pierreqi/r_code_50k · Datasets at Hugging Face

1f70b98ac551dfc72af5bff1e81fa7686fdce681

17e0b4e4c0fddaa71ce2b137b7f59d17fa47243b

/res/ld_st_indirect_y.r

5512cf6ef41e0b2c6d979ba0e7037b9e00e7c506

[ "MIT" ]

permissive

JSpuri/EmuParadise

6f6d26c43d9dce8f05448b6c07db133d691e39b2

b8f6cf8823f8553f28dab5c6b44df20978ad6ba0

refs/heads/master

2020-06-28T18:33:56.341244

2019-11-22T22:49:53

200,309,043

0

MIT

2019-09-27T15:59:31

2019-08-02T23:26:20

C

UTF-8

R

false

1,534

r

ld_st_indirect_y.r

| pc = 0xc002 | a = 0xab | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 10110100 | | pc = 0xc004 | a = 0xab | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 10110100 | MEM[0x00ff] = 0xab | | pc = 0xc006 | a = 0x07 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | | pc = 0xc008 | a = 0x07 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | MEM[0x0000] = 0x07 | | pc = 0xc00a | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110110 | | pc = 0xc00c | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110110 | | pc = 0xc00e | a = 0x01 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | | pc = 0xc010 | a = 0x01 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | MEM[0x07ab] = 0x01 | | pc = 0xc012 | a = 0x01 | x = 0x00 | y = 0x01 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | | pc = 0xc014 | a = 0xf2 | x = 0x00 | y = 0x01 | sp = 0x01fd | p[NV-BDIZC] = 10110100 | | pc = 0xc016 | a = 0xf2 | x = 0x00 | y = 0x01 | sp = 0x01fd | p[NV-BDIZC] = 10110100 | MEM[0x07ac] = 0xf2 | | pc = 0xc018 | a = 0x00 | x = 0x00 | y = 0x01 | sp = 0x01fd | p[NV-BDIZC] = 00110110 | | pc = 0xc01a | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110110 | | pc = 0xc01c | a = 0x01 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | MEM[0x07ab] = 0x01 | | pc = 0xc01e | a = 0x01 | x = 0x00 | y = 0x01 | sp = 0x01fd | p[NV-BDIZC] = 00110100 | | pc = 0xc020 | a = 0xf2 | x = 0x00 | y = 0x01 | sp = 0x01fd | p[NV-BDIZC] = 10110100 | MEM[0x07ac] = 0xf2 |

885f5c5b7232cb1f57a18de38570f57b939d3e0d

3c639ff3361293d0c1fb2cbec04680da9a271dbc

/Scripts/Gut Check.R

70abcce86598c6241c93210226b41ac23d307c76

[]

no_license

9Olive/FACES

8d2093e3b7fbf4c2a1c68e17532f810413031320

24f986d29ae47e423a8f6470f3c0a99f976f9e4a

refs/heads/master

2022-11-25T08:40:38.858945

2020-07-28T01:36:14

274,549,081

1

0

null

UTF-8

R

false

2,201

r

Gut Check.R

faces_avg <- faces %>% group_by(Group, `Participant #`, Time, Survey) %>% summarise(avg_resp = mean(Response)) %>% ungroup() faces_sum <- faces %>% group_by(Group, `Participant #`, Time, Survey) %>% summarise(avg_resp = sum(Response)) %>% ungroup() #Assuming the faces data is cleaned correctly. can be verified elswhere. #starting w/ base faces data prePostExp <- faces_sum %>% filter(Group == 'Experimental') %>% mutate(Survey = factor(Survey)) surveys <- unique(prePostExp$Survey) pValppExp <- c() for (i in 1:6) { pValppExp[i] <- wilcox.test(avg_resp ~ Time, paired = TRUE, alternative="less", data = filter(prePostExp, Survey == surveys[i]))[3] } pValppExp <- t(pValppExp) colnames(pValppExp) <- surveys pValppExp prePostCon <- faces_sum %>% filter(Group == 'Control') %>% mutate(Survey = factor(Survey)) pValppCon <- c() for (i in 1:6) { pValppCon[i] <- wilcox.test(avg_resp ~ Time, paired = TRUE, alternative="less", data = filter(prePostCon, Survey == surveys[i]))[3] } pValppCon <- t(pValppCon) colnames(pValppCon) <- surveys pValppCon #------------------------------- tcPre <- faces_sum %>% filter(Time == 'Pre') %>% mutate(Survey = factor(Survey), factor(Group)) pValTCPre <- c() for (i in 1:6) { pValTCPre[i] <- wilcox.test(avg_resp ~ Group, paired = FALSE, alternative="two.sided", data = filter(tcPre, Survey == surveys[i]))[3] } pValTCPre <- t(pValTCPre) colnames(pValTCPre) <- surveys pValTCPre tcPost <- faces_sum %>% filter(Time == 'Post') %>% mutate(Survey = factor(Survey), factor(Group)) pValTCPost <- c() for (i in 1:6) { pValTCPost[i] <- wilcox.test(avg_resp ~ Group, paired = FALSE, alternative="two.sided", data = filter(tcPost, Survey == surveys[i]))[3] } pValTCPost <- t(pValTCPost) colnames(pValTCPost) <- surveys pValTCPost

58744939b6615be71a99543d98fa21fb984aa0f6

5976a3a11d1b46dc1b0f38d1235adc629fe54101

/Plot 5.R

d24bfce3a5022472ada831ae63954dbbf21b2eb9

[]

no_license

DamekH/ExData_Emissions_Project

92a7d18d04e306757574975bcc5e8bd66efaa46e

e90bc25d13a2a206934f6a65eb7421405ea5fc67

refs/heads/master

2021-01-01T18:05:12.062714

2017-07-28T03:55:31

98,243,508

0

null

UTF-8

R

false

751

r

Plot 5.R

# Load and assign data NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") # Pulls subset of Baltimore and on-road data from full dataset subset_Baltimore_onroad <- NEI[NEI$fips == "24510" & NEI$type == "ON-ROAD", ] # Aggregates data from each year Yearly_Emissions_Baltimore_onroad <- aggregate(Emissions ~ year, subset_Baltimore_onroad, sum) # Open device and defines size of image png("plot5.png", width = 480, height = 480) # Construct and save barplot barplot(Yearly_Emissions_Baltimore_onroad$Emissions, names.arg = Yearly_Emissions_Baltimore_onroad$year, main = "On-Road Emissions by Year for Baltimore City, MD", xlab = "Year", ylab = "Sum of on-road PM2.5 emissions (tons)") dev.off()

78a5e62456ff4591daf9d19a27196fab0b3e0640

c750c1991c8d0ed18b174dc72f3014fd35e5bd8c

/pkgs/bayesm/man/cgetC.Rd

02b9235a6e674c681a8387b00d4e7d0a1c07b456

[]

no_license

vaguiar/EDAV_Project_2017

4b190e66fe7a6b4078cfe1b875bccd9b5a594b25

288ffaeec1cfdd873fe7439c0fa0c46a90a16a4f

refs/heads/base

2021-01-23T02:39:36.272851

2017-05-01T23:21:03

86,010,131

1

0

null

2017-05-01T23:43:04

2017-03-24T00:21:20

HTML

UTF-8

R

false

1,043

rd

cgetC.Rd

\name{cgetC} \alias{cgetC} \title{ Obtain A List of Cut-offs for Scale Usage Problems } \description{ \code{cgetC} obtains a list of censoring points, or cut-offs, used in the ordinal multivariate probit model of Rossi et al (2001). This approach uses a quadratic parameterization of the cut-offs. The model is useful for modeling correlated ordinal data on a scale from 1, ..., k with different scale usage patterns. } \usage{ cgetC(e, k) } \arguments{ \item{e}{ quadratic parameter (>0 and less than 1) } \item{k}{ items are on a scale from 1, \ldots, k } } \section{Warning}{ This is a utility function which implements \strong{no} error-checking. } \value{ A vector of k+1 cut-offs. } \references{ Rossi et al (2001), \dQuote{Overcoming Scale Usage Heterogeneity,} \emph{JASA}96, 20-31. } \author{ Rob McCulloch and Peter Rossi, Anderson School, UCLA. \email{perossichi@gmail.com}. } \seealso{ \code{\link{rscaleUsage}} } \examples{ ## cgetC(.1,10) } \keyword{ utilities }

dd9b456a54f864e380656282eda7307d5cba9c73

da6a579eef90305d8a9b10e728fdb737303e063e

/model_branch/subCodes/LapseStatistics.R

0f4e20f316cb8596bd571185606679e8f3a8ec50

[]

no_license

fidelsteiner/debriscoveredglaciers

ab7433db84cf3c40c3d22c20917e92504ac952e6

5f989bfb53e0a1311749489c2a19f46d14ddcc1a

refs/heads/master

2021-06-09T18:10:38.717316

2021-05-22T08:59:27

147,206,872

1

4

null

UTF-8

R

false

989

r

LapseStatistics.R

################################################################################ # Statistics of Bulk Models # # LapseStatsitics.R # # ReadMe: calculate Statistics for Model Performance # # Created: 2017/11/27 # Latest Revision: 2017/11/27 # # Jakob F Steiner | PhD candidate | Faculty of Geosciences | Universiteit Utrecht | # Heidelberglaan 2, 3584 CS Utrecht | W.C. van Unnik building | Room 124, Zonneveldvleugel | # j.f.steiner@uu.nl | www.uu.nl/staff/jfsteiner | www.mountainhydrology.org # LapseStatsitics <- function(Qmeas, Qmod, match) { #browser() TStats <- array(0, c(1, 3)); TStats[1,1] <- summary(lm(Qmeas[match] ~ Qmod[match]))$r.squared # R2 of Sensible Heat Flux TStats[1,2] <- sqrt(sum((Qmeas[match] - Qmod[match])^2,na.rm=T) / length(which(is.na((Qmeas[match] - Qmod[match])^2)==F))); #RMSE TStats[1,3] <- sum(Qmeas[match] - Qmod[match],na.rm=T) / length(which(is.na((Qmeas[match] - Qmod[match])^2)==F)) # MBE LapseStatsitics <- TStats }

9aee59fca1ea196e96be120aa1817f4121abaed5

f22ceaa4fbb61eb443b802964c78ece95151e76e

/plot4.R

f6b6991603771f1cd2e9f338e143a773ccdc828c

[]

no_license

pmcody77/ExData_Plotting1

7cf5435c25927fce4908a6549c9fc2ca345fe427

7041ad733dce9c216ad37d215b8215aeb3d08c03

refs/heads/master

2020-12-14T09:57:52.094168

2017-07-02T21:08:40

95,485,259

0

null

2017-06-26T20:11:01

null

UTF-8

R

false

2,005

r

plot4.R

## Plotting Assignment #1 for Coursera course on Exploratory Data Analysis ## ## Assumes the UC data set is already downloaded and extracted to working directory #### Plot4.R ##### ## Loading and preparing the data frame to plot from ##### #set coltypes so numeric columns come in as numbers not characters when loaded in (but date must be converted) coltypes <- c("character", "character", "double", "double","double","double","double","double","double") hhdata <- read.table("household_power_consumption.txt", header = TRUE, sep=";", colClasses = coltypes, na.strings = c("?")) #convert date and time variables from characters to one variable called datetime hhdata$datetime <- strptime(paste(hhdata$Date,hhdata$Time, sep=" "), "%d/%m/%Y %H:%M:%S") #subset the dataframe to the dates of interest startdate <- strptime("2007-02-01", "%Y-%m-%d") enddate <- strptime("2007-02-03", "%Y-%m-%d") #will use < this date, thus including the 2nd hhdata$includedate <- (hhdata$datetime >= startdate & hhdata$datetime < enddate) subdata <- subset(hhdata, includedate == TRUE) png("Plot4.png", width = 480, height = 480, units = "px") ## Set up for four plots par(mfrow = c(2,2)) ## CREATE THE PLOT(s) ## plot 1: plot(subdata$datetime, subdata$Global_active_power, ylab="Global Active Power (kilowatts)", xlab = "", type="l") ## plot 2: plot(subdata$datetime, subdata$Voltage, ylab="Voltage", xlab = "datetime", type="l") ## plot 3: plot(subdata$datetime, subdata$Sub_metering_1, ylab="Energy sub metering", xlab = "", type="l") lines(subdata$datetime, subdata$Sub_metering_2, col="red", type="l") lines(subdata$datetime, subdata$Sub_metering_3, col="blue", type="l") legend("topright", lwd = c(1,1,1), col = c("black", "red", "blue"), bty="n", legend = c("Sub_metering_1","Sub_metering_2", "Sub_metering_3")) ## plot 4: plot(subdata$datetime, subdata$Global_reactive_power, ylab="Global_reactive_power", xlab = "datetime", type="l") ## SAVE AS PNG dev.off()

04016efc8c7dd28893757d05461e455ed417d4e9

587bd26d91c30820bfe9debc9888e52d642f372d

/utility.R

83b0aa8db46c57d969398e38d3d9a99e2184cc9e

[]

no_license

abelmontesdeoca/ExData_Plotting1

71a4212c1cf578f0ce71165f376f2fc3293167f6

3102ff5d19e906d0fffaf055319ff58942ac76a0

refs/heads/master

2021-01-18T07:56:46.265156

2015-05-10T10:05:35

35,339,284

0

null

2015-05-09T18:03:12

null

UTF-8

R

false

449

r

utility.R

todo <- read.table("household_power_consumption.txt", header = TRUE, sep=";", na.strings="?") hpc <<- with(subset(todo, Date == "1/2/2007" | Date == "2/2/2007"), data.frame(Timestamp = strptime(paste(Date, Time), format="%d/%m/%Y %H:%M:%S"), Global_active_power, Global_reactive_power, Voltage, Global_intensity, Sub_metering_1, Sub_metering_2, Sub_metering_3))

