$x[seq_len(2)]) / 2 rect(xleft = ret$x - dx, ybottom = 0, xright = ret$x + dx, ytop = ret$y, col = bincols[findInterval(ret$x, binbreaks, all.inside = TRUE)], border = NA) lines(ret$x, ret$y) if (!is.null(legend) && legend[1] != FALSE) legend(x = legend, legend = sprintf("%s : %d", levels(b), table(b)), fill = bincols, bty = "n", cex = legend.cex) invisible(ret) } #' @title Scatter plot (xy-plot) of binned elements. #' #' @description Plot a scatter (xy-plot) of binned elements with binning information. #' #' @param x A numerical vector with x values. #' @param y A numerical vector with y values (the values used for binning). #' @param b A factor that groups elements of \code{x,y} into bins (typically the output #' of \code{\link{bin}(y)}). #' @param cols A color vector (will be computed based on \code{b} by default using #' \code{\link{getColsByBin}(b)}). #' @param xlab Label for x-axis. #' @param ylab Label for y-axis. #' @param main Main title. #' @param legend If not \code{NULL}, draw a legend with binning information (will #' be passed to \code{legend(x=legend)} to control legend position). #' @param legend.cex A scalar that controls the text size in the legend relative #' to the current \code{par("cex")} (see \code{\link{legend}}). #' @param ... Further arguments passed to \code{plot(x, y, ...)}. #' #' @seealso \code{\link{bin}}, \code{\link{getColsByBin}} #' #' @return Invisibly the return value of \code{plot(x, y, ...)} that generated the plot. #' #' @examples #' set.seed(1) #' x <- rnorm(100) #' y <- rnorm(100) #' b <- bin(y, "equalN", nElements = 10) #' plotBinScatter(x, y, b) #' #' @export plotBinScatter <- function(x, y, b, cols = getColsByBin(b), xlab = deparse(substitute(x, env = as.environment(-1))), ylab = deparse(substitute(y, env = as.environment(-1))), main = "", legend = "topright", legend.cex = 1.0, ...) { .assertVector(x = y, len = length(x)) .assertVector(x = b, len = length(x)) .assertScalar(x = legend.cex, type = "numeric", rngExcl = c(0, Inf)) if (length(cols) == 1L) cols <- rep(cols, length(x)) stopifnot(length(x) == length(cols)) par(mar = c(5, 4, 4 - if (main == "") 3 else 0, 2) + 0.1, cex = 1.25) ret <- plot(x, y, pch = 16, cex = 0.6, col = cols, xlab = xlab, ylab = ylab, main = main, axes = FALSE, ...) axis(1) axis(2) pusr <- par('usr') segments(x0 = pusr[c(1,1)], y0 = pusr[c(4,3)], x1 = pusr[c(1,2)], y1 = pusr[c(3,3)]) if (!is.null(legend) && legend[1] != FALSE) { stopifnot("cols" %in% names(attributes(cols))) bincols <- attr(cols, "cols") legend(x = legend, legend = sprintf("%s : %d", levels(b), table(b)), fill = bincols, bty = "n", cex = legend.cex) } invisible(ret) } #' @title Heatmap of motif enrichments. #' #' @description Plot motif enrichments (e.g. significance or magnitude) as a heatmap. #' #' @param x A \code{\link[SummarizedExperiment]{SummarizedExperiment}} with numerical matrices #' (motifs-by-bins) in its \code{assays()}, typically the return value #' of \code{\link{calcBinnedMotifEnrR}} or \code{\link{calcBinnedMotifEnrHomer}}. #' @param which.plots Selects which heatmaps to plot (one or several from \code{"negLog10P"}, #' \code{"negLog10Padj"}, \code{"pearsonResid"} and \code{"log2enr"}). #' @param width The width (in inches) of each individual heatmap, without legend. #' @param col.enr Colors used for enrichment heatmap ("pearsonResid" and "log2enr"). #' @param col.sig Colors used for significance hetmaps ("negLog10P" and "negLog10Padj"). #' @param col.gc Colors used for motif GC content (for \code{show_motif_GC = TRUE}). #' @param maxEnr Cap color mapping at enrichment = \code{maxEnr} (default: 99.5th percentile). #' @param maxSig Cap color mapping at -log10 P value or -log10 FDR = \code{maxSig} #' (default: 99.5th percentile). #' @param highlight A logical vector indicating motifs to be highlighted. #' @param cluster If \code{TRUE}, the order of transcription factors will be determined by #' hierarchical clustering of the \code{"pearsonResid"} component. Alternatively, an #' \code{hclust}-object can be supplied which will determine the motif ordering. #' No reordering is done for \code{cluster = FALSE}. #' @param show_dendrogram If \code{cluster != FALSE}, controls whether to show #' a row dendrogram for the clustering of motifs. Ignored for \code{cluster = FALSE}. #' @param show_motif_GC If \code{TRUE}, show a column with the percent G+C of the motif #' as part of the heatmap. #' @param show_seqlogo If \code{TRUE}, show a sequence logo next to each motif label. #' This will likely only make sense for a heatmap with a low number of motifs. #' @param width.seqlogo The width (in inches) for the longest sequence logo (shorter #' logos are drawn to scale). #' @param use_raster \code{TRUE} or \code{FALSE} (default). Passed to \code{use_raster} #' of \code{\link[ComplexHeatmap]{Heatmap}}. #' @param na_col "white" (default). Passed to \code{na_col} of #' \code{\link[ComplexHeatmap]{Heatmap}}. #' @param ... Further arguments passed to \code{\link[ComplexHeatmap]{Heatmap}} #' when creating the main heatmaps selected by \code{which.plots}. #' #' @details The heatmaps are created using the \pkg{ComplexHeatmap} package #' and plotted side-by-side. #' #' Each heatmap will be \code{width} inches wide, so the total plot needs a #' graphics device with a width of at least \code{length(which.plots) * width} #' plus the space used for motif names and legend. The height will be auto-adjusted to #' the graphics device. #' #' @seealso \code{\link{bin}}, \code{\link[ComplexHeatmap]{Heatmap}} #' #' @references Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional #' genomic data. Bioinformatics 2016. #' #' @return A list of \code{ComplexHeatmap::Heatmap} objects. #' #' @examples #' se <- readRDS(system.file("extdata", "results.binned_motif_enrichment_LMRs.rds", package = "monaLisa")) #' i <- which(SummarizedExperiment::assay(se, "negLog10Padj")[, 8] > 4) #' plotMotifHeatmaps(se[i, ], which.plots = "pearsonResid", #' width = 2, show_seqlogo = TRUE) #' #' @importFrom methods is #' @importFrom stats hclust dist quantile #' @importFrom TFBSTools