0653701c98c76fe2d14d8be159575bb438c2293e

b2f61fde194bfcb362b2266da124138efd27d867

/code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query25_query08_1344/query25_query08_1344.R

faf2a79700b4005e8f7bbb9577aef0be11463866

[]

no_license

arey0pushpa/dcnf-autarky

e95fddba85c035e8b229f5fe9ac540b692a4d5c0

a6c9a52236af11d7f7e165a4b25b32c538da1c98

refs/heads/master

2021-06-09T00:56:32.937250

2021-02-19T15:15:23

136,440,042

0

null

UTF-8

R

false

70

r

query25_query08_1344.R

79fa44dbade359ecf7230a32a46cb6f3 query25_query08_1344.qdimacs 790 2113

df1189b5e231e0a46fdc6ca1714547d3dc2a9e4e

81a791350b09bb1999a42b7d26f160eb69039172

/ch3/R/cogsH_analysis.R

da9d6fe84f01ccd685ed3fa394baaad6f3ab9903

[]

no_license

ollyburren/thesis

08ab64cf3ec3b31213a87eb6cc676c23c2409978

3ab8bbf6e7a255f6ddfdd1ffdf5f79fb8dd57d20

refs/heads/master

2020-07-18T22:59:41.613683

2019-06-13T09:28:28

94,329,558

0

null

UTF-8

R

false

2,590

r

cogsH_analysis.R

## analyse hierachical prioritisation DATA_DIR <- '/home/ob219/share/cogs_bb/COGS' fs <- list.files(path=DATA_DIR,pattern="\\_prioritised.tab",full.names=TRUE) res.DT <- lapply(fs,fread) %>% rbindlist ## examine protein_coding only resf <- res.DT[biotype=='protein_coding',] ## filter such that there is at least marginal overall posterior that a gene ## is involved in a disease D <- resf Df <- melt(D,id.vars=c('disease','ensg','name'),measure.vars='node') c.DT<-Df[,list(gene.count=.N),by=c('disease','value')] c.DT[,] test.DT <- c.DT[disease %in% c('CD','RA','SLE','T1D','UC'),] back.DT <- c.DT[!disease %in% c('CD','RA','SLE','T1D','UC'),] bdist <- back.DT[,list(mean=mean(log(gene.count+1)),var=var(log(gene.count+1))),by='value'] M <- merge(test.DT,bdist,by='value') M[,Z:=(log(gene.count+1)-mean)/sqrt(var)] ## try Tukey transform back.DT[,tukey.trans:=transformTukey(gene.count,plotit=FALSE),by='value'] bdist <- back.DT[,list(mean=mean(log(gene.count+1)),var=var(log(gene.count+1))),by='value'] setnames(M,'value','node.name') M <- melt(M,id.vars=c('node.name','disease'),measure.vars='Z') #M <- dcast(M,disease~node.name+variable) #mat <- as.matrix(M)[,-1] %>% apply(.,2,as.numeric) #rownames(mat) <- M$disease #colnames(mat) <- colnames(mat) %>% sub("\\_Z","",.) #library(pheatmap) #pheatmap(mat) ## do in ggplot M[,p.adj:=(pnorm(value,lower.tail=FALSE) * 2) %>% p.adjust] library(cowplot) ggplot(M[p.adj<0.05,],aes(x=node.name,y=disease,fill=value)) + geom_tile(color='black') + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + xlab("Node") + ylab("Disease") + scale_fill_continuous("Z") ## looking at background distro node <- 'overall' library(rcompanion) par(mfrow=c(2,2)) qqnorm(back.DT[value==node,]$gene.count,main="Overall node gene counts") qqline(back.DT[value==node,]$gene.count,col='red') qqnorm(log(back.DT[value==node,]$gene.count + 1),main="Overall node log(gene counts+1)") qqline(log(back.DT[value==node,]$gene.count + 1),col='red') qqnorm(sqrt(back.DT[value==node,]$gene.count),main="Overall node sqrt(gene counts)") qqline(sqrt(back.DT[value==node,]$gene.count),col='red') T_tuk = transformTukey(back.DT[value==node,]$gene.count,plotit=FALSE) qqnorm(T_tuk,main="Overall node transformTukey") qqline(T_tuk,col='red') par(mfrow=c(1,1)) hist(back.DT[value==node,]$gene.count,main="Overall node gene counts") bb_trait <- readRDS('/home/ob219/share/cogs_bb/bb_trait_manifest.RDS') scount.DT <- merge(back.DT,bb_trait[,.(phe,cases)],by.y='phe',by.x='disease') test <- scount.DT[,list(cor=cor(gene.count,cases)),by='value']

553a570fb765ef1ba0e03279f88f622cb56af9ab

1197eab6d7008068cd5558d26039fa157c6e7854

/ui.R

cc0d5012ce684145fdaa92cf409bc49fc44c9009

[]

no_license

arjitmazumdar/DataProducts

66976b90e9a38cfb9bf57f63ce7376eb43e12716

1f490236d971fe62cbb16eae0c521c3252b76887

refs/heads/master

2016-08-12T03:53:32.081992

2015-12-27T11:31:27

48,642,153

0

null

UTF-8

R

false

1,569

r

ui.R

# ui.R shinyUI(fluidPage( titlePanel(" BMI- Body Mass Index Calculation For Men and Women"), sidebarLayout( sidebarPanel( p(h4("Please Enter Your Details")), textInput("text", label = ("Name"), value = "Enter Name..."), radioButtons("radio", label = ("Sex"), choices = list("Male" = 1, "Female" = 2 ),selected = 1), sliderInput("slider1", label = ("Height (in meters)"), min = 0, max = 300, value = 0), sliderInput("slider2", label = ("Weight (in kgs)"), min = 0, max = 200, value = 0), submitButton("Submit") ), mainPanel( h4(em("Stay Healthy Stay Happy")), p(strong("To keep the body in", em("good health"),"is a duty ... otherwise we shall not be able to keep our mind", em("strong and clear.- Buddha"))), br(), img(src="bmi.png", height = 200, width = 500), br(), br(), br(), textOutput("bmi"), br(), p("For maintaining a good BMI, visit the ", a("National Heart, Lung and Blood Institute homepage.", href = "http://www.nhlbi.nih.gov/health/educational/lose_wt/index.htm")), p(h6(" Copyright: National Institute of Health ")) ))))

fb72ee83b281b21d283768edaad6a2f428f03e90

32f251147606d865a04834ba8f08f8be75410738

/man/Hdr_LN2.Rd

4899b3447d869ff6eb19991624d42a3e2d8c3e0a

[]

no_license

cdv04/ACTR

4e17aaab32d319b1b609b6c1c0c553a0f7e41317

d1762dc8884eb37b023cf146a71c05a96508cc08

refs/heads/master

2021-01-01T05:11:47.528297

2017-04-07T10:16:40

59,212,181

0

null

UTF-8

R

false

true

731

rd

Hdr_LN2.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Hdr_LN2.R \name{Hdr_LN2} \alias{Hdr_LN2} \title{Calculating HDR5 of observed data from log normal distribution.} \usage{ Hdr_LN2(edr10.df, th) } \arguments{ \item{th}{The desired probability (i.e. 0.05 for HDR5)} \item{edr10}{A dataframe containing the edr10 for all the var_group we want to calculate hdr th} } \value{ a data frame containing the hdr th calculated from empirical distribution } \description{ Calculating HDR5 of observed data from log normal distribution. } \details{ This function is necessary for IC_HdrLN2, when IC of hdr th are etsimated without caclculation weight of the bootstrap sample } \examples{ Hdr_LN(edr10_obs,0.05) }

50bd58e9ece0751d14487be10b0b851365bd4909

046d61375e3d1fb5efcad840a992a9ea2b7d897f

/R/eb.R

81cd6a8b8ee0ea34a3aa3b82bd2a47bc2e87530d

[]

no_license

pbreheny/deepn

a494f634ebcce4bdf022af6539aa1fc92bd5af5c

04d69d464f1b8f410d747fe416898ec22a8361ef

refs/heads/master

2021-01-09T20:18:45.151939

2020-05-04T22:17:33

61,055,789

0

2

null

UTF-8

R

false

226

r

eb.R

# NOT FUNCTIONAL eb <- function(z, se, lam=0.05) { n <- length(z) w <- 1/se^2 m <- weighted.mean(z, w) mu <- lam*m + (1-lam)*z v <- 1/(w + lam*w) mu[is.na(mu)] <- m #v[is.na(v)] <- 1/(lam*) #list(mu=mu, v=v) }

d14c9f8f841de0ad71c0ecc905d5006a881247b4

e0b0ddad937ce559aba8ce8e0ef2d5432ed839c9

/result/real_data_0621/lib/sim.R

795695727509e49d9f71eac3eb6334daeefe037a

[]

no_license

jenjong/RankConsistency

968b4dfb7f5a43b33ad8bc0a8d872dbb4d6a24f5

ad262789e833757e2437fb2423ca71e7ce89793c

refs/heads/master

2020-03-11T01:21:25.848962

2019-04-08T11:11:15

129,689,557

1

0

null

UTF-8

R

false

19,653

r

sim.R

#1 Dirichlet density function ddirichlet<- function(x,alpha.vec) { xd = 1-sum(x) v<- sum((alpha.vec[1:2]-1)*log(x)) + (alpha.vec[3]-1)*log(xd) v1<- gamma(sum(alpha.vec))/prod(gamma(alpha.vec))*exp(v) v1 } #2 glmnet design matrix and reponse matrix gen.designR <- function(p) { x = matrix(0, p*(p-1), p) y = rep(0, p*(p-1) ) ix = 1 for (i in 1:p) { for (j in 1:p) { if (i == j) next jx1 = min(i,j) jx2 = max(i,j) x[ix,jx1] = 1; x[ix,jx2] = -1 if (i<j) y[ix] = 1 ix = ix + 1 } } x = x[,-p] return(list(x = x, y = y)) } #3 Construct Qmat gen.Qmat <- function(p,counter, tn, rand.sim = F ) { if (counter == 1) { alpha.vec1 = c(10, 8, 3) alpha.vec2 = c(3, 3, 10) alpha.vec3 = c(10, 3, 4) } if (counter == 2) { alpha.vec1 = c(1, 8, 3) alpha.vec2 = c(3, 3, 10) alpha.vec3 = c(10, 3, 4)} if (counter == 3) { alpha.vec1 = c(3, 2, 1) alpha.vec2 = c(3, 4, 1) alpha.vec3 = c(3, 6, 1) } if (counter<4) { pi.var1 = 1/3 pi.var2 = 1/3 pi.var3 = 1/3 z = seq(1/p,1-1/p, length = p) dmat <- matrix(0, length(z), length(z)) for (i in 1:length(z)) { for (j in 1:length(z)) { xvar = c(z[i], z[j]) if (sum(xvar)>= 1) next dmat[i,j] <- pi.var1*ddirichlet(xvar,alpha.vec1)+ pi.var2*ddirichlet(xvar,alpha.vec2) + (1-pi.var1-pi.var2)*ddirichlet(xvar,alpha.vec3) } } #dmat dmat <- dmat/sum(dmat) if (rand.sim == T) { tmp <- drop(rmultinom(1, tn, prob = c(dmat)) ) dmat1 <- matrix(tmp, p, p) } if (rand.sim == F) dmat1 <- round(dmat*tn) dmat2 <- matrix(0,p,p) for (j in 1:p) dmat2[,j] <- rev(dmat1[j,]) dmat2 <- dmat2 + t(dmat2) Qmat = dmat2 } if (counter == 4) { #Qmat = matrix( rpois( p^2 , p^2)^2 , p, p) Qmat = matrix( runif(p^2), p , p) Qmat[lower.tri(Qmat, diag = FALSE)] = 0 #Qmat[sample(1:length(Qmat), trunc(length(Qmat)/4))] = 0 Qmat = Qmat + t(Qmat) diag(Qmat) <- 0 dmat1 = NULL n = sum(Qmat) nj = colSums(Qmat) Qpmat = Qmat for (j in 1:nrow(Qmat)) Qpmat[j,] = round(Qmat[j,]/n*2,2) return(list(Qmat = Qmat, dmat = dmat1, Qpmat = Qpmat)) } n = sum(Qmat) nj = colSums(Qmat) Qpmat = Qmat for (j in 1:nrow(Qmat)) Qpmat[j,] = Qmat[j,]/n*2 # dmat1 has the coordiantes for image() return(list(Qmat = Qmat, dmat = dmat1, Qpmat = Qpmat)) } #4 simulation code in Gmat.hat gen.Gmathat <- function(Gmat, Qmat) { p = ncol(Gmat) gmat.prob<-c(Gmat) gmat.num <- c(Qmat) gmat.gen<- rep(0, length(gmat.num)) for (i in 1:length(gmat.num)) { gmat.gen[i] <- rbinom(n = 1, size = gmat.num[i], prob = gmat.prob[i]) } Gmat.hat <- matrix(gmat.gen,p,p) Gmat.hat[lower.tri(Gmat.hat, diag = T)] = 0 tmp <- Qmat - t(Gmat.hat) Gmat.hat[lower.tri(Qmat)]<- tmp[lower.tri(Qmat)] Gmat.hat <- Gmat.hat/Qmat Gmat.hat[!is.finite(Gmat.hat)] = 0 return( Gmat.hat ) } #5 naive est naive.fun <- function(Qpmat, Gmat.hat,x,y,p) { wmat = Qpmat*Gmat.hat wmat = t(wmat) wvec = wmat[ - (1 + ( 0:(p-1) ) *(p+1))] # fit glmnet fit <- glmnet(x, y, family = 'binomial', intercept = FALSE, weights = wvec, lambda = 0, standardize = F, thresh = 1e-09) est = c(fit$beta[,1],0) naive.est <- est return(naive.est) } ## # # check the existence of estimator by Qpmat.c1 # # note that the condition of existence only holds for population version # # 1. make edges # tmp <- cbind( rep(1:p, p), rep(1:p, each = p), c(Qpmat.c1)) # tmp = tmp[tmp[,3] > 0, 1:2] # ed = c(t(tmp)) # # 2. make directed graph # g <-make_graph(ed, n = p, directed = TRUE) # # 3. connectivity check # cg <- components(g, mode = 'strong') # # 4. report # if (cg$no == 1) print("estimator exists!") else print("estimator does not exist !") # prop.fun <- function(max.k, Qpmat, Gmat.hat, x, y, p) { Result = NULL Result.list = list() k = 0 for (k in 0:max.k) { #cat('outer interataion::: ', k , '\n') # Thresholding Qpmat.c1 by constant 'cvar' # unique cvec cvec<- sort(unique(Qpmat[upper.tri(Qpmat)])) # the first threshold if (k == 0 ) idx <- which(Qpmat <= 0) if ( k>0 ) idx <- which(Qpmat <= cvec[k]) Qpmat.c1 = Qpmat Qpmat.c1[idx] <- 0 ############################### # set weight-vector i1 = 1 ; i2 = 2 idx = 1 result = matrix(0, p*(p-1)/2, 4) for ( i1 in 1:(p-1)) { for (i2 in (i1+1):p) { Qpmat.c2 = Qpmat.c1 nvec1 = Qpmat.c1[i1,] nvec2 = Qpmat.c1[i2,] idx1 = which(nvec1 == 0 | nvec2 == 0) idx2 = setdiff( idx1, c(i1, i2)) nvec3 = (nvec1[-idx1]+nvec2[-idx1])/2 Qpmat.c2[i1,-idx1] = Qpmat.c2[i2,-idx1] = nvec3 if (length(idx2)>0) Qpmat.c2[i1,idx2] = Qpmat.c2[i2,idx2] = 0 Qpmat.c2[,i1] <- Qpmat.c2[i1,] Qpmat.c2[,i2] <- Qpmat.c2[i2,] # check the result: Qpmat.c2[i1,] # : Qpmat.c2[i2,] # if (length(idx2) > 0 & max( c( Qpmat.c1[i1,idx2],Qpmat.c1[i2,idx2]) ) >0) :: check the existence of estimator ############################### # set weight-vector in glmnet wmat = Qpmat.c2*Gmat.hat wmat = t(wmat) wvec = wmat[ - (1 + ( 0:(p-1) ) *(p+1))] # fit glmnet fit <- glmnet(x, y, family = 'binomial', intercept = FALSE, weights = wvec, lambda = 0, standardize = F, thresh = 1e-09) est = c(fit$beta[,1],0) result[idx, 1:2] = c(i1, i2) if( est[i1] > est[i2]) result[idx, 3] = 1 pmat = plogis(outer(est, est, FUN = '-')) vmat = pmat*(1-pmat) v1 = rowSums(Qpmat.c2*vmat) v1 <- v1[-p] v2 = (-Qpmat.c2*vmat)[-p,-p] diag(v2) <- v1 inv.v2 <- solve(v2) if (i2 < p ) result[idx, 4] <- inv.v2[i1,i1] + inv.v2[i2,i2] - 2*inv.v2[i1,i2] if (i2 == p ) result[idx, 4] <- inv.v2[i1,i1] idx = idx + 1 #cat(' inner iteration:' , idx, '\n') #plot(c(fit$beta[,1],0), type ='b') #min(-diff(est, 1)) #sum(-diff(est, 1)<0) #v1 = rowSums(Qpmat.c2*vmat) } } Result <- cbind(Result, result[,4]) Result.list[[k+1]] <- result } return(list(Result = Result, Result.list = Result.list)) } #6 aggregation agg.fun <- function(prop.result) { min.vec<-apply(prop.result$Result, 1, which.min) agg.result <- matrix(0,nrow(prop.result$Result), 3) agg.result[,1:2] <- (prop.result$Result.list[[1]])[,1:2] for (i in 1:nrow(agg.result)) { agg.result[i,3] <- (prop.result$Result.list[[min.vec[i]]])[i,3] } return(agg.result) } summary.fun<- function(tmp) { sum(tmp[,3])== nrow(tmp) sum(tmp[,3]==0) #tmp[tmp[,3] == 1,1:2] tmp1 <- tmp[,1:3] tmp2 <- tmp[,c(2,1,3)] tmp2[,3]<- abs(tmp2[,3]-1) sc <- c() for ( i in 1:p) { sc[i] = sum(tmp1[tmp1[,1]== i ,3]) + sum(tmp2[tmp2[,1]== i ,3]) } return(sc) } ### cons.rank<- function(tmp) { tmp.copy<- tmp[,c(2,1,3)] tmp.copy[,3] <- 1-tmp.copy[,3] tmp <- rbind(tmp, tmp.copy) tmp <- tmp[tmp[,1] != 0,] Cset <- unique( tmp[,1]) sc <- c() while( length(Cset)>0) { Uset <- c() Cset <- unique( tmp[,1]) for (i in Cset) { tmp.sub1<-tmp[tmp[,1] == i,, drop = F] if ( sum(tmp.sub1[,3]) == nrow(tmp.sub1) ) Uset <- c(Uset, i) } #cat(sc,'\n') if ( length(Uset) == 1) { sc <- c(sc,Uset) if (length(sc)==(p-1)) { sc<- c( sc, setdiff(Cset, Uset) ) break } Uset.idx<-tmp[, 1] == Uset | tmp[, 2] == Uset tmp <- tmp[!Uset.idx,] } else { sc <- NA break } } return(sc) } ## cv code # make cv_mat # cv_mat_fun <- function(Gmat_obs, Qmat, k_fold) { cv_mat = matrix(0, tn, 4) s1 = 1 for (j in 1:(ncol(Gmat_obs)-1)) { for(i in (j+1):nrow(Gmat_obs)) { a1 = Qmat[i,j] if (a1 == 0 ) next f_idx <- s1:(s1+a1-1) cv_mat[f_idx,1] <- i cv_mat[f_idx,2] <- j s1 = s1 + a1 a2 = Gmat_obs[i,j] if (a2 == 0 ) next f_idx2 <- sample(f_idx,a2) cv_mat[f_idx2,3] = 1 } } cv_mat[,4] <- sample(1:k_fold, tn, replace = TRUE) colnames(cv_mat) = c("j", "k", "y_jk", "partition") return(cv_mat) } cv_table_fun = function(cv_m) { cv_m <- as.data.frame(cv_m) # require(dplyr) result = cv_m %>% group_by(j, k) %>% summarize(sum = length(y_jk)) %>% as.matrix() Qmat_tr = matrix(0, p, p) for ( i in 1:nrow(result)) { Qmat_tr[result[i, 1], result[i, 2]] <- result[i, 3] } Qmat_tr <- Qmat_tr + t(Qmat_tr) result = cv_m %>% group_by(j, k) %>% summarize(sum = sum(y_jk)) %>% as.matrix() Gmat_tr = matrix(0, p, p) for ( i in 1:nrow(result)) { Gmat_tr[result[i, 1], result[i, 2]] <- result[i, 3] } tmp <- Qmat_tr - t(Gmat_tr) Gmat_tr[upper.tri(Qmat_tr)] <- tmp[upper.tri(Qmat_tr)] return(list(Q=Qmat_tr, G=Gmat_tr)) } gen_sim_fun = function(Gmat, Qmat) { ## Gmat.hat : true Gmat을 이용하여 data generation을 할 경우에 승패 수와 전체 대결 수를 이용하여 ## 만든 Gmat의 추정값 gmat_prob <- c(Gmat) ## Gmat_jk : j object와 k object에서 j가 k를 이길 확률. gmat_num <- c(Qmat) gmat_gen<- rep(0, length(gmat_num)) for (i in 1:length(gmat_num)) { gmat_gen[i] <- rbinom(n = 1, size = gmat_num[i], prob = gmat_prob[i]) } Gmat_obs <- matrix(gmat_gen,p,p) Gmat_obs[lower.tri(Gmat_obs, diag = T)] = 0 tmp <- Qmat - t(Gmat_obs) Gmat_obs[lower.tri(Qmat)]<- tmp[lower.tri(Qmat)] return( list(G = Gmat_obs, Q = Qmat) ) } bt_fun = function(gen_fit, lambda.vec = NULL) { Gmat.hat <- gen_fit$G Qmat <- gen_fit$Q p = ncol(Qmat) Gmat.hat <- Gmat.hat/Qmat Gmat.hat[!is.finite(Gmat.hat)] = 0 n = sum(Qmat) Qpmat = Qmat/n*2 wmat = Qpmat*Gmat.hat wmat = t(wmat) wvec = wmat[ - (1 + ( 0:(p-1) ) *(p+1))] # fit glmnet fit <- glmnet(x, y, family = 'binomial', intercept = FALSE, weights = wvec, lambda = 0, standardize = F, thresh = 1e-09) est = c(fit$beta[,1],0) if (is.null(lambda.vec)) cor.r = NULL else cor.r = cor(est, lambda.vec, method = 'kendall') return( list (coefficients = est, cor = cor.r) ) } gbt_step1_fun = function(Qpmat, Gmat.hat, p, cval) { result = matrix(0, p*(p-1)/2, 4) idx = 1 # select a pair of items for obtaining rank consistent estimator # define a matrix to save paired results for ( i1 in 1:(p-1)) { for (i2 in (i1+1):p) { Qpmat.c1 = Qpmat # threshold step idx1 <- ( Qpmat.c1[i1,] <= cval ) idx2 <- ( Qpmat.c1[i2,] <= cval ) ## intersect(!idx1,!idx2)=\hat{O}_jk if (sum(idx1)>0 ) ##length->sum으로 고침 { Qpmat.c1[i1,idx1] <- 0 ; Qpmat.c1[idx1,i1] <- 0 } if (sum(idx2)>0 ) ##length->sum으로 고침 { Qpmat.c1[i2,idx2] <- 0 ; Qpmat.c1[idx2,i2] <- 0 } Qpmat.c2 = Qpmat.c1 ## thresholding procedure Qpmat.c2 = Qpmat.c2*(Qpmat.c2>cval) nvec1 = Qpmat.c1[i1,] nvec2 = Qpmat.c1[i2,] idx1 = which(nvec1 == 0 | nvec2 == 0) ## !idx1 : \hat{O}_jk idx2 = setdiff( idx1, c(i1, i2)) # balancing the weight parameter in gBT model for the selected pair nvec3 = (nvec1[-idx1]+nvec2[-idx1])/2 Qpmat.c2[i1,-idx1] = Qpmat.c2[i2,-idx1] = nvec3 if (length(idx2)>0) Qpmat.c2[i1,idx2] = Qpmat.c2[i2,idx2] = 0 Qpmat.c2[,i1] <- Qpmat.c2[i1,] ## 대칭이 되도록 만들자 Qpmat.c2[,i2] <- Qpmat.c2[i2,] ## 대칭이 되도록 만들자 ## find V_jk(maximum connected set) i1i2_adj_matrix = matrix(as.integer(Qpmat.c2>0) , p , p) ## adjacency matrix i1i2_graph = graph_from_adjacency_matrix(i1i2_adj_matrix , mode="undirected" , weighted=NULL) ## make a graph i1i2_clusters = clusters(i1i2_graph)$mem ## clustering using adj matrix if (i1i2_clusters[i1] != i1i2_clusters[i2]) { ## i1과 i2가 다른 connected 되지 않은 경우 #cat(' k:',k,', ',i1,'and',i2, 'is not connected!!\n') idx = idx + 1 next } ## idx3 : edge index set of V_jk idx3 = sort(which(i1i2_clusters %in% i1i2_clusters[i1])) ######################################### ## computing gBT estimator ######################################### wmat <- Qpmat.c2[idx3, idx3]*Gmat.hat[idx3, idx3] wmat = t(wmat) pp <- length(idx3) wvec = wmat[ - (1 + ( 0:(pp-1) ) *(pp+1))] ## w_jj는 제거.. xx = matrix(0, pp*(pp-1), pp) yy = rep(0, pp*(pp-1) ) ix = 1 for (i in 1:pp) { for (j in 1:pp) { if (i == j) next jx1 = min(i,j) jx2 = max(i,j) xx[ix,jx1] = 1; xx[ix,jx2] = -1 if (i<j) yy[ix] = 1 ix = ix + 1 } } xx = xx[,-pp] # note that the gBT estimator may not exist because of the thresolding # use ridge try.fit <- try(fit <- glmnet(xx, yy, family = 'binomial', intercept = FALSE, weights = wvec, lambda = 1e-5, alpha = 0, standardize = F, thresh = 1e-09), silent = T) if (class(try.fit)[1] == 'try-error') { ## Q: result[idx,]에 error가 났다는 어떤 표시도 하지 않나..?? ## A: result에 (i1,i2) 대신 (0,0)을 표시.. idx = idx + 1 next } est = c(fit$beta[,1],0) ## lambda_pp 추가 result[idx, 1:2] = c(i1, i2) # compare the values of lambda_{i1} and lambda_{i2} if( est[which(idx3==i1)] > est[which(idx3==i2)]) result[idx, 3] = 1 ## \hat{lambda_i1}>\hat{lambda_i2}면 result[idx,3]에 1을 부여..(아니면 0) ## calculate weight v_jk # 1. obtain the asymptotic variance pmat = plogis(outer(est, est, FUN = '-')) vmat = pmat*(1-pmat) v1 = rowSums(Qpmat.c2[idx3,idx3]*vmat) v1 <- v1[-pp] v2 = (-Qpmat.c2[idx3,idx3]*vmat)[-pp,-pp] diag(v2) <- v1 ## calculate asymptotic covariance matrix tri.fit2 = try(inv.v2 <- solve(v2*sum(Qmat)/2)) if (class(tri.fit2)[1] == 'try-error'){ cat(' k:',k,', ',i1,'and',i2, ': cannot calaulate inverse') result[idx , 4] = 4/(sum(Qpmat.c2[idx3,idx3]*sum(Qmat)/2)/2) ## alternative v_jk idx = idx+1 ## if error -> next step next } i1_ind = which(idx3==i1); i2_ind = which(idx3==i2) if ((i1_ind<pp) & (i2_ind<pp)) { result[idx, 4] <- inv.v2[i1_ind,i1_ind] + inv.v2[i2_ind,i2_ind] - 2*inv.v2[i1_ind,i2_ind] } if ((i1_ind==pp) | (i2_ind==pp) ) { min_ind = min(i1_ind , i2_ind) result[idx , 4] = inv.v2[min_ind , min_ind] } ## 2. v_jk = \sum n_ml #result[idx , 4] = sum(Qpmat.c2[idx3,idx3])/2 #cat(' inner iteration:' , idx, '\n') idx = idx + 1 #plot(c(fit$beta[,1],0), type ='b') #min(-diff(est, 1)) #sum(-diff(est, 1)<0) #v1 = rowSums(Qpmat.c2*vmat) #} #} } } return(result) } # note that gbt_step2_fun has two types of returns: gbt_step2_fun = function(result, p, lambda.vec, newdata = NULL, weight = TRUE) { tmp<-result not0_ind = (tmp[,1]!=0) tmp <-tmp[not0_ind, 1:3] p.set <-sort(unique(c(tmp[,1:2]))) if (length(p.set) != p) { return(list(gbt_est = rep(NA,p), cor = NA, test_cor = NA)) } xx <- matrix(0, nrow(tmp)*2, p) yy <- rep(0, nrow(tmp)*2) i = 1 for ( i in 1:nrow(tmp)) { vec1<-tmp[i,1:2]; vec2<- tmp[i,3] xx[2*(i-1)+1, vec1] <- c(1,-1) ; yy[2*(i-1)+1] <- vec2 xx[2*i, vec1] <- c(-1,1) ; yy[2*i] <- abs(vec2 - 1) } xx<- xx[,-p] ## See fit: Note that weights denotes v_jk v_weight = rep(result[not0_ind, 4],each=2) if (weight == FALSE) v_weight = rep(1, length(v_weight)) fit<-glmnet(xx,yy, family = 'binomial', alpha = 0, lambda = 1e-5, intercept = FALSE, weights = v_weight , standardize = F) gbt.est <- c(fit$beta[,1],0) if (is.null(lambda.vec)) { cor.r = NA } else { cor.r <- cor(gbt.est, lambda.vec, method = 'kendall') } if (is.null(newdata)) { test_cor = NA } else { tmp = matrix(0,p,p) Q = newdata$Q G = newdata$G for (i in 1:p) { for (j in 1:p) { if (i == j) next tmp[i,j] = as.integer( gbt.est[i]-gbt.est[j] > 0 ) } } test_cor = sum(tmp*G)/(sum(Q)/2) } return(list(gbt_est = gbt.est, cor = cor.r, test_cor = test_cor)) } sparse_gen_fun <- function(dmat, kn, rn, tn, random = TRUE) { if (random == TRUE) { dmat1 <- dmat ## dmat : {q_jk} matrix u.idx <- which( dmat > 0) sel.u.idx<- sample(u.idx, kn) dmat1[sel.u.idx] <- 0 dmat1 <- dmat1/sum(dmat1) d.sample <- drop (rmultinom(1, tn-rn*kn, prob = c(dmat1))) ## n_jk d.sample[sel.u.idx] <- rn dmat1 <- matrix(d.sample, p , p) Qmat <- matrix(0, p, p ) for (j in 1:p) Qmat[,j] <- rev(dmat1[j,]) Qmat <- Qmat + t(Qmat) return(Qmat) } if (random == FALSE) { dmat1 <- dmat ## dmat : {q_jk} matrix for (j in 1:p) dmat1[,j] <- rev(dmat[j,]) #dmat1 <- dmat1 + t(dmat1) u.idx <- which( dmat1>0) sel.u.idx<- sample(u.idx, kn) dmat1[sel.u.idx] <- 0 dmat1 <- dmat1/sum(dmat1) d.sample <- drop (rmultinom(1, tn-rn*kn, prob = c(dmat1))) d.sample[sel.u.idx] <- rn dmat1 <- matrix(d.sample, p , p) Qmat <- matrix(0, p, p ) for (j in 1:p) Qmat[,j] <- rev(dmat1[j,]) Qmat <- Qmat + t(Qmat) ## Qmat : 최종적인 n_jk matrix return(Qmat) } } cv.gbt_fun = function(gen_fit, cvec, k_fold, lambda.vec) { fit = gen_fit p = ncol(fit$Q) cv_mat = cv_mat_fun(fit$G, fit$Q, k_fold) cor_mat = matrix(NA, k_fold, length(cvec)) for (k in 1:length(cvec)) { ######### gBT model ########### cval <- cvec[k] # k-fold for (k_num in 1:k_fold) { result = NULL tmp_te = cv_mat[cv_mat[,"partition"] == k_num,-4] tmp_tr = cv_mat[cv_mat[,"partition"] != k_num,-4] cv_table <- cv_table_fun(tmp_tr) Qmat <- cv_table$Q Gmat.hat <- cv_table$G Gmat.hat = Gmat.hat/Qmat Gmat.hat[!is.finite(Gmat.hat)] = 0 n = sum(Qmat) Qpmat = Qmat/n*2 result <- gbt_step1_fun(Qpmat, Gmat.hat, p, cval) # gbt_step2_fun cv_table <- cv_table_fun(tmp_te) gbt_fit <- gbt_step2_fun(result, p, lambda.vec, cv_table) # cat ("k:", k, " k_num:", k_num," ", gbt_fit$test_cor,'\n') if (any(is.na(gbt_fit))) next cor_mat[k_num, k] <- gbt_fit$test_cor } } return(cor_mat) } gbt_fun = function(gen_fit, cval, lambda.vec) { Gmat.hat <- gen_fit$G Qmat <- gen_fit$Q p = ncol(Qmat) Gmat.hat <- Gmat.hat/Qmat Gmat.hat[!is.finite(Gmat.hat)] = 0 n = sum(Qmat) Qpmat = Qmat/n*2 result <- gbt_step1_fun(Qpmat, Gmat.hat, p, cval) gbt_fit<- gbt_step2_fun(result, p, lambda.vec) gbt_est = gbt_fit$gbt_est cor = gbt_fit$cor return( list (coefficients = gbt_est, cor = cor) ) }