Matrix #' @importFrom grDevices colorRampPalette #' @importFrom S4Vectors metadata #' @importFrom SummarizedExperiment assayNames assay rowData #' @importFrom ComplexHeatmap HeatmapAnnotation Heatmap add_heatmap #' @importFrom grid unit #' @importFrom circlize colorRamp2 #' #' @export plotMotifHeatmaps <- function(x, which.plots = c("negLog10P", "pearsonResid", "negLog10Padj", "log2enr"), width = 4, col.enr = c("#053061","#2166AC","#4393C3","#92C5DE", "#D1E5F0","#F7F7F7","#FDDBC7","#F4A582", "#D6604D","#B2182B","#67001F"), col.sig = c("#F0F0F0","#D9D9D9","#BDBDBD","#969696", "#737373","#525252","#252525","#000000"), col.gc = c("#F7FCF5","#E5F5E0","#C7E9C0","#A1D99B", "#74C476","#41AB5D","#238B45","#006D2C", "#00441B"), maxEnr = NULL, maxSig = NULL, highlight = NULL, cluster = FALSE, show_dendrogram = FALSE, show_motif_GC = FALSE, show_seqlogo = FALSE, width.seqlogo = 1.5, use_raster = FALSE, na_col = "white", ...) { stopifnot(exprs = { is(x, "SummarizedExperiment") all(which.plots %in% assayNames(x)) "bins" %in% names(metadata(x)) (!show_motif_GC || "motif.percentGC" %in% colnames(rowData(x))) }) b <- metadata(x)$bins .assertScalar(x = width, type = "numeric", rngExcl = c(0, Inf)) .assertScalar(x = show_dendrogram, type = "logical") .assertScalar(x = show_motif_GC, type = "logical") .assertScalar(x = show_seqlogo, type = "logical") .assertScalar(x = width.seqlogo, type = "numeric", rngExcl = c(0, Inf)) .assertScalar(x = use_raster, type = "logical") .assertScalar(x = na_col, type = "character") stopifnot(exprs = { ncol(x) == nlevels(b) all(which.plots %in% c("negLog10P", "negLog10Padj", "pearsonResid", "log2enr")) is.null(highlight) || (is.logical(highlight) && length(highlight) == nrow(x)) }) bincols <- attr(getColsByBin(b), "cols") if (identical(cluster, TRUE)) { clAssayName <- "pearsonResid" clAssay <- assay(x, clAssayName) allNA <- rowSums(is.na(clAssay)) == ncol(clAssay) if (any(allNA)) { warning("removing motifs without finite values in '", clAssayName, "': ", paste(rownames(clAssay)[allNA], collapse = ", ")) x <- x[!allNA, ] clAssay <- clAssay[!allNA, ] } clres <- hclust(dist(clAssay)) } else if (identical(cluster, FALSE)) { clres <- FALSE } else if (is(cluster, "hclust")) { clres <- cluster } else { stop("'cluster' must be either TRUE, FALSE or an hclust-object.") } hmBin <- HeatmapAnnotation(df = data.frame(bin = colnames(x)), name = "bin", col = list(bin = bincols), show_annotation_name = FALSE, which = "column", width = unit(width,"inch"), annotation_height = unit(width / 16, "inch"), show_legend = FALSE) tmp <- matrix(if (!is.null(highlight)) as.character(highlight) else rep(NA, nrow(x)), ncol = 1, dimnames = list(unname(rowData(x)$motif.name), NULL)) hmSeqlogo <- NULL if (show_seqlogo) { pfms <- rowData(x)$motif.pfm maxwidth <- max(vapply(TFBSTools::Matrix(pfms), ncol, 0L)) grobL <- lapply(pfms, seqLogoGrob, xmax = maxwidth, xjust = "center") hmSeqlogo <- HeatmapAnnotation( logo = anno_seqlogo(grobL = grobL, which = "row", space = unit(0.5, "mm"), width = unit(width.seqlogo, "inch")), show_legend = FALSE, show_annotation_name = FALSE, which = "row") } hmMotifs <- Heatmap(matrix = tmp, name = "names", width = unit(if (!is.null(highlight)) .2 else 0, "inch"), na_col = NA, col = c("TRUE" = "green3", "FALSE" = "white"), cluster_rows = clres, show_row_dend = show_dendrogram, cluster_columns = FALSE, show_row_names = TRUE, row_names_side = "left", show_column_names = FALSE, show_heatmap_legend = FALSE, left_annotation = hmSeqlogo) assayNameMap1 <- c(negLog10P = "P value", negLog10Padj = "adj. P value", pearsonResid = "Pearson residual", log2enr = "log2 enrichment") assayNameMap2 <- c(negLog10P = "P value (-log10)", negLog10Padj = "adj. P value (-log10)", pearsonResid = "Pearson residual (o-e)/sqrt(e)", log2enr = "enrichment (log2)") L <- list(labels = hmMotifs) if (show_motif_GC) { tmp <- as.matrix(rowData(x)[, "motif.percentGC", drop = FALSE]) hmPercentGC <- Heatmap(matrix = tmp, name = "Percent G+C", width = unit(0.2, "inch"), na_col = NA, col = colorRamp2(breaks = c(0, seq(20, 80, length.out = 254), 100), colors = colorRampPalette(col.gc)(256)), cluster_rows = FALSE, cluster_columns = FALSE, show_row_names = FALSE, show_column_names = FALSE, show_heatmap_legend = TRUE, heatmap_legend_param = list(color_bar = "continuous"), use_raster = use_raster) L <- c(L, list("percentGC" = hmPercentGC)) } ret <- c(L, lapply(which.plots, function(w) { dat <- assay(x, w) if ((w == "pearsonResid") | (w == "log2enr")) { rng <- c(-1, 1) * if (is.null(maxEnr)) quantile(abs(dat), .995, na.rm = TRUE) else maxEnr cols <- col.enr } else { rng <- c(0, if (is.null(maxSig)) quantile(dat, .995, na.rm = TRUE) else maxSig) cols <- col.sig } Heatmap(matrix = dat, name = assayNameMap1[w], width = unit(width,"inch"), column_title = assayNameMap2[w], col = colorRamp2(breaks = seq(rng[1], rng[2], length.out = 256), colors = colorRampPalette(cols)(256)), cluster_rows = FALSE, cluster_columns = FALSE, show_row_names = FALSE, show_column_names = FALSE, ##column_names_side = "bottom", column_names_max_height = unit(1.5,"inch"), top_annotation = hmBin, show_heatmap_legend = TRUE, heatmap_legend_param = list(color_bar = "continuous"), use_raster = use_raster, na_col = na_col, ...) })) names(ret)[seq(length(ret) - length(which.plots) + 1L, length(ret))] <- which.plots show(Reduce(ComplexHeatmap::add_heatmap, ret)) invisible(ret) } #' @title Plot Stability Paths #' #' @description Plot the stability paths of each variable (predictor), showing the selection probability #' as a function of the regularization step. #' #' @param se the \code{SummarizedExperiment} object resulting from stability selection, #' by running \code{\link[monaLisa]{randLassoStabSel}}. #' @param selProbMin A numerical scalar in [0,1]. Predictors with a selection #' probability greater than \code{selProbMin} are shown as colored lines. The #' color is defined by the \code{col} argument. #' @param col color of the selected predictors. #' @param lwd line width (default = 1). #' @param lty line type (default = 1). #' @param ylim limits for