a1c7d8a5ad122811bd5e4d7eab53b17f5a6f5267

51edc04f9e08625a2ba5fd4bb1ab18cd6b244549

/cachematrix.R

9bc6f2cdf9ca1f1cdcbe6fe034e8f9f1886d36fc

[]

no_license

cmattoon/ProgrammingAssignment2

da2b6d4f0623271aa7d1f1d685586fe3c3f83730

29f85802713d1156b98e380946077d8cbc8b4b05

refs/heads/master

2020-12-25T21:01:00.118162

2014-10-25T17:36:41

null

0

null

UTF-8

R

false

1,285

r

cachematrix.R

## Solves and caches inverse of matrix. ## Usage: ## > cache_matrix <- makeCacheMatrix(c(11,12,21,22), ncol=2, nrow=2) ## > inverse <- cacheSolve(cache_matrix) ## Attaches getters/setters for both the object ## and the inverse property. makeCacheMatrix <- function(x = matrix()) { inverse <- NULL ## This function resets the cache (inverse <<- NULL) when the ## value of the original matrix (x) changes. set <- function(y) { x <<- y inverse <<- NULL } ## Getter for self. get <- function() x ## Sets the inverse 'property' to inv setinverse <- function(inv) inverse <<- inv ## Gets the inverse 'property' getinverse <- function() inverse ## List list(set = set, get = get, setinverse = setinverse, getinverse = getinverse, solve=solve) } ## This function leverages the $inverse property ## to avoid expensive calculations in solve(x) cacheSolve <- function(x, ...) { ## Get the inverse (matrix or NULL) inverse <- x$getinverse() if(!is.null(inverse)) { ## Cache hit, return the cached value. return(inverse) } ## If we're here, it was a miss. Get the actual result ## with solve() inverse <- solve(x$get()) ## Set the inverse to the new value with the setter. x$setinverse(inverse) inverse }

0a913dbb663ccbcedf194a9b5a9d9fecc9e77910

d45283f3b64c81c2a1c1e7a4cf5e2ff4d9e7b495

/man/mtcarz.Rd

f8c54db704efa8522a4990805273cd7f12278183

[]

no_license

DuyDN/descriptr

d138c1121e4fdb969b4d70e08402320c8417b1d2

6f3469f2e068c8e685dbb25471468b0816d1406e

refs/heads/master

2022-05-19T14:40:56.429504

2020-02-01T10:20:44

null

0

null

UTF-8

R

false

true

334

rd

mtcarz.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ds-data-mtcarz.R \docType{data} \name{mtcarz} \alias{mtcarz} \title{mtcarz} \format{An object of class \code{data.frame} with 32 rows and 11 columns.} \usage{ mtcarz } \description{ Copy of mtcars data set with modified variable types } \keyword{datasets}

c2e4221beb946aa02ac7713de2b3c01cab9ad375

288b4b6998906714ab368e0ee14c70a4059be4ab

/tests/testthat/test_data_mcdaniel1994.R

550f3a9f377bcbb1d95ab35cb1780af18cb741a5

[]

no_license

qsh7950/metadat

f6243a382c8c0e3f4c9a0e2cd657edb0ffa3e018

5c70fa63d7acfa1f315534fb292950513cb2281e

refs/heads/master

2021-02-26T06:42:18.937872

2019-10-21T21:58:33

null

0

null

UTF-8

R

false

207

r

test_data_mcdaniel1994.R

source("hashTable.R") context("Checking data: mcdaniel1994") library(digest) test_that("checks data md5 hash", { expect_match(digest(metadat::dat.mcdaniel1994, algo = "md5"), hashTable$mcdaniel1994) })

459c300a1e4ce858d587412b34bf906e812f59b4

dbfe5ce272e204a8e1663ced35c9d48ef4870496

/man/str_similar_pair.Rd

902e24218c87186defe968a20b33412d249c7af4

[ "MIT", "LicenseRef-scancode-unknown-license-reference" ]

permissive

hmito/hmRLib

fac91a4e2ddfcd899283ec0b63c87c31965fb17f

f2cfd54ea491ee79d64f7dd976a94086092b8ef5

refs/heads/master

2023-08-31T07:21:31.825394

2023-08-28T10:02:07

41,907,654

0

null

UTF-8

R

false

true

551

rd

str_similar_pair.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/character.R \name{str_similar_pair} \alias{str_similar_pair} \title{Check similarity pair of given string with themselves.} \usage{ str_similar_pair(string, similarity = 3, only_sub = FALSE) } \arguments{ \item{string}{character for checking similarity} \item{similarity}{threshold similarity} \item{only_sub}{count only substitute} } \value{ logical: TRUE if string is enough similar with target. } \description{ Check similarity pair of given string with themselves. }

1251f1fff2d5b13c78109d3d978bc16eba23bc96

3eab8baafc916551a9cae1f1145949facfbe20ba

/Sync_DB_with_Ratings.R

ddbae58617586e800c69062ce73c263f001cfb64

[]

no_license

LovinSpoonful/IS607-Project3

1d1a67c666eeb7dc4eeb0ada5a4da4958c07c514

2a7fbadb946aa8a8ecf1cbd09f4ae02d84131ab3

refs/heads/master

2021-01-10T16:54:25.180508

2016-03-27T03:20:56

54,080,950

0

1

null

UTF-8

R

false

1,413

r

Sync_DB_with_Ratings.R

library(RMySQL) # MySQL DB info proj_user <- "project3" proj_pwd <- "CUNYRBridge4" proj_db <- "skill" proj_host <- "db4free.net" ## ------------------------------------------ ## Using RMYSQL ## ------------------------------------------ # establish the connection to the skill DB on db4free.net skilldb = dbConnect(MySQL(), user=proj_user, password=proj_pwd, dbname=proj_db, host=proj_host) # weighted_rating_by_skill_type dbWriteTable(skilldb, name="df_temp1", value=data.frame.name) dbSendQuery(con, " UPDATE TBL_DATA T, DF_TEMP1 R SET T.WEIGHTED_RATING_BY_SKILL_TYPE = R.WEIGHTED_RATING_BY_SKILL_TYPE WHERE T.SKILL_ID = R.SKILL_ID AND T.SOURCE_ID = R.SOURCE_ID;") # overall_weighted_rating dbWriteTable(skilldb, name="df_temp2", value=data.frame.name) dbSendQuery(con, " UPDATE TBL_DATA T, DF_TEMP2 R SET T.OVERALL_WEIGHTED_RATING = R.OVERALL_WEIGHTED_RATING WHERE T.SKILL_ID = R.SKILL_ID AND T.SOURCE_ID = R.SOURCE_ID;") # rating_by_skill_set dbWriteTable(skilldb, name="df_temp3", value=data.frame.name) dbSendQuery(con, " UPDATE TBL_DATA T, DF_TEMP2 R SET T.RATING_BY_SKILL_SET = R.RATING_BY_SKILL_SET WHERE T.SKILL_ID = R.SKILL_ID AND T.SOURCE_ID = R.SOURCE_ID;") dbSendQuery(mydb, "DROP TABLE IF EXISTS DF_TEMP1, DF_TEMP2, DF_TEMP3")

6a1141c819ee1ee596d6b7e86e8032173c0b7075

6033a4d1ddfa4000df0095adf4e16d60b465fab7

/top5_CityHotel.R

cad98d4272401598c25273e4fdd2f88fe9a50e5e

[]

no_license

Pari02/Summer_Practicum_Orbitz

e2408856605e7a4f7449c585df12bd801cf726db

61b519b26b8f829a69170068dd90467a24fb6985

refs/heads/master

2021-01-15T16:57:39.754646

2015-09-27T09:01:03

38,257,253

1

0

null

UTF-8

R

false

6,989

r

top5_CityHotel.R

# installing required libraries install.packages("rjson") install.packages("jsonlite") install.packages("hydroTSM") install.packages("RCurl") install.packages("XML") install.packages("zoo") installed.packages("ggplot2") library(rjson) library(jsonlite) library(hydroTSM) library(plyr) library(RCurl) library(XML) library(zoo) library(ggplot2) library(RColorBrewer) library(wordcloud) # assign path to the variables dirP <- file.path("C:", "Users", "Parikshita", "Desktop", "Data Science", "SummerSemester") dirS <- file.path(dirP, "SummerPracticum", "USHotelCityRatingPlots") # cityRating for csv's path <- file.path(dirP, "Practicum-CSP 572", "TripAdvisorJson", "json") # extract the data from the files filename <- list.files(path, pattern = ".json", full.names = TRUE) # extracting all the files all_data <- lapply(filename, function(x) fromJSON(x)) # extracting cities and countries name all_address <- lapply(1:length(all_data), function(i) all_data[[i]]$HotelInfo$Address) all_doc <- lapply(1:length(all_address), function(x) htmlParse(all_address[x], asText = TRUE)) all_city <- lapply(1:length(all_doc), function(x) xpathSApply(all_doc[[x]], "//span[@property='v:locality']", xmlValue)) all_country <- lapply(1:length(all_doc), function(x) xpathSApply(all_doc[[x]], "//span[@property='v:country-name']", xmlValue)) all_country <- unique(subset(all_country, lapply(1:length(all_country), function(x) length(all_country[[x]])) > 0)) # extracting address of all the files plain.text <- lapply(1:length(all_doc), function(x) xpathSApply(all_doc[[x]], "//text()[not(ancestor::script)][not(ancestor::style)][not(ancestor::noscript)][not(ancestor::form)]", xmlValue)) pattern <- lapply(1:length(all_doc), function(x) which(plain.text[[x]] %in% c(" ", ", "))) address <- lapply(1:length(plain.text), function(i) { if(length(pattern[[i]]) != 0) { plain.text[[i]][-pattern[[i]]] } else { plain.text[[i]] <- plain.text[[i]] } }) # creating directory where output will be saved dir.create(file.path(dirS, "top5"), showWarnings = FALSE) # removing address of hotels which are not in United States country_filter <- which(sapply(lapply(1:length(address), function(x) any(which(address[[x]] %in% all_country))), isTRUE)) address <- address[-country_filter] # extracting rating & review date for all the hotels all_rating <- lapply(1:length(all_data), function(i) all_data[[i]]$Reviews$Ratings$Overall) rating_all <- all_rating[-country_filter] all_reviewDate <- lapply(1:length(all_data), function(i) all_data[[i]]$Reviews$Date) reviewDate_all <- all_reviewDate[-country_filter] all_hotel <- lapply(1:length(all_data), function(i) all_data[[i]]$HotelInfo$Name) hotels <- all_hotel[-country_filter] # extarcting us_cities us_cities <- all_city[-country_filter] cities <- unique(subset(us_cities, lapply(1:length(us_cities), function(x) length(us_cities[[x]])) > 0)) # getting only those indexes which have city name in them city_filter <- which(sapply(lapply(1:length(address) , function(x) any(which(address[[x]] %in% cities))), isTRUE)) city_name <- us_cities[city_filter] city_freq <- as.data.frame(table(unlist(city_name))) # subsetting cities with hotel count >= 30 city_freq <- as.character(subset(city_freq$Var1, city_freq$Freq >= 30)) city_30 <- which(sapply(lapply(1:length(address) , function(x) any(which(address[[x]] %in% city_freq))), isTRUE)) # using the index field to get rating, review date information rating_city <- rating_all[city_30] reviewDate_city <- reviewDate_all[city_30] hotelName <- hotels[city_30] city <- us_cities[city_30] # combining city name, review date and rating city_rating <- lapply(1:length(city_30), function(x) cbind(city[[x]], hotelName[[x]], reviewDate_city[[x]], rating_city[[x]])) # command to create flatlist in R by row city_rating <- do.call(rbind.data.frame, city_rating) # assigning names to columns colnames(city_rating) <- c("City", "Hotel", "Date", "Rating") # conversting datatype of Date column to Date and Rating to numeric city_rating$Date <- as.Date(city_rating$Date, "%B%d, %Y") # as.character(f) requires a "primitive lookup" to find the function as.character.factor() #, which is defined as as.numeric(levels(f))[f] city_rating$Rating <- as.numeric(levels(city_rating$Rating))[city_rating$Rating] # subsetting thr data set for review dates > year 2012 cr_2012 <- subset(city_rating, city_rating$Date >= "2012-01-01") cr_2012$Date <- format(as.Date(cr_2012$Date, "%Y-%m-%d"), "%Y-%m") city_avg <- ddply(cr_2012, c("City"), summarise, AvgRating = mean(Rating)) top_5 <- head(city_avg[order(city_avg$AvgRating, decreasing= T),], n = 5) # extracting average top 5 citis from city avg top5_filter <- which(sapply(lapply(1:nrow(city_avg) , function(x) any(which(city_avg[x,1] %in% as.character(top_5$City)))) , isTRUE)) city_avg_5 <- city_avg[top5_filter,] top5 <- as.character(top_5$City) for (i in 1:length(top5)) { city <- top5[i] indexes <- which(sapply(lapply(1:nrow(cr_2012) , function(x) any(which(cr_2012[x,"City"] %in% city))) , isTRUE)) city_5 <- cr_2012[indexes,] # aggregating data by hotel name hotel_5_avg <- ddply(city_5, c("Hotel", "Date"), summarise, AvgRating = mean(Rating)) hotel_5 <- ddply(city_5, c("Hotel"), summarise, AvgRating = mean(Rating)) top_5_hotel <- head(hotel_5[order(hotel_5$AvgRating, decreasing= T),], n = 5) filter_5_hotel <- which(sapply(lapply(1:nrow(hotel_5_avg) , function(x) any(which(hotel_5_avg[x,"Hotel"] %in% as.character(top_5_hotel$Hotel)))) , isTRUE)) hotel_5_avg <- hotel_5_avg[filter_5_hotel,] colnames(hotel_5_avg) <- c("Name", "Date", "AvgRating") city_5_avg <- ddply(city_5, c("City", "Date"), summarise, AvgRating = mean(Rating)) colnames(city_5_avg) <- c("Name", "Date", "AvgRating") df_5_avg <- do.call(rbind, list(city_5_avg, hotel_5_avg)) # setting dimensions for saving the plot file.name <- paste(city, "top5_Hotels.png", sep="_") savepath <- file.path(dirS, "top5", file.name) # writing the data into a csv (optional) #write.csv(avg_rating, paste(city , "USavg_rating.csv", sep = "_")) #len <- length(unique(df_5_avg$Name)) col <- palette(rev(rich.colors(6))) # generating plot df_5_plot <- ggplot(data = df_5_avg, aes(Date, AvgRating, group = Name, colour = Name)) + geom_line(size = 2) + scale_colour_manual(name = "City & Hotels", breaks = df_5_avg$Name, values = col) + xlab("Date") + ggtitle(paste("Average Rating of Top 5 Hotels -", city)) + theme(legend.justification = c(1, 0), legend.position = c(1, 0), legend.background = element_rect(colour = "black")) # save the plot ggsave(savepath, plot = df_5_plot, width = 12, height = 6, units = 'in') }

40b51f19aaa54eeb40ccf1b1f244b1ba3a9bc3d3

30b6762b3eefc492ef6e41715e6076aed74154a6

/vim/nostow/test/test_colorscheme/statistics.R

67b6d539d891e8f41bd64ca419701b0d8dd17cb7

[ "MIT" ]

permissive

viccuad/dotfiles

e7ac04e3c4671aa8f1f1d6e7fca5278e6c0ec458

fbd13a108c8201310af323ccda74aac23ec7a03a

refs/heads/master

2020-04-15T22:48:29.276891

2017-09-03T09:49:49

18,060,758

26

11

null

UTF-8

R

false

135

r

statistics.R

library(foreign) detach("package:datasets") x <- rnorm(1) if(x > 0) cat("Yes\n") else cat("No\n") x <- numeric(123) x <- NULL

7fd07f3ab621a634f0ab1fa02782ca58b8bad400

4f8c7cd717fd4a0a7b9b5d9e7dbd856b21ad8533

/v1/server.R

9d2bd33f591261f9a5d521be8cbbdfde50a0f856

[]

no_license

omker04/LinkedIn

c9b7803a05784e9aaf076724c25cc1e5ba5816bd

54b80a68f3cebc79682dbc086f110efac4d35af7

refs/heads/master

2020-03-14T22:02:43.632143

2018-05-02T07:08:54

131,811,968

0

null

UTF-8

R

false

6,203

r

server.R

shinyServer(function(input, output, session) { ##### Login Signup #### observe({ if (input$user_name != '' & input$user_ID %like% 'GTS' & nchar(input$user_ID) == 7 & input$agree) { enable('login') enable('signup') } else { disable('login') disable('signup') } }) observeEvent(input$login, { closeAlert(session, 'warning_alert') if (!python.get(paste0('"', input$user_ID, '" in person.keys()'))) { createAlert(session, anchorId = 'danger', alertId = 'warning_alert', title = 'User Not Registered.', content = 'You have not yet signed up. Please sign up now.', style = 'warning', dismiss = TRUE) } else { if (python.call('add_new_user', input$user_ID, input$user_name) %like% 'match') { createAlert(session, anchorId = 'warning', alertId = 'warning_alert', title = 'Incorrect Entry', content = 'Input GTS_ID and Name doesnt match', style = 'danger', dismiss = TRUE) } else { shinyjs::hide(id = 'login_page', anim = TRUE) shinyjs::show(id = 'search_bar', anim = TRUE) shinyjs::show(id = 'self_profile', anim = TRUE) # shinyjs::show(id = 'managers_profile', anim = TRUE) # shinyjs::show(id = 'connection_profile', anim = TRUE) # shinyjs::show(id = 'direct_report_profile', anim = TRUE) # hide(id = 'self_profile', anim = TRUE) #shinyjs::show(id = 'managers_profile', anim = TRUE) } } }) # observeEvent(input$signup, { # closeAlert(session, 'warning_alert') # if (python.call('add_new_user', input$user_ID, input$user_name) %like% 'already') { # createAlert(session, anchorId = 'warning', alertId = 'warning_alert', # title = 'User Already Registered.', # content = 'You have already signed up. Try logging in.', # style = 'warning', dismiss = TRUE) # } else { # shinyjs::hide(id = 'login_page', anim = TRUE) # shinyjs::show(id = 'search_bar', anim = TRUE) # shinyjs::show(id = 'self_profile', anim = TRUE) # } # }) # current_click <- reactiveValues(type = NULL, id = NULL, self = 'Yes', anchor = NULL, manager = NULL) anchor <- reactive({ print(self()) if (!is.null(self()$id)) { current_click$anchor <- self()$id } else { current_click$anchor <- input$user_ID } manager = python.get(paste0('person[', char(current_click$anchor), ']["Manager"]')) return(list('anchor' = current_click$anchor, 'manager' = manager)) }) # observe({ # self <<- callModule(self_profile_, id = 'self_page', self = input$user_ID, is_manager = python.get(paste0(char(input$user_ID), ' in manager.keys()'))) # anchor_manager <<- callModule(manager_profile_, id = 'manager', manager = anchor()$manager) # anchor_connections <<- callModule(associate_names_, id = 'Conn', Uid = anchor()$anchor, type = 'connections') # anchor_directs <<- callModule(associate_names_, id = 'DR', Uid = anchor()$anchor, type = 'DR') # }) # # observe({ # if (is.null(self()$id)) { # current_click$anchor <- input$user_ID # } else { # current_click$anchor <- self()$id # } # current_click$self <- '' # print(self()) # print(paste('anchor', anchor())) # }) # # observe({ # print(anchor()) # }) # callModule(self_profile_, id = 'self_page', self = input$user_ID, is_manager = python.get(paste0(char(input$user_ID), ' in manager.keys()'))) # # # observe({ # if (!is.null(s()$id)) { # current_click$id <- s()$id # current_click$type <- s()$type # current_click$self <- 'No' # } # }) # # new <- eventReactive(current_click, { # if (current_click$type == 'Manager') { # show(id = 'managers_profile', anim = TRUE) # hide(id = 'connection_profile', anim = TRUE) # hide(id = 'direct_report_profile', anim = TRUE) # hide(id = 'self_profile', anim = TRUE) # s1 <- callModule(manager_profile_, id = 'manager', manager = s()$id) # } # if (current_click$type == 'Connection') { # hide(id = 'managers_profile', anim = TRUE) # show(id = 'connection_profile', anim = TRUE) # hide(id = 'direct_report_profile', anim = TRUE) # hide(id = 'self_profile', anim = TRUE) # s1 <- callModule(associate_names_, id = 'Conn', Uid = s()$id, type = 'connections') # } # if (current_click$type == 'Directs') { # hide(id = 'managers_profile', anim = TRUE) # hide(id = 'connection_profile', anim = TRUE) # show(id = 'direct_report_profile', anim = TRUE) # hide(id = 'self_profile', anim = TRUE) # s1 <- callModule(associate_names_, id = 'DR', Uid = s()$id, type = 'DR') # } # return(s1) # }) # # # change_current_value <- eventReactive(new(), { # s <- new() # print('esss') # print(s()) # current_click$id <- s()$id # current_click$type <- s()$type # return(new()) # }) # # observe({ # print(paste('observe', current_click$type)) # print(paste('observe', current_click$id)) # if (!is.null(current_click$type) && !is.null(current_click$id) && current_click$self == 'No') { # s1 <- new() # change_current_value() # } else { # print('else') # } # }) # observe({ # print('observe') # print(current_click$id) # print(current_click$type) # print(type()) # print(id()) # }) })