y-axis (default = c(0,1.1)). #' @param ... additional parameters to pass on to \code{matplot}. #' #' @return plot of stability paths. #' #' @seealso \code{\link[stabs]{stabsel}} and \code{\link[graphics]{matplot}} #' #' @importFrom SummarizedExperiment assay rowData colData #' @importFrom graphics matplot #' #' @export plotStabilityPaths <- function(se, selProbMin = metadata(se)$stabsel.params.cutoff, col = "cadetblue", lwd = 1, lty = 1, ylim = c(0, 1.1), ...) { # checks if (!is(se, "SummarizedExperiment")) { stop("'se' must be a SummarizedExperiment") } # set plot parameters mat <- as.matrix(colData(se)) mat <- t(mat[, grep(pattern = "^regStep", x = colnames(mat))]) cols <- rep("black", ncol(mat)) sel <- se$selProb > selProbMin cols[sel] <- col # plot stability paths graphics::matplot(mat, col = cols, type = "l", lty = lty, ylab = "Selection Probability", xlab = "Regularization Step", ylim = ylim, lwd = lwd, ...) abline(h = selProbMin, lty = 5, col = "red", lwd = lwd) legend("topleft", legend = c("not selected", "selected", "selProbMin"), col = c("black", col, "red"), lty = c(1, 1, 5), bty = "n", lwd = lwd) # return TRUE invisible(TRUE) } #' @title Plot selection probabilities of predictors #' #' @description This function plots the selection probabilities of predictors #' (for example the selected motifs), optionally multiplied with either +1 or #' -1 to give a sense of both the strength and the directionality of the #' associated effects. The directionality is estimated from the sign of the #' correlation coefficient between each predictor and the response vector. #' #' @param se The \code{SummarizedExperiment} object with the results from #' stability selection (typically returned by \code{\link{randLassoStabSel}}). #' @param directional A logical scalar. If \code{TRUE}, selection probabilities #' are plotted with the sign of the marginal correlation between a predictor #' and the response. #' @param selProbMin A numerical scalar in [0,1]. Predictors with a selection #' probability greater than \code{selProbMin} are shown as colored bars. The #' color is defined by \code{col[1]}. By default, \code{selProbMin} is #' extracted from the parameters stored in \code{se}. #' @param selProbMinPlot A numerical scalar in [0,1] less than \code{selProbMin}. #' Predictors with a selection probability greater than \code{selProbMinPlot} #' but less than \code{selProbMin} are shown as bars with color \code{col[2]}. #' \code{selProbMinPlot} is useful to include additional predictors in the plot #' that were not selected according to \code{selProbMin} but may be close to #' that cutoff. Setting \code{selProbMinPlot = 0} will create a plot including #' all predictors. #' @param showSelProbMin A logical scalar. If \code{TRUE}, the value of #' \code{selProbMin} is shown by a horizontal dashed line of color \code{col[3]}. #' @param col A color vector giving the three colors used for predictors with #' selection probability greater than \code{selProbMin}, additional predictors #' with selection probability greater than \code{selProbMinPlot}, and the #' selection probability cutoff line. #' @param method A character scalar with the correlation method to use in the #' calculation of predictor-response marginal correlations. One of "pearson", #' "kendall" or "spearman" (see \code{\link[stats]{cor}}). #' @param ylimext A numeric scalar defining how much the y axis limits should be #' expanded beyond the plotted probabilities to allow for space for the #' bar labels. #' @param ... additional parameters passed to \code{\link[graphics]{barplot}}. #' #' @return \code{TRUE} (invisible). The function is called to create a barplot #' indicating the selection probability and optionally directionality of the #' predictors (motifs). #' #' @importFrom SummarizedExperiment rowData assay #' @importFrom S4Vectors metadata #' @importFrom stats cor #' @importFrom graphics barplot abline legend text axis #' #' @export plotSelectionProb <- function(se, directional = TRUE, selProbMin = metadata(se)$stabsel.params.cutoff, selProbMinPlot = 0.4, showSelProbMin = TRUE, col = c("cadetblue", "grey", "red"), method = c("pearson", "kendall", "spearman"), ylimext = 0.25, ...) { # checks .assertScalar(x = directional, type = "logical") .assertScalar(x = selProbMin, type = "numeric", rngIncl = c(0, 1)) .assertScalar(x = selProbMinPlot, type = "numeric", rngIncl = c(0, 1)) .assertScalar(x = showSelProbMin, type = "logical") stopifnot(exprs = { is(se, "SummarizedExperiment") selProbMin > selProbMinPlot }) .assertVector(x = col, len = 3L) method <- match.arg(method) .assertScalar(x = ylimext, type = "numeric", rngIncl = c(0, Inf)) # selection probabilities * sign(correlation to y) probs <- se$selProb cols <- ifelse(probs > selProbMin, col[1], col[2]) if (directional) { corcoef <- as.vector(cor(x = SummarizedExperiment::rowData(se)$y, y = SummarizedExperiment::assay(se, "x"), method = method)) probs <- probs * sign(corcoef) } # kept and ordered keep <- which(abs(probs) >= selProbMinPlot) keep <- keep[order(probs[keep], decreasing = TRUE)] cols <- cols[keep] predNames <- colnames(se)[keep] probs <- probs[keep] up <- probs > 0 # plot if (any(keep)) { bar <- graphics::barplot(probs, col = cols, border = NA, ylab = ifelse(directional, "Directional selection probability", "Selection probability"), names.arg = NA, axes = FALSE, ylim = c(min(probs) - ylimext, max(probs) + ylimext), ...) ys <- pretty(x = c(0, probs)) graphics::axis(side = 2, at = ys) if (showSelProbMin) { hval <- if (directional) c(-1, 1) * selProbMin else selProbMin graphics::abline(h = hval, lty = 5, col = col[3]) } graphics::legend("topright", bty = "n", fill = col[seq_len(2)], border = NA, legend = c("selected", "not selected")) if (any(up)) { graphics::text(x = bar[up], y = probs[up] + par("cxy")[2] / 3, labels = predNames[up], col = cols[up], xpd = TRUE, srt = 90, adj = c(0, 0.5)) } if (any(!up)) { graphics::text(x = bar[!up], y = probs[!up] - par("cxy")[2] / 3, labels = predNames[!up], col = cols[!up], xpd = TRUE, srt = 90, adj = c(1, 0.5)) } } invisible(TRUE) }