b60a0b789623b93d91b469c5461c68fb2b0cdf23

20a14c8e7e54196ec8fe97157b52a8a7789ab349

/wrangling/joining_data.R

0ed0194101d25826a3a7a11f0ed35f49816245b4

[]

no_license

azbenoit/EDX

f31235250f69d331ff675087fd3dc7626ee27321

5bd741b10ea0cef4542fb6d162930937fa321048

refs/heads/master

2022-11-22T13:55:54.653207

2020-07-30T22:32:26

261,275,799

0

null

UTF-8

R

false

609

r

joining_data.R

library(tidyverse) library(Lahman) top <- Batting %>% filter(yearID == 2016) %>% arrange(desc(HR)) %>% # arrange by descending HR count slice(1:10) # take entries 1-10 top %>% as_tibble() Master %>% as_tibble() top_names <- top %>% left_join(Master, "playerID") %>% select(playerID, nameFirst, nameLast, HR) top_salary <- Salaries %>% filter(yearID == 2016) %>% right_join(top_names) %>% select(nameFirst, nameLast, teamID, HR, salary) Awards <- AwardsPlayers %>% filter(yearID == 2016) non_top_awards <- anti_join(Awards, top_names) nrow(semi_join(Master, non_top_awards))

2f15b74feb8bd89edbb1efd047ef733e3ff2c517

afd08df66beef2f756ead9d2af095b5a6f0aee8c

/R/gather.PharmacoSet.R

f51a809dca0fe0e56d511ae41cddccf417b94512

[]

no_license

chapmandu2/tidyMultiAssay

05ee00642684c5813cb7d05fdfc65bd62ba72993

59d1388c87e8ce68f5b52e38315fa2b5bf37e5ad

refs/heads/master

2021-01-23T03:37:31.209263

2017-01-06T08:54:27

64,685,249

0

null

UTF-8

R

false

2,404

r

gather.PharmacoSet.R

#' @title #' Convert a PharmacoSet object into tidy format #' #' @description #' Convert a \linkS4class{PharmacoSet} object into tidy format. This is essentially a wrapper function #' for \code{\link{gather_response.PharmacoSet}} and \code{\link{gather_assay.PharmacoSet}} #' #' @param x PharmacoSet object #' @param sample_ids A vector of sample ids. Default is NULL (don't filter on sample id) #' @param gene_ids A vector of gene ids. Default is NULL (don't filter on gene id) #' @param resp_ids A vector of response ids. Default is NULL (don't filter on response id) #' @param sample_col Name of the column in the pData data frame to use for filtering on sample id #' @param gene_col Name of the column in the rowData data frame to use for filtering on gene id #' @param resp_col Response variable to retrieve #' @param data_types Names of the components of the PharmacoSet object to gather #' @param resp_df Data frame to process response data from (rather than PharmcaGx object) #' #' @return a data frame in tall_df format #' @export gather.PharmacoSet #' #' @examples #' data('CCLEsmall', package='PharmacoGx') #' #' gather.PharmacoSet(CCLEsmall, sample_ids=c('143B', '23132-87'), gene_ids=c('BRAF', 'EGFR'), #' data_types=c('rna', 'mutation'), gene_col=c('Symbol', 'Symbol')) #' #' gather.PharmacoSet(CCLEsmall, sample_ids=c('CHL-1', 'SW1573'), gene_ids=c('BRAF', 'EGFR'), #' resp_ids=c("AEW541","Nilotinib","PHA-665752","lapatinib"), #' data_types=c('rna', 'mutation'), gene_col=c('Symbol', 'Symbol'), resp_col='ic50_published') #' #' #' gather.PharmacoSet <- function(x, sample_ids=NULL, gene_ids=NULL, resp_ids=NULL, sample_col='cellid', gene_col=c('Symbol', 'gene_name'), resp_col='ic50_published', data_types=c('rna', 'mutation'), resp_df=NULL) { stopifnot(length(gene_col) == length(data_types)) genetic_data <- purrr::map2(data_types, gene_col, function(z1, z2) { gather_assay.PharmacoSet(x, sample_ids=sample_ids, gene_ids=gene_ids, data_type=z1, sample_col=sample_col, gene_col=z2) }) %>% dplyr::bind_rows() if(is.null(resp_ids)) { return(genetic_data) } else { drug_data <- gather_response.PharmacoSet(x, sample_ids=sample_ids, resp_ids=resp_ids, resp_col=resp_col) dplyr::bind_rows(genetic_data, drug_data) } }

df60569aaebf7bdcd5a36db1cc01d72d38d7f1e8

28d8c54ff161be6c0123748935c5c88124fea7b8

/data-raw/create_test_data.R

da35fae9bacf5c757c3ec0773c33eb1a8c2623bc

[]

no_license

lawinslow/LAGOSNE

1b1fd7b7574a1e5cf9c87b6c28d30c3008255b41

b68fff7dc4862e90b5f178a545e46e6ecf5f97fb

refs/heads/master

2020-03-08T16:48:32.275788

2018-03-26T19:33:06

null

0

null

UTF-8

R

false

1,345

r

create_test_data.R

# Create a testing dataset from compiled LAGOS database rds # LAGOS:::lagos_compile("1.087.1", "rds") dt <- LAGOSNE::lagosne_load("1.087.1") dt_subset <- purrr::map(dt, function(x) head(x, n = 2)) # manually add rows for lake_info test dt_subset$state <- rbind(dt_subset$state, dt$state[which(dt$state$state == "MA"),]) # names(dt_subset) exclude_names <- c("lakes4ha.buffer100m", "lakes4ha.buffer100m.lulc", "lakes4ha.buffer500m.conn", "lakes4ha.buffer500m.lulc", "lakes4ha.buffer500m", "hu8", "hu8.chag", "hu8.conn", "hu8.lulc", "hu12", "hu12.chag", "hu12.conn", "hu12.lulc", "name", "type", "variables", "observations", "identifier", "group", "county", "county.chag", "county.conn", "county.lulc") dt_subset <- dt_subset[!(names(dt_subset) %in% exclude_names)] saveRDS(dt_subset, "tests/testthat/lagos_test_subset.rds") lg_subset <- dt_subset devtools::use_data(lg_subset, overwrite = TRUE)

04ffcb4963e596f4708c7cf3f9eefbb1b6331491

e85a80ba4df857d2cccc3884a782be50833ea7f0

/05.transcription_factors/04.expression_correlation/two_gene_correlation_all.R

7aa8ecc6c28242b791deb1f1694e0b01fb0497db

[]

no_license

zengxiaofei/monolignol-biosynthesis

4c9ffc2cc9b69c4056a114dadb8c2de16e73fddb

c16862fc70bf32aa679eb792cb973f3f0c5c1e28

refs/heads/master

2023-01-11T17:45:54.555382

2020-11-05T03:06:30

272,724,214

2

0

null

UTF-8

R

false

801

r

two_gene_correlation_all.R

#!/usr/bin/env Rscript args <- commandArgs(T) library(ggplot2) args <- commandArgs(T) gene1 <- args[1] gene2 <- args[2] md <- read.table("RSEM.gene.TMM.EXPR.annotated.CCR1_2.matrix", header=T) tmd <- as.data.frame(t(md)) sp <- cor.test(tmd[[gene1]], tmd[[gene2]], method="spearman") pval <- sp$p.value rho <- sp$estimate # print to screen anno <- paste("Spearman's rho = ", rho, ", p-value = ", pval, sep="") anno pval <- signif(pval, 2) rho <- signif(rho, 2) anno <- paste("Spearman's rho = ", rho, ", p-value = ", pval, sep="") pdf(paste(paste(gene1, gene2, sep="_"), ".pdf", sep=""), width=5, height=5) ggplot(tmd, aes_string(x=gene1, y=gene2)) + geom_point() + geom_smooth() + annotate("text", -Inf, Inf, label=anno, hjust=-0.5, vjust=2) + theme_bw() dev.off()

0b2452b69422c17f681dc4f78ac9b6ba6a9a7f9b

3a94e79e085df45b5bcf25e7778a26daee82ad53

/man/plot_risk_premium_en.Rd

37085ce4638d37e3e22deef31bdaeb11c6cc8771

[ "MIT" ]

permissive

cran/ceRtainty

bac5e856ec90963f4d72f9a2d0abbc50fdff6202

f43c62da68d3c6bb6ae256c8c87cd4186d60b866

refs/heads/master

2020-12-21T21:32:37.227829

2019-06-14T13:40:03

236,569,284

0

null

UTF-8

R

false

true

729

rd

plot_risk_premium_en.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_risk_premium_en.R \name{plot_risk_premium_en} \alias{plot_risk_premium_en} \title{Plot of the Risk Premium values using Exponential Negative Utility Function} \usage{ plot_risk_premium_en(data, rac_ini, rac_fin, rac_len) } \arguments{ \item{data}{\code{data.frame} of CE computed by Exponential Negative function} \item{rac_ini}{Initial RAC values used in the CE computation} \item{rac_fin}{Final RAC values used in the CE computation} \item{rac_len}{Length of the RAC vector used in the CE computation} } \value{ Plot object } \description{ Plot of the Risk Premium values using Exponential Negative Utility Function }

7393dcba81010504c594e5a509d8ef8e7b0d8076

ef4c19bbcd2bb4915aa17654c7760fe0e6e14e5c

/man/prostate.Rd

0f383a8f26046225f4edb524fd6ae85c97481c61

[]

no_license

3shmawei/faraway

ee4f1442e5b28de2c4e436c57a7c34f2f3065a2b

dc770de1a2d55d31a217e14c911495644ea17b3f

refs/heads/master

2020-03-23T18:10:35.105916

2016-02-15T15:07:03

null

0

null

UTF-8

R

false

923

rd

prostate.Rd

\name{prostate} \alias{prostate} \title{Prostate cancer surgery} \description{The \code{prostate} data frame has 97 rows and 9 columns. A study on 97 men with prostate cancer who were due to receive a radical prostatectomy. } \usage{ data(prostate) } \format{ This data frame contains the following columns: \describe{ \item{\code{lcavol}}{ log(cancer volume) } \item{\code{lweight}}{ log(prostate weight) } \item{\code{age}}{ age } \item{\code{lbph}}{ log(benign prostatic hyperplasia amount) } \item{\code{svi}}{ seminal vesicle invasion } \item{\code{lcp}}{ log(capsular penetration) } \item{\code{gleason}}{ Gleason score } \item{\code{pgg45}}{ percentage Gleason scores 4 or 5 } \item{\code{lpsa}}{ log(prostate specific antigen) } }} \source{Andrews DF and Herzberg AM (1985): Data. New York: Springer-Verlag} \keyword{datasets}

5c0fb9d13e862293229b5a771de0a0bd56ea1213

285d35f03c59b3eca8a8639e30009cc020ee7fbf

/00-metadata/HemisphereChart.R

5087979dd93f066f0c8b2c7c66bbaa35a70c6ecd

[]

no_license

octopode/cteno-lipids-2021

5a039c0506111cc3bbd6e689bd7bf02ac585dc1c

b2c66cf59149e5c79918bdcc317303a23e59e338

refs/heads/master

2023-03-26T07:59:36.219753

2021-03-12T18:19:25

289,368,885

0

null

UTF-8

R

false

634

r

HemisphereChart.R

library(tidyverse) library(ggrepel) library(ggpubr) # plot that lonely northern hemisphere land <- map_data("world") %>% filter( (region %in% c( "Greenland", "Iceland", "Canada", "USA", "Mexico", "Guatemala", "Belize", "Honduras", "El Salvador", "Nicaragua", "Costa Rica" ) ) | (subregion %in% c( "Svalbard" ) ) ) %>% filter(long < 75) ggplot() + geom_polygon( data = land, aes(x=long, y=lat, group=group), fill = "white" ) + coord_map(projection = "ortho", orientation = c(45, -120, 0))

8737b7ad3adc7d8cfc14ef229f68bb1ec4d795cd

9aafde089eb3d8bba05aec912e61fbd9fb84bd49

/codeml_files/newick_trees_processed/9043_0/rinput.R

5cd176e81ccc82b2fffc244bc97c6c47ff3460e3

[]

no_license

DaniBoo/cyanobacteria_project

6a816bb0ccf285842b61bfd3612c176f5877a1fb

be08ff723284b0c38f9c758d3e250c664bbfbf3b

refs/heads/master

2021-01-25T05:28:00.686474

2013-03-23T15:09:39

null

0

null

UTF-8

R

false

135

r

rinput.R

library(ape) testtree <- read.tree("9043_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="9043_0_unrooted.txt")

20c11f00e8e6b423ee86a89ba4cf104cb88724dc

8ad96eb29e6839e486a6d2cd72a4a93bab45d57d

/script/subscript/calculatePP.R

20a171bb48bd5adcb6c2b51f864f979dbb934aa3

[ "MIT" ]

permissive

hoangtn/DECO

4ca3049875db954eca9044397dfe59fda8ae638b

864bb0e9ac163717739536c37688479a05e0a826

refs/heads/master

2023-08-06T14:37:53.698455

2021-09-24T00:13:09

272,843,394

2

0

null

UTF-8

R

false

3,081

r

calculatePP.R

##Calculate posterior probabilities calculatePP <- function(pars, caseData = NULL, controlData = NULL, dnData = NULL, mutData = NULL, geneName, geneSet){ outData <- data.frame(geneName, geneSet) colnames(outData) <- c("GENE", paste0("GeneSet", 1:dim(geneSet)[2])) if (!is.null(dnData)) outData <- cbind(outData, dnData) if (!is.null(mutData)) outData <- cbind(outData, mutData) if (!is.null(caseData)) outData <- cbind(outData, caseData) if (!is.null(controlData)) outData <- cbind(outData, controlData) ##Calculate pi sumGeneSet = rep(pars$alphaValue[1], length(geneName)) for (j in 1:dim(geneSet)[2]) sumGeneSet = sumGeneSet + pars$alphaValue[j+1]*geneSet[, j] pi0 = exp(sumGeneSet)/(1 + exp(sumGeneSet)) bfAll <- rep(1, dim(outData)[1]) if ( length(pars$gammaMeanDN) == 0) { message("No parameters for de novo data; therefore, these categories are not calculated in this step.\n") } else { bfDN <- matrix(1, nrow = dim(dnData)[1], ncol = dim(dnData)[2]) ##De novo bayes factors for (j2 in 1:dim(bfDN)[2]) { e.hyperGammaMeanDN <- pars$gammaMeanDN[j2] e.hyperBetaDN <- pars$betaDN[j2] e.bf <- bayes.factor.denovo(x = dnData[, j2], N = pars$nfamily[j2], mu = mutData[, j2], gamma.mean = e.hyperGammaMeanDN, beta = e.hyperBetaDN) bfDN[, j2] <- e.bf } bfAll <- bfAll*apply(bfDN, 1, prod) } if (length(pars$gammaMeanCC) == 0) { message("No parameters for case-control data; therefore, these categories are not calculated in this step.\n") } else { bfCC <- matrix(1, ncol = dim(caseData)[2], nrow = dim(caseData)[1]) for (cc3 in 1:dim(bfCC)[2]){ e.hyperGammaMeanCC <- pars$gammaMeanCC[cc3] e.hyperBetaCC <- pars$betaCC[cc3] e.nu <- 200 t.case <- caseData[, cc3] t.control <- controlData[, cc3] e.rho <- e.nu*mean(t.case + t.control)/(pars$ncase[cc3] + pars$ncontrol[cc3]) e.bf <- BayesFactorCC3(Nsample = list(ca = pars$ncase[cc3], cn = pars$ncontrol[cc3]), x.case = t.case, x.control = t.control, gamma.meanCC = e.hyperGammaMeanCC, betaCC = e.hyperBetaCC, rhoCC = e.rho, nuCC = e.nu) # e.bf0 <- bayes.factor.CC(x bfCC[, cc3] <- e.bf } bfAll <- bfAll*apply(bfCC, 1, prod) } outData[, 'pi0'] <- pi0 outData$BF <- bfAll outData[, 'PP'] <- pi0*bfAll/(pi0*bfAll + (1 - pi0)) # outData <- outData[order(-outData$BF),] # outData$qvalue <- Bayesian.FDR(outData$BF, 1 - pars$pi0)$FDR return(outData) }

cadbae3e6bf8fc2ee3a6744caf5f50dee59d3a0e

087f9b379877bc87466f8b24d97be9632d46ed2f

/DataCode.R

feaad97124a726e3c04e454afc83540b70ace391

[]

no_license

Soma586/Data

e407904a7e382ec81bbd123be15bc51b07bec372

a469b2eba7d7f86ba455b3bce396074008eee10f

refs/heads/master

2020-03-29T08:08:47.364818

2018-12-13T16:56:47

149,696,017

0

null

UTF-8

R

false

322

r

DataCode.R

pro <-read.csv("FHVData.csv") x = as.numeric(pro$American) mean(x) sd(x) y = as.numeric(pro$Uber) mean(y) sd(y) z = as.numeric(pro$Highclass) mean(z) sd(z) hist(x, main = "Histogram of Pro$American") plot(z, main = "Plot of high class", y = "frequency") plot(y, main = "Plot of Pro$Uber", ylab = "frequency")

b3622870f9dc95f92c64e4e4a1e53c3ee8bc2285

aac4e0c44de4aa89ca0081f2de4d113897ee9c65

/man/scotland_births.Rd

ac74221b97d15148d380cf0d804f2f224f8b3b3c

[]

no_license

TKosfeld/fosdata

c2c39db8cc7a62ebfca48ba87b30c4ebb9a3eee9

7353861f61ae094c474a8e1a4d698f4919333ba6

refs/heads/master

2023-08-01T11:53:33.944662

2021-09-15T04:12:52

null

0

null

UTF-8

R

false

true

684

rd

scotland_births.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scotland_births.R \docType{data} \name{scotland_births} \alias{scotland_births} \title{Births in Scotland} \format{ A data frame with 45 observations of 76 variables. \describe{ \item{age}{Age of the mother, in years} \item{x****}{Year of birth of baby; 75 columns of this type from 1945-2019.} } } \source{ National Records of Scotland, https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/births/births-time-series-data } \usage{ scotland_births } \description{ The number of babies born in Scotland from 1945-2019, by age of the mother. } \keyword{datasets}

dd412b8fab20ef3b30e9e8ea6247f880dfbe3cb5

504f277ca5e3d508ff047d24944ab6550ed8e431

/pairwiseAcf.R

4232dc5909e0f6450698d95591deece108fa0d5f

[]

no_license

srivathsesh/DengueAI

ca99c032920acb5bd17fb025f8c45d23c6d2c99e

54cbe790404b72ee94bdbe79996a708c06373ff2

refs/heads/master

2020-03-18T23:48:24.731364

2018-06-11T19:29:24

135,429,683

0

null

UTF-8

R

false

1,263

r

pairwiseAcf.R

#**************************************************************** # Optimal lags decision #**************************************************************** pairwiseAcf <- function(x, y, varnames) { bestlag <- function(p) { # browser() cf.df <- forecast::Ccf(y[,1], x[, p], plot = T,lag.max = 10) data.frame( predictor = p, lags = which.max(abs(cf.df[["acf"]][11:21, 1, 1]))-1, correlation = abs(cf.df[["acf"]][11:21, 1, 1])[which.max(abs(cf.df[["acf"]][11:21, 1, 1]))] ) } varnames %>% map_df(bestlag) } varnames <- c('reanalysis_relative_humidity_percent', 'reanalysis_precip_amt_kg_per_m2', 'reanalysis_max_air_temp_k', "ndvi_ne","ndvi_nw", "ndvi_se", "ndvi_sw", "reanalysis_sat_precip_amt_mm", "reanalysis_tdtr_k") lagstouse <- pairwiseAcf(ts(x.train,start = c(1990,18), frequency = 52),ts(y,start = c(1990,18), frequency = 52),varnames) #lagCcf(tsTotalcasesSj,ts(trainImputedSj$reanalysis_relative_humidity_percent,start = c(1990,18), frequency = 52)) lagstouse <- pairwiseAcf(ts(x.train.diff,start = c(1990,19), frequency = 52),ts(y.boxcox.diff,start = c(1990,18), frequency = 52),varnames)

6838502364ef51da33704d5fbf365525389fcb1c

b5ebe52bb3594b62395ff452235fed8c8be477b8

/tsa_project/main/R/latest.r

91f7ba9c8bed881fee5da72b671c7dc9b2a69772

[]

no_license

Steven-Sakurai/Math-Potential-Well

1e69992ca867d85d05af756ca471cf64627a9f20

01c5bdf1964b363b4455c6da4c9879aff1b66846

refs/heads/master

2021-01-20T01:17:16.833077

2017-06-18T15:31:26

89,248,955

0

null

UTF-8

R

false

743

r

latest.r

library(curl) library(forecast) library(TSA) library(rugarch) tmpf <- tempfile() curl_download("http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.5.0.0.monthly_ns_avg.txt", tmpf) gtemp <- read.table(tmpf)[, 1:2] gtemp = as.numeric(gtemp$V2) mytemp = gtemp[600:1980] plot.ts(mytemp) acf(mytemp) pacf(mytemp) m1 = auto.arima(mytemp, seasonal = TRUE) m2 = arima(mytemp, order = c(3, 1, 1), seasonal = list(order = c(1, 0, 1), period = 24)) res2 = residuals(m2) acf(res2) m3 = arfima(mytemp) res3 = residuals(m3) res4 = residuals(arima(res3, seasonal = list(order = c(1, 0, 1), period = 24))) # seasonal adjustment atemp = diff(mytemp, lag = 24) m5 = auto.arima(atemp) res5 = residuals(m5) acf(res5) pacf(res5)

ce65d4d9c7ce818c2de072fe0b4ea237608bc947

28767234045ae57efe03a7d9949f75c01388ce14

/ImageCollection/02_metadata.R

998b055928488969db45602c3678d62f59387544

[ "MIT" ]

permissive

yangxhcaf/rgee-examples

596ef77d80b33be402e1c4e5bbb76dba0a135fa6

9e2dc8c18a87ebf2bae9666830f9018a9ce3df1f

refs/heads/master

2021-02-12T04:39:26.594480

2020-02-29T09:41:16

null

0

null

UTF-8

R

false

1,133

r

02_metadata.R

library(rgee) ee_Initialize() # Load a Landsat 8 ImageCollection for a single path-row. collection = ee$ImageCollection('LANDSAT/LC08/C01/T1_TOA')$ filter(ee$Filter$eq('WRS_PATH', 44))$ filter(ee$Filter$eq('WRS_ROW', 34))$ filterDate('2014-03-01', '2014-08-01') print('Collection: ', collection$getInfo()) # Get the number of images. count = collection$size() print('Count: ', count$getInfo()) # Get the date range of images in the collection. range = collection$reduceColumns(ee$Reducer$minMax(), list("system:time_start")) print('Date range: ', ee$Date(range$get('min'))$getInfo(), ee$Date(range$get('max'))$getInfo()) # Get statistics for a property of the images in the collection. sunStats = collection$aggregate_stats('SUN_ELEVATION') print('Sun elevation statistics: ', sunStats$getInfo()) # Sort by a cloud cover property, get the least cloudy image. image = ee$Image(collection$sort('CLOUD_COVER')$first()) print('Least cloudy image: ', image$getInfo()) # Limit the collection to the 10 most recent images. recent = collection$sort('system:time_start', FALSE)$limit(10) print('Recent images: ', recent$getInfo())

849b103d1d49b847a2de93b097945d1085c94f95

4ee386eaa3017a4d8a83ff902ef45ef1572c92e9

/R help/XML/extract_XML_attributes.r

7d963c65f7ebb1fc70b4dc71b66370067d11be58

[]

no_license

aayush26/My_Notes

3a733fad0cfd34495530d77082c1b96dff8a626e

5c53ce2bb5fef4c328c4118e9c4ab22b5c8a3068

refs/heads/master

2021-01-17T13:36:09.202631

2019-11-11T13:51:24

39,803,397

0

null

UTF-8

R

false

379

r

extract_XML_attributes.r

library(XML) file <- xmlTreeParse("Tags.xml",useInternalNodes = TRUE) xmltop = xmlRoot(file) #access top-node #xml tag attribute extract Id <- xpathApply(xmltop,'//row',xmlGetAttr,"Id") Count <- xpathApply(xmltop,'//row',xmlGetAttr,"Count") ExcerptPostId <- xpathApply(xmltop,'//row',xmlGetAttr,"ExcerptPostId") WikiPostId <- xpathApply(xmltop,'//row',xmlGetAttr,"WikiPostId")