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lalgp_graph.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/lalgp_graph.R \name{lalgp_graph} \alias{lalgp_graph} \title{Frequency graph} \usage{ lalgp_graph(data, x_min = NA, x_max = NA, y_max = NA, uncertainty = TRUE, uncertainty2 = TRUE, curve = TRUE, rug_plot = 1, graphic_theme = 1, grid = 1, xlab = "Age (Ma)", ylab = "frequency", grid_color = "grey30", area_color = "grey80", line_color = "black", rug_plot_color = rgb(0, 0, 0, alpha = 0.6), hist = FALSE, hist_bin = as.integer(50), hist_height = 0.2, hist_color = rgb(1, 0.62, 0, alpha = 0.6), analyze = FALSE) } \arguments{ \item{data}{list data of the result of functions; \code{\link{lalgp}} or \code{\link{manual_lalgp}}.} \item{x_min}{numeric; the smallest end point of x axis.} \item{x_max}{numeric; the largest end point of x axis.} \item{y_max}{numeric; the largest end point of y axis.} \item{uncertainty}{logical; if \code{TRUE} (default), drawing 95 percent confident interval of the estimate at certain age.} \item{uncertainty2}{logical; if \code{TRUE} (default), drawing 90 percent confident interval of the estimate line.} \item{rug_plot}{apply data points' shape of rug plot. \itemize{ \item{0: no plot} \item{1: thin line plot (default)} \item{2: thin square plot. This plot should be used for discrete data, such as integer data.} \item{3: thick square plot. Also used for discrete data} }} \item{graphic_theme}{one of either: \itemize{ \item{1: default theme, with y axis label, graph frame, and grid.} \item{2: simple theme, only with x axis in plot area.} }} \item{grid}{one of either (only used if \code{graphic_theme = 1}): \itemize{ \item{0: no grid} \item{1: dotted grid (default)} }} \item{xlab}{a text style title for the x axis. Defaults to "Age (Ma)".} \item{ylab}{a text style title for the y axis, only used if \code{graphic_theme = 1}.} \item{grid_color}{the color to be used for grid, only used if \code{graphic_theme = 1}. Defaults to "grey30".} \item{area_color}{the color to be used for 95 percent confident interval area, only used if \code{uncertainty = 1}. Defaults to "grey80".} \item{line_color}{the color to be used for estimate line. Defaults to "black".} \item{rug_plot_color}{the color to be used for rug plot. Defaults to rgb(0, 0, 0, alpha = .6).} \item{hist}{logical; if \code{TRUE}, drawing histogram in plot area. Defaults to \code{FALSE}.} \item{hist_bin}{integer, larger than 0. Number of histogram bins. Only used if \code{hist = TRUE}.} \item{hist_height}{numeric, between 0 and 1. If \code{hist_height = 1}, then the height of largest bin is that of plot area. Generally, the height is \code{hist_height} times. Only used if \code{hist = TRUE}.} \item{hist_color}{the color to be used for histgrams. Defaults to rgb(1, 0.62, 0, alpha = .6). Only used if \code{hist = TRUE}.} \item{analyze}{logical; if \code{TRUE}, drawing the peak x-coordinate, largest and smallest data, and number of data. Defaults to \code{FALSE}.} } \value{ data.frame of the x-coordinats and height of peaks. Only returned if \code{analyze =TRUE}. } \description{ Drawing frequency graph (density plot) and analyzing the out put of \code{\link{lalgp}} or \code{\link{manual_lalgp}}. } \examples{ d <- Osayama e <- lalgp(d) lalgp_graph(e) }

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/Projects/DMP/heritability_analysis_v2/DMP_heritability_v10_mockdata_plotresults.R

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GRONINGEN-MICROBIOME-CENTRE/Groningen-Microbiome

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DMP_heritability_v10_mockdata_plotresults.R

# ======================================================================== # By: R.Gacesa, Weersma Group, UMCG (2020) # # Script plots results of heritability analysis # (Panels A & B in Figure 2 in main DMP manuscript ) # NOTE: These codes are implemented for mock data heritability models # constructed using # DMP_heritability_v10_mockdata.taxa.R # DMP_heritability_v10_mockdata.pwys.R # and processed using # DMP_heritability_v10_mockdata_collect.R # # ========================================================================= # function that parses confidence intervals from heritability results table parseCIrange <- function(inDFs=inDFs,varN,toget="range") { ret = c() for (cc in c(1:nrow(inDFs))) { if (!is.na(inDFs[[varN]][cc]) & ! grepl('Inf',inDFs[[varN]][cc])) { ccc <- unlist(strsplit(inDFs[[varN]][cc],'-') ) if (toget=="range") { ret <- c(ret,abs(as.numeric(ccc[2])-as.numeric(ccc[1])) ) } else if (toget=="high") { ret <- c(ret,as.numeric(ccc[2])) } else if (toget=="low") { ret <- c(ret,as.numeric(ccc[1])) } } else { ret <- c(ret,0) } } ret } library(ggplot2) library(tidyr) # ====================== # set working directory: NOTE this has to be set to appropriate path! # example: #setwd('D:/Vbox/shared/dag/git_14_05/DMP/heritability_analysis_v2/') setwd('.') # initalize folder for storing plots if (!dir.exists('Plots')) { dir.create('Plots') } # input: heritability results (taxa) inDFm <- read.table('results_mockdata_withFDRs_and_CIs_taxa.csv',sep=',',header=T,quote = '"',fill = T,stringsAsFactors = F) # make plots (taxa) inDFm$FTYPE <- NA inDFm$FTYPE[grep('^s_',inDFm$Trait.short)] <-"Taxon.S" inDFm$FTYPE[grep('^g_',inDFm$Trait.short)] <-"Taxon.G" inDFm$FTYPE[grep('^f_',inDFm$Trait.short)] <-"Taxon.F" inDFm$FTYPE[grep('^c_',inDFm$Trait.short)] <-"Taxon.C" inDFm$FTYPE[grep('^p_',inDFm$Trait.short)] <-"Taxon.P" inDFm$FTYPE[grep('^k_',inDFm$Trait.short)] <-"Taxon.K" inDFm$FTYPE[grep('^o_',inDFm$Trait.short)] <-"Taxon.O" # debug output #print(inDFm$FEATURE[is.na(inDFm$FTYPE)]) # select which taxonomic levels to plot fTypes <- c("Taxon.S","Taxon.G","Taxon.F","Taxon.C","Taxon.O","Taxon.P") # color-blind color palette cbPalette <- c("#E69F00","#999999", "#009E73","#56B4E9")#, "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # iterate over taxonomy levels for (oneType in fTypes) { #debug #oneType <- "Taxon.S" inDFs <- inDFm[inDFm$FTYPE==oneType,] inDFs$Taxon <- factor(as.character(inDFs$Trait.short),levels=inDFs$Trait.short[order(inDFs$VE_ID)]) inDFs <- inDFs[order(inDFs$VE_ID,decreasing = T),] inDFs$LBL = "" inDFs$LBL[inDFs$SW_PV_ID <= 0.05] <- "*" inDFs$LBL[inDFs$SW_FDR_ID <= 0.1] <- "**" inDFs$H2 <- inDFs$VE_ID inDFs$VE_Cohousing <- inDFs$VE_COHOUSING.ID_DMP inDFs$VE_Family <- inDFs$VE_famID inDFs$VE_Environment <- inDFs$VE_Residual inDFs$VE_ID[is.na(inDFs$VE_ID)] <- 0.0 if (grepl('Taxon',oneType)) { inDFs$Taxon_Shortname <- as.character(inDFs$Taxon) inDFs$Taxon_Shortname <- factor(as.character(inDFs$Taxon_Shortname),levels=inDFs$Taxon_Shortname[order(inDFs$VE_ID,decreasing = F)]) } # parse confidence intervals inDFs$CI_ID_low <- parseCIrange(inDFs,"CI_ID","low") inDFs$CI_ID_low[inDFs$CI_ID_low < 0] <- 0 inDFs$CI_ID_high <- parseCIrange(inDFs,"CI_ID","high") inDFs$CI_ID_high[inDFs$CI_ID_high > 1] <- 1 inDFtoPlot <- inDFs[,c("Taxon_Shortname","LBL","H2","VE_Cohousing","VE_Family","VE_Environment","CI_ID_low","CI_ID_high")] #inDFtoPlot$VE_Environment <- 1-inDFtoPlot$H2-inDFtoPlot$VE_Cohousing-inDFtoPlot$VE_Family #inDFtoPlot$SEH2 <- inDFs$SEH2 inDFtoPlot$LBL <- inDFs$LBL inDFtoPlotL <- gather(inDFtoPlot,"Var.Exp","Var.Exp.NR", H2:VE_Environment,factor_key = T) inDFtoPlotL$Var.Exp <- as.character(inDFtoPlotL$Var.Exp) inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "H2"] <- "Additive genetics" inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "VE_Environment"] <- "Environment" inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "VE_Cohousing"] <- "Cohousing" inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "VE_Family"] <- "Family" inDFtoPlotL$Var.Exp <- factor(as.character(inDFtoPlotL$Var.Exp),level = c("Environment","Cohousing","Family","Additive genetics")) inDFtoPlotL$Var.Exp <- factor(as.character(inDFtoPlotL$Var.Exp),level = c("Cohousing","Family","Environment","Additive genetics")) g <- ggplot(inDFtoPlotL,aes(x=Taxon_Shortname,y=Var.Exp.NR,fill=Var.Exp)) + scale_fill_manual(values = cbPalette) + geom_col(col="black", width=1,size=0.75) + theme(axis.text.x = element_text(angle = 0,face="bold")) + ylim(-0.01,1.01) + theme(axis.text.y = element_text(face="bold")) + geom_errorbar(ymin=inDFtoPlotL$CI_ID_low,ymax=inDFtoPlotL$CI_ID_high,width=0.25, linetype='solid') + geom_text(data = inDFtoPlotL, aes(x = Taxon_Shortname, y=CI_ID_high, label = format(LBL, nsmall = 0, digits=1, scientific = FALSE)), color="black", vjust=+0.75, angle = 0, hjust=-1,size=6) + ylim(-0.01,1.01) + ylab('Microbiome variance explained') + xlab('') + theme(legend.position="bottom") + theme(text = element_text(size = 14)) + coord_flip() print(g) ggsave(paste0('Plots/mockdata_heritability_',oneType,'.png'),height = 1.25+8/50*nrow(inDFs),width = 9,limitsize = F) # smaller plot if (nrow(inDFs) > 20) { topN <- 20 topFeatures <- inDFs[order(inDFs$VE_ID,decreasing = T),]$Trait.short[1:topN] inDFtoPlotLs <- inDFtoPlotL[inDFtoPlotL$Taxon_Shortname %in% topFeatures,] g <- ggplot(inDFtoPlotLs,aes(x=Taxon_Shortname,y=Var.Exp.NR,fill=Var.Exp)) + scale_fill_manual(values = cbPalette) + geom_col(col="black", width=1,size=0.75) + theme(axis.text.x = element_text(angle = 0,face="bold")) + ylim(-0.01,1.01) + theme(axis.text.y = element_text(face="bold")) + geom_errorbar(ymin=inDFtoPlotLs$CI_ID_low,ymax=inDFtoPlotLs$CI_ID_high,width=0.25, linetype='solid') + geom_text(data = inDFtoPlotLs, aes(x = Taxon_Shortname, y=CI_ID_high, label = format(LBL, nsmall = 0, digits=1, scientific = FALSE)), color="black", vjust=+0.75, angle = 0, hjust=-1,size=6) + ylim(-0.01,1.01) + ylab('Microbiome variance explained') + xlab('') + theme(legend.position="bottom") + theme(text = element_text(size = 14)) + coord_flip() print(g) ggsave(paste0('Plots/mockdata_heritability_',oneType,'_topsignals.png'),height = 1.25+8/50*topN,width = 9,limitsize = F) } } # ================================ # PATHWAY PLOTS # ================================ # input: heritability results (taxa) inDFm <- read.table('results_mockdata_withFDRs_and_CIs_pwys.csv',sep=',',header=T,quote = '"',fill = T,stringsAsFactors = F) # make plots (pwys) inDFm$FTYPE <- "PWYS" cbPalette <- c("#E69F00","#999999", "#009E73","#56B4E9")#, "#F0E442", "#0072B2", "#D55E00", "#CC79A7") oneType <- "PWYS" inDFs <- inDFm[inDFm$FTYPE==oneType,] inDFs$VE_ID[is.na(inDFs$VE_ID)] <- 0.0 inDFs$Taxon <- factor(as.character(inDFs$Trait),levels=inDFs$Trait[order(inDFs$VE_ID)]) inDFs <- inDFs[order(inDFs$VE_ID),] inDFs$LBL = "" inDFs$LBL[inDFs$SW_PV_ID <= 0.05] <- "*" inDFs$LBL[inDFs$SW_FDR_ID <= 0.1] <- "**" inDFs$H2 <- inDFs$VE_ID inDFs$VE_Cohousing <- inDFs$VE_COHOUSING.ID_DMP inDFs$VE_Family <- inDFs$VE_famID inDFs$VE_Environment <- inDFs$VE_Residual inDFs$Taxon_Shortname <- as.character(inDFs$Taxon) inDFs$Taxon_Shortname <- factor(as.character(inDFs$Taxon_Shortname),levels=inDFs$Taxon_Shortname[order(inDFs$VE_ID)]) inDFs$CI_ID_low <- parseCIrange(inDFs,varN = "CI_ID","low") inDFs$CI_ID_low[inDFs$CI_ID_low < 0] <- 0 inDFs$CI_ID_high <- parseCIrange(inDFs,"CI_ID","high") inDFs$CI_ID_high[inDFs$CI_ID_high > 1] <- 1 inDFtoPlot <- inDFs[,c("Taxon_Shortname","LBL","H2","VE_Cohousing","VE_Family","VE_Environment","CI_ID_low","CI_ID_high")] inDFtoPlot$LBL <- inDFs$LBL inDFtoPlotL <- gather(inDFtoPlot,"Var.Exp","Var.Exp.NR", H2:VE_Environment,factor_key = T) inDFtoPlotL$Var.Exp <- as.character(inDFtoPlotL$Var.Exp) inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "H2"] <- "Additive genetics" inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "VE_Environment"] <- "Environment" inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "VE_Cohousing"] <- "Cohousing" inDFtoPlotL$Var.Exp[inDFtoPlotL$Var.Exp == "VE_Family"] <- "Family" inDFtoPlotL$Var.Exp <- factor(as.character(inDFtoPlotL$Var.Exp),level = c("Environment","Cohousing","Family","Additive genetics")) inDFtoPlotL$Var.Exp <- factor(as.character(inDFtoPlotL$Var.Exp),level = c("Cohousing","Family","Environment","Additive genetics")) g <- ggplot(inDFtoPlotL,aes(x=Taxon_Shortname,y=Var.Exp.NR,fill=Var.Exp)) + scale_fill_manual(values = cbPalette) + geom_col(col="black", width=1,size=0.75) + theme(axis.text.x = element_text(angle = 0,face="bold")) + ylim(-0.01,1.01) + theme(axis.text.y = element_text(face="bold")) + geom_errorbar(ymin=inDFtoPlotL$CI_ID_low,ymax=inDFtoPlotL$CI_ID_high,width=0.25, linetype='solid') + geom_text(data = inDFtoPlotL, aes(x = Taxon_Shortname, y=CI_ID_high, label = format(LBL, nsmall = 0, digits=1, scientific = FALSE)), color="black", vjust=+0.75, angle = 0, hjust=-1,size=6) + ylim(-0.01,1.01) + ylab('Microbiome variance explained') + xlab('') + theme(legend.position="bottom") + theme(text = element_text(size = 14)) + coord_flip() print(g) ggsave(paste0('Plots/mockdata_heritability_PWYS.png'),height = 1.25+8/50*nrow(inDFs),width = 20,limitsize = F) # smaller plot (top 20) topN <- 20 topFeatures <- inDFm[order(inDFm$VE_ID,decreasing = T),]$Trait[1:topN] inDFtoPlotLs <- inDFtoPlotL[inDFtoPlotL$Taxon_Shortname %in% topFeatures,] g <- ggplot(inDFtoPlotLs,aes(x=Taxon_Shortname,y=Var.Exp.NR,fill=Var.Exp)) + scale_fill_manual(values = cbPalette) + geom_col(col="black", width=1,size=0.75) + theme(axis.text.x = element_text(angle = 0,face="bold")) + ylim(-0.01,1.01) + theme(axis.text.y = element_text(face="bold")) + geom_errorbar(ymin=inDFtoPlotLs$CI_ID_low,ymax=inDFtoPlotLs$CI_ID_high,width=0.25, linetype='solid') + geom_text(data = inDFtoPlotLs, aes(x = Taxon_Shortname, y=CI_ID_high, label = format(LBL, nsmall = 0, digits=1, scientific = FALSE)), color="black", vjust=+0.75, angle = 0, hjust=-1,size=6) + ylim(-0.01,1.01) + ylab('Microbiome variance explained') + xlab('') + theme(legend.position="bottom") + theme(text = element_text(size = 14)) + coord_flip() print(g) ggsave(paste0('Plots/mockdata_heritability_PWYS_top20.png'),height = 1.25+8/50*topN,width = 20,limitsize = F)

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/cov_vsvb.R

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Sutanoy/Covariate-dependent-Graph-Estimation

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cov_vsvb.R

##The core function that calculates the variational parameter updates and returns the final variational estimates ##for a single regression. So in the arguments, y plays the role of the response, i.e the j th variable whose ##conditional distribution given the remaining variables is being calculated. ##This calls the ELBO_calculator function ##X: the data matrix except the j th variable ##XtX is x transpose times x. ##DXtX:diagonal elements of XtX ##Diff_mat: XtX-diag(DXtX) ##Xty: x transpose times y ##sigmasq: variance of response given the parameters (homoscedastic part, actual variance sigma_sq/w_i) ##sigmabeta_sq: prior variance of coefficient parameter ##true_pi: estimate of spike and slab mixture proportion. cov_vsvb= function(y,X,Z,XtX,DXtX,Diff_mat,Xty,sigmasq,sigmabeta_sq,true_pi){ thres=1e-7 tol=1e-9 msg <- function(s, ...) { time <- format(Sys.time(), "%X") cat(sprintf("%s %s\n", time, s)) } change_alpha <- rep(0.001,n*p) #alpha_new - alpha_int max_iter <- 100 iter=1 Mu_vec=matrix(rep(mu,n),n*p,1) while(sqrt(sum(change_alpha^2))>tol & iter<max_iter){#The max_iter controls the max number of iterations until convergence alpha_int=alpha ##Initialization of inclusion probability parameter. alpha_mat=matrix(alpha,n,p,byrow=TRUE) alpha_vec=matrix(alpha,n*p,1,byrow=TRUE) for(i in 1:n){ S_sq[i,]=sigmasq*(t(DXtX_Big_ind)%*%D_long[,i] + 1/sigmabeta_sq)^(-1) ##variance parameter } S_sq_vec=matrix(t(S_sq),n*p,1) for(i in 1:n){ y_XW=y_long_vec*X_vec*D_long[,i] y_XW_mat=matrix(y_XW,n,p,byrow=TRUE) X_mu_alpha=X_vec*Mu_vec*alpha_vec xmualpha_mat=t(matrix(X_mu_alpha,p,n))%*%(matrix(1,p,p)-diag(rep(1,p))) XW_mat=matrix(X_vec*D_long[,i],n,p,byrow=TRUE)*xmualpha_mat mu_mat[i,]=(t(y_XW_mat)%*%matrix(1,n,1)-(t(XW_mat)%*%matrix(1,n,1)))*(S_sq[i,]/sigmasq) ### ### CAVI updation of mean variational parameter mu } Mu_vec=matrix(t(mu_mat),n*p,1) vec_1=log(true_pi/(1-true_pi)) ##term 1 of the update of inclusion probability alpha vec_2=as.matrix(0.5*log(S_sq_vec/(sigmasq*sigmabeta_sq))) ##term 2 of update of alpha vec_3=as.matrix(Mu_vec^2/(2*S_sq_vec)) ##term 3 of update of alpha # (vec_1+vec_2+vec_3)[1:p] unlogitalpha=vec_1+vec_2+vec_3 #Sum of 3 terms for alpha update # thres=10^{-9} lthres=logit(thres) uthres=logit(1-thres) indlarge=which(unlogitalpha > uthres) indsmall=which(unlogitalpha < lthres) unlogitalpha[indlarge]<-uthres unlogitalpha[indsmall]<-lthres alpha[which(unlogitalpha>9)]=1 #thresholding very large values to 1 for computational stability alpha[which(unlogitalpha<=9)]=1/(1+ exp(-unlogitalpha[which(unlogitalpha<=9)])) ### ### CAVI updation of variational parameter alpha e=0 for(i in 1:n){ ## calculates ELBO for the j th variable by adding the contribution of the parameter ##corresponding to every individual in study. i th iteration takes the contribution of the variational ##parameters corresponding to the i th individual in study, but the information is borrowed from ## all the n individuals depending on the weights coded in D[,i] e=e+ELBO_calculator(y,X_mat,S_sq[i,],mu_mat[i,],alpha_mat[i,],sigmasq, sigmabeta_sq, true_pi, D[,i],n,p ) } ELBO_LB= e alpha_new <- alpha change_alpha <-alpha_new - alpha_int ELBO_LBit[iter]=ELBO_LB iter=iter+1 } ELBO_LBit=ELBO_LBit[1:(iter-1)] list(var.alpha=alpha, var.mu=mu_mat, var.S_sq=S_sq, var.elbo=ELBO_LB,var.elboit=ELBO_LBit) }

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Dados da Spera por municipio.R

## corrige eerro em ler polygon dataframes # muda acentos para pontos Sys.setlocale(category = "LC_ALL", locale = "C") # carregar library library(ggmap) library(raster) library(maptools) library(spatial.tools) library(snow) # Diretorio do shape setwd("/home/eduardo/Documents/public-ipam/data_geo/shapes/municipios_IBGE/municipios_dissolve") dir() # shape dos municipios mun2001 <-readOGR(".",layer="Mtopiba_2001") mun2001 #P4S <- CRS("+proj=longlat +datum=WGS84") #utm <-CRS("+proj=utm +zone=22 +south +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 ") #tang_plano<-spTransform(tang,utm) #plot(tang_plano) # diretorio dos dados da Spera setwd("D:/IPAM/Dados coletados/Dados Spera/Mato Grosso (Cerrado)") pasta="D:/IPAM/Dados coletados/Dados Spera/Mato Grosso (Cerrado)/Dados Spera por muncipio" sp2001=raster("GY2001.tif") tang2001<-crop(sp2001,tang_plano) tang2001_mask<-mask(tang2001,tang_plano) #reclassifying sp2001 to get just soybean cover types table(values(sp2001_mask)) #conferindo tipos de cobertura presentes no raster tab<-c(0,1,2,4,5,6,11,13,99,NA,1,NA,NA,5,6,NA,NA,NA);tab<-matrix(tab,9,2);tab sp2001_soja<-reclassify(tang2001_mask,tab) #another way to reclass #ff=function(x){x[x==0]=NA;x[x==2]=NA;x[x==4]=NA;x[x==11]=NA;x[x==13]=NA;x[x==99]=NA;return(x)} #soja=calc(sp2001,fun=ff) x11() plot(sp2001_soja) start.time <- Sys.time() beginCluster( detectCores() ) #use all but one core tang_plano$SoyArea2001 <- extract(sp2001_soja, tang_plano, fun = sum, na.rm = TRUE,cellnumbers=TRUE) endCluster() #end.time <- Sys.time() #time.taken <- end.time - start.time #time.taken head(mumt@data) arquivo = foreach(i = 1:100 )%dopar%{ x.temp = subset(shape, XX == i) extract(x.temp, RASTER) } ######################################### ##Quantas c?lulas de soja existem em cada talh?o da Tanguro entre os anos de 2001 a 2013? ######################################### # diretorio dos dados da Spera setwd("D:/IPAM/Dados coletados/Dados Spera/Mato Grosso (Cerrado)/only_tif") #pasta="D:/IPAM/Dados coletados/Dados Spera/Mato Grosso (Cerrado)/only_tif" x1=list.files(,pattern="*.tif");x1 spera = stack(x1) tang_sp<-crop(spera,tang_plano) tang_sp_mask<-mask(tang_sp,tang_plano) #reclassifying sp2001 to get just soybean cover types table(values(tang_sp_mask)) #conferindo tipos de cobertura presentes no raster tab<-c(0,1,2,4,5,6,11,13,99,NA,1,NA,NA,5,6,NA,NA,NA);tab<-matrix(tab,9,2);tab tang_soja<-reclassify(tang_sp_mask,tab) #another way to reclass #ff=function(x){x[x==0]=NA;x[x==2]=NA;x[x==4]=NA;x[x==11]=NA;x[x==13]=NA;x[x==99]=NA;return(x)} #soja=calc(sp2001,fun=ff) x11() plot(tang_soja) start.time <- Sys.time() beginCluster( detectCores() ) #use all but one core tang_plano$SoyArea <- extract(tang_soja, tang_plano, fun = sum, na.rm = TRUE,cellnumbers=TRUE) endCluster() barplot(tang_plano@data$SoyArea) pixels<-data.frame(tang_plano@data$SoyArea) area<-pixels*53.4 barplot(as.matrix(area))

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/data/genthat_extracted_code/BNPdensity/examples/enzyme.Rd.R

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surayaaramli/typeRrh

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enzyme.Rd.R

library(BNPdensity) ### Name: enzyme ### Title: Enzyme Dataset ### Aliases: enzyme ### Keywords: datasets ### ** Examples data(enzyme) hist(enzyme)

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/man/sfilter.Rd

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bmcclintock/crwHMM

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sfilter.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sfilter.R \name{sfilter} \alias{sfilter} \title{fit the state-space model to \code{prefilter}-ed data} \usage{ sfilter(x, model = c("rw", "crw"), time.step = 6, parameters = NULL, fit.to.subset = TRUE, optim = c("nlminb", "optim"), verbose = FALSE, inner.control = NULL) } \arguments{ \item{x}{Argos data passed through prefilter()} \item{model}{specify which SSM is to be fit: "rw" or "crw"} \item{time.step}{the regular time interval, in hours, to predict to. Alternatively, a vector of prediction times, possibly not regular, must be specified as a data.frame with id and POSIXt dates.} \item{parameters}{a list of initial values for all model parameters and unobserved states, default is to let sfilter specifiy these. Only play with this if you know what you are doing...} \item{fit.to.subset}{fit the SSM to the data subset determined by prefilter (default is TRUE)} \item{optim}{numerical optimizer to be used ("nlminb" or "optim")} \item{verbose}{report progress during minimization} \item{inner.control}{list of control settings for the inner optimization (see ?TMB::MakeADFUN for additional details)} } \description{ generates initial values for model parameters and unobserved states; structures data and initial values for C++ \code{TMB} template; fits state-space model; minimises the joint log-likelihood via the selected optimizer (\code{nlminb} or \code{optim}); structures and passes output object to \code{fit_ssm} } \details{ called by \code{fit_ssm}. \code{sfilter} can only fit to an individual track, use \code{fit_ssm} to fit to multiple tracks (see ?fit_ssm). } \examples{ data(ellie) pf <- prefilter(ellie, vmax=10, ang=c(15,25), min.dt=120) out <- sfilter(pf, model="rw", time.step=24) }

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/backup_code.R

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gaojingyuusa/CEM_Tool

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backup_code.R

# Test data query from data # Comparators+Target isocode <- c("CHN", "JPN", "USA", "CHL", "BRA", "RUS", "CHL", "IDN", "MYS", "MEX", "COL", "SGP","KOR") group <- c("Target", "Stuc_1","Stuc_2","Stuc_3","Aspr_1","Aspr_2","Aspr_3","High income","High income","ASEAN","ASEAN","OECD","OECD") basis <- data.frame( isocode = isocode, group = group, stringsAsFactors = F ) # Subset user defined data ## Individual comparators basis_inv <- basis[1:7,] normal_inv <- data.frame() for (i in seq_along(basis_inv$isocode)){ repl <- subset(normal_dt, ISO == basis_inv$isocode[i] & Year >=2005 & Year <= 2010) %>% mutate(identifier=paste0(basis_inv$isocode[i],"_",basis_inv$group[i])) %>% select(-Source, -ISO) normal_inv <- rbind(normal_inv,repl) } ## Aggregate 3 typologies # Define a function to do the trick typ_cal <-function(test, start, end){ typo_iso <- basis$isocode[basis$group==test] sub_tp <- subset(normal_dt, ISO %in% typo_iso & Year >=start & Year <= end) sub_tp <- aggregate(x=sub_tp$value, by=list(sub_tp$Year, sub_tp$Indicator), FUN=mean, na.rm=T) names(sub_tp) <- c("Year", "Indicator", "value") sub_tp$identifier <- test sub_tp} # run a loop to do the trick normal_typ <- data.frame() basis_typ <- unique(basis$group[8:nrow(basis)]) for (j in basis_typ){ repl <- typ_cal(j, 2005, 2010) normal_typ <- rbind(normal_typ, repl) } ## Append all data together full <- rbind(normal_inv, normal_typ) %>% spread(identifier, value) ordername <- names(full) full <- full[c("Indicator","Year", ordername[grep("Target",ordername)], ordername[grep("Stuc_1",ordername)], ordername[grep("Stuc_2",ordername)], ordername[grep("Stuc_3",ordername)], ordername[grep("Aspr_1",ordername)], ordername[grep("Aspr_2",ordername)], ordername[grep("Aspr_3",ordername)], ordername[grep(basis_typ[1],ordername)], ordername[grep(basis_typ[2],ordername)], ordername[grep(basis_typ[3],ordername)] )]

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/man/mmf.Rd

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cran/growthmodels

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2023-05-25T02:10:25.242190

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mmf.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mmf.R \name{mmf} \alias{mmf} \alias{mmf.inverse} \title{Morgan-Mercer-Flodin growth model} \usage{ mmf(t, alpha, w0, gamma, m) mmf.inverse(x, alpha, w0, gamma, m) } \arguments{ \item{t}{time} \item{alpha}{upper asymptote} \item{w0}{the value at t = 0} \item{gamma}{parameter that controls the point of inflection} \item{m}{growth rate} \item{x}{size} } \description{ Computes the Morgan-Mercer-Flodin growth model \deqn{ y(t) = \frac{(w_0 \gamma + \alpha t^m)}{\gamma} +t^m}{ y(t) = (w_0 * \gamma + \alpha * t^m) / (\gamma + t^m)} } \examples{ growth <- mmf(0:10, 10, 0.5, 4, 1) # Calculate inverse function time <- mmf.inverse(growth, 10, 0.5, 4, 1) } \references{ A. Khamiz, Z. Ismail, and A. T. Muhammad, "Nonlinear growth models for modeling oil palm yield growth," Journal of Mathematics and Statistics, vol. 1, no. 3, p. 225, 2005. } \author{ Daniel Rodriguez }