05c294e6db47514cba6270b211ea95c92faaf77a

5c0ad661c78c7d51fbcf73ab8d1f8459c76e78b2

/CH9#4.R

d77e15c150c0028db060649163712d714cc8df9b

[]

no_license

nickblink/Intro.-to-Stat.-Learning-Codes

38b00eb44347e299527f74294331bc539eda5ba7

7b82a911ea5fb74107412e7a90cbc9fe90213003

refs/heads/master

2020-04-06T20:57:54.843322

2018-11-16T00:10:58

157,788,682

0

null

UTF-8

R

false

3,605

r

CH9#4.R

# I guess all the previous stuff got deleted. Well screw it I'm gonna # continue with 8. Whose gonna come back and look at this anyway? library(ISLR) D = Auto D$mpg.level = ifelse(D$mpg>median(D$mpg),1,0) D$mpg.level = as.factor(D$mpg.level) D = D[,setdiff(Colnames(D),'mpg')] library(e1071) tune.out = tune(svm,mpg.level~.-mpg,data=D,kernel="linear", ranges = list(cost=c(0.001,0.01,0.1,1,5,10,100))) summary(tune.out) # error is pretty similar for .1, 1, 5, but is the lowest with cost=1 (error =.095) # that's a pretty good error. = 90.5% classification accuracy. svm.lin = svm(mpg.level ~., data = D, kernel='linear',cost=0.01) tune.rad = tune(svm,mpg.level~.-mpg,data=D, kernel="radial", ranges = list(cost = c(.01,1,10,100), gamma = c(0.5, 1, 2, 3, 4))) summary(tune.rad) # min at cost = 1, gamma = 0.5. Error = .063. # What's the role of gamma again? This will # be a tight fit. Idk now. If I end up needing to use svms I should look # into this more, but for now I will pass. svm.rad = svm(mpg.level~.-mpg,data=D, kernel="radial",cost=10,gamma=0.5) tune.poly = tune(svm,mpg.level~.-mpg,data=D, kernel="polynomial", ranges = list(cost=c(.01, 1, 10, 100), degree = c(1,2,3,4))) summary(tune.poly) # bad all around. It did best with degree 1, aka linear. Obvi similar # results to linear (though slightly diff because of randomness). svm.poly = svm(mpg.level~.-mpg,data=D, kernel="polynomial",degree=2,cost=1) par(mfrow=c(1,3)) plot(svm.lin,D) plot(svm.rad,D) plot(svm.poly,D) ##### rm(list=ls()) library(ISLR) train.sample = sample(1070,800) train = OJ[train.sample,] test = OJ[-train.sample,] svm.fit = svm(Purchase~.,data=train,cost=0.01,kernel='linear') summary(svm.fit) mean(train$Purchase==predict(svm.fit,train)) # 0.82 accuracy - weirdly lower than test, though they're # close enough to be random. I guess the linear # model is unlikely to overfit, esp. with all of this data. mean(test$Purchase==predict(svm.fit,test)) # 0.84 accuracy. It's aite. library(e1071) tune.out = tune(svm, Purchase~.,data=train,kernel = 'linear', ranges = list(cost = c(0.01,0.1,1,5,10))) # (e) no point in this one - computing new training and test error rates tune.out = tune(svm, Purchase~.,data=train,kernel = 'radial', ranges = list(gamma = c(0.5,1,2,3,4), cost = c(0.01,0.1,1,10))) # best has error ~ 0.21 radial.best = svm(Purchase~.,data=train,kernel = 'radial', gamma = 0.5, cost = 1) radial.worst = svm(Purchase~.,data=train,kernel = 'radial', gamma = 3, cost = .1) mean(test$Purchase==predict(radial.best,test)) # 0.82 mean(test$Purchase==predict(radial.worst,test)) # 0.64. No bueno tune.out = tune(svm, Purchase~.,data=train,kernel = 'polynomial', ranges = list(degree = c(1,2,3,4), cost = c(0.01,0.1,1,10))) poly.best = svm(Purchase~.,data=train,kernel = 'polynomial', degree = 1, cost = 1) poly.worst = svm(Purchase~.,data=train,kernel = 'polynomial', degree = 1, cost = .01) mean(test$Purchase==predict(poly.best,test)) # 0.84 mean(test$Purchase==predict(poly.worst,test)) # 0.82 # Again we have a linear boundary is the best. Shows that it # all depends on the data set. Radial does not do as well # on this data set. I am really tired. I just want to go home # and nap.

3b79f8460e26d2dd2087b711eb675ac2da845561

821045753bd189beebe239e346b1e3183be415b4

/tests/test_Bike_counter.R

e6bbc630a26e579751ffd060059ecce2456955f5

[ "MIT" ]

permissive

flynn-d/BikeCount

9688bf4580bb117b7e44d6f5d1c3602e2011d8d7

4befb007d703cededb12cf72017f506737fff1c1

refs/heads/master

2021-06-18T11:43:29.411162

2020-12-06T02:21:19

142,794,979

0

1

MIT

2020-12-06T02:21:20

2018-07-29T20:56:30

R

UTF-8

R

false

265

r

test_Bike_counter.R

# Testing the Bike Counter code # Building off of these resources # http://r-pkgs.had.co.nz/tests.html # https://dzone.com/articles/example-unit-testing-r-code # https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf source('Bike_counter_get.R')

98ec6bd0644e56997af91c37d44a6c7c22f23c55

dbe5615e3de9440d1e2ba4ee362feb6a457229b8

/Plot3_code.R

53e8e354aeb156a8ccdd35c9e189779c15c57d25

[]

no_license

vucko83/ExData_Plotting1

f3d160643d7c16d870c0c496e808526b36371f19

2de3a8209c213d292b13083cb466ed5a7dc0b7b6

refs/heads/master

2021-01-17T10:27:04.671695

2014-10-12T22:28:41

null

0

null

UTF-8

R

false

1,062

r

Plot3_code.R

power<-read.table("household_power_consumption.txt",header=T,sep=";") power$Date<-as.Date(power$Date, format="%d/%m/%Y") subpower <-power[(power$Date=="2007-02-01") | (power$Date=="2007-02-02"),] subpower$Global_active_power <- as.numeric(as.character(subpower$Global_active_power)) subpower<- transform(subpower, timestamp=as.POSIXct(paste(Date, Time)), "%d/%m/%Y %H:%M:%S") subpower$Sub_metering_1 <- as.numeric(as.character(subpower$Sub_metering_1)) subpower$Sub_metering_2 <- as.numeric(as.character(subpower$Sub_metering_2)) subpower$Sub_metering_3 <- as.numeric(as.character(subpower$Sub_metering_3)) png(filename = "plot3.png", width = 480, height = 480, units = "px", bg = "transparent") plot(subpower$timestamp,subpower$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering") lines(subpower$timestamp,subpower$Sub_metering_2, type="l", col="red") lines(subpower$timestamp,subpower$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"),lwd=1) dev.off()

4388932ec223f5d1fb352602c06e26e7c2e6341c

a462a24ff937e151e8151f3a1bdc9c3714b12c0e

/creation/create_scripts_azzini.R

34239fe133301c2e9933512c11cf198e5ca2c61e

[]

no_license

noeliarico/kemeny

b4cbcac57203237769252de2c50ce959aa4ca50e

50819f8bf0d19fb29a0b5c6d2ee031e8a811497d

refs/heads/main

2023-03-29T14:36:37.931286

2023-03-16T09:04:12

330,797,494

0

null

UTF-8

R

false

5,243

r

create_scripts_azzini.R

scripts_azzini <- function(pors, n, file_name, rep = 0, type = "") { imports <- c(" import pandas as pd import numpy as np from kemeny.scf.borda import * from kemeny.scf.condorcet import * import kemeny.scf.distance as dist import kemeny.scf.initialization as init import kemeny.azzinimunda.azzinimunda0 as am0 import kemeny.azzinimunda.azzinimunda1 as am1 import kemeny.azzinimunda.azzinimunda2 as am2 import kemeny.azzinimunda.azzinimunda3 as am3 import time ") if(rep == 0) { out <- c(imports, paste0(' results',n,' = np.zeros(0).reshape(0,8) ')) for(i in 1:length(pors)) { for(j in 1:length(pors[[i]])) { if(!is.null(pors[[i]][[j]])) { out0 <- "##############################################################" out1 <- to_python_om(votrix(pors[[i]][[j]])) out2 <- paste0(' # Algorithm without Condorcet exec_time = 0 algorithm = am0.AzziniMunda0(om) start_time = time.time() sol = algorithm.execute() exec_time = time.time() - start_time result = np.array([',n,', ',i,', ',j,', 0, exec_time, sol.shape[0], algorithm.ntentative, "NULL"], dtype=np.dtype(object)) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out3 <- paste0(' # Algorithm with Condorcet winner exec_time = 0 algorithm = am1.AzziniMunda1(om) start_time = time.time() sol = algorithm.execute() exec_time = time.time() - start_time result = np.array([',n,', ',i,', ',j,', 1, exec_time, sol.shape[0], algorithm.ntentative, algorithm.cwinner], dtype=np.dtype(object)) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out4 <- paste0(' # Algorithm with Condorcet winner exec_time = 0 algorithm = am2.AzziniMunda2(om) start_time = time.time() sol = algorithm.execute() exec_time = time.time() - start_time result = np.array([',n,', ',i,', ',j,', 2, exec_time, sol.shape[0], algorithm.ntentative, algorithm.cwinner], dtype=np.dtype(object)) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out5 <- paste0(' # Algorithm with Condorcet winner exec_time = 0 algorithm = am3.AzziniMunda3(om, np.float("inf")) start_time = time.time() sol = algorithm.execute() exec_time = time.time() - start_time result = np.array([',n,', ',i,', ',j,', 3, exec_time, sol.shape[0], algorithm.ntentative, algorithm.cwinner], dtype=np.dtype(object)) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out <- c(out, out0, out1, out2, out3, out4, out5) } } } } else { out <- c(paste0(imports, ' rep = ',rep,' results',n,' = np.zeros(0).reshape(0,8+rep) ' )) for(i in 1:length(pors)) { for(j in 1:length(pors[[i]])) { if(!is.null(pors[[i]][[j]])) { out0 <- "##############################################################" out1 <- to_python_om(votrix(pors[[i]][[j]])) out2 <- paste0(' times = np.zeros(rep) for i in range(rep): # Algorithm without Condorcet algorithm = am0.AzziniMunda0(om) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([',n,', ',i,', ',j,', 0, exec_time, sol.shape[0], algorithm.ntentative, "NULL"], dtype=np.dtype(object)), times) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out3 <- paste0(' times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = am1.AzziniMunda1(om) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([',n,', ',i,', ',j,', 1, exec_time, sol.shape[0], algorithm.ntentative, algorithm.cwinner], dtype=np.dtype(object)), times) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out4 <- paste0(' times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = am2.AzziniMunda2(om) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([',n,', ',i,', ',j,', 2, exec_time, sol.shape[0], algorithm.ntentative, algorithm.cwinner], dtype=np.dtype(object)), times) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out5 <- paste0(' times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = am3.AzziniMunda3(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([',n,', ',i,', ',j,', 3, exec_time, sol.shape[0], algorithm.ntentative, algorithm.cwinner], dtype=np.dtype(object)), times) print(result[:7]) results',n,' = np.vstack((results',n,', result)) ') out <- c(out, out0, out1, out2, out3, out4, out5) } } } } out <- c(out, paste0(' pd.DataFrame(results',n,').to_csv("results',n,type,'_azzini.csv")')) out <- paste(out, collapse = "\n") sink(file_name) cat(out) sink() }

b7f4e07a772a0d3a6ab3a2967dc196acb2adb382

c9f3369c749e5a3cfebaa96dc1b484e72a3dd7d0

/man/brmle.Rd

543d05ba72917ac569d158d07c22816a1b165333

[]

no_license

amoloudi/R-PKG-Distributions

128ff992a10a0da915f27aa941d2f9d476ad9e9a

20daa9657d6833cb7aff2ac9194340504c1f0270

refs/heads/master

2021-09-02T19:18:04.772144

2018-01-03T19:13:48

115,752,321

0

null

UTF-8

R

false

814

rd

brmle.Rd

\name{brmle} \alias{brmle} \title{ bernoulli estimation } \description{ finds parameter of bernoulli distribution given as input } \usage{ brmle(x) } \arguments{ \item{x}{ x is a vector contains a set of bernoulli numbers } } \details{ p is mean of X values } \value{ return value is the parameter p of bernoulli distribution } \references{ https://www.projectrhea.org/rhea/index.php/Maximum_Likelihood_Estimation_Analysis_for_various_Probability_Distributions} \author{ A.Moloudi } \note{ Nothing to note! } \seealso{ bimle.R } \examples{ x <- read.delim("DATA.txt", header=TRUE, sep="\t") res = brmle(x) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ ~brmle }% use one of RShowDoc("KEYWORDS") \keyword{ ~bernoullimle }% __ONLY ONE__ keyword per line

3ff2ce4247b9dc35c6baf511ce6fe11874751ba9

33c3502bf722baa887d932a7a42d0ab1456ea450

/Task3R.r

56805206894b65090341be32f7c55373a4c8e262

[]

no_license

aasthas10/TSF-Task3-R

043e6269beb7dffd9d92b1fd2e6766dd9274b428

4527e027c1a5f3a84ec000461ce7f4137856f15e

refs/heads/main

2023-05-08T15:30:25.271553

2021-05-09T17:11:35

365,224,427

0

null

UTF-8

R

false

5,367

r

Task3R.r

library(dplyr) library(ggplot2) library(tidyr) library(shiny) library(plotly) library(corrr) library(treemap) superstore <- read.csv("C:\\Users\\ADMIN\\Downloads\\SampleSuperstore.csv") head(superstore) tail(superstore) summary(superstore) #To check if there are any null values is.null(superstore) #To check if there is any duplicacy and remove them too along with removing two columns (postal codes and country) #as I do not require them for further analysis. data <- superstore %>% distinct() %>% select(-c(Country, Postal.Code)) data x <- data %>%select(Sales, Quantity, Discount, Profit) corr_var <- correlate(x, method = 'pearson',use = "pairwise.complete.obs", diagonal = 1) corr_var y<- data %>%select(Sales, Quantity, Discount, Profit) cov_var <- cov(y) cov_var summary(data$Sales) statewise_sales <- data %>% group_by(State) %>% summarise(total_sales = sum(Sales)) %>% arrange(desc(total_sales)) statewise_sales ggplot(data, aes( x= State, y= Sales, fill= State),options(scipen=999)) + geom_col()+ ggtitle("Statewise Sales Analysis") + coord_flip() + theme(legend.position = "None", axis.text.y = element_text(size=6)) regionwise_sales = data %>% group_by(Region) %>% summarize(totalS= sum(Sales)) %>% arrange(desc(totalS)) regionwise_sales ggplot(data, aes( x= Region, y= Sales, fill= Region),options(scipen=99)) + geom_col()+ ggtitle("Regionwise Sales Analysis") + theme(legend.position = "None", axis.text.y = element_text(size=6)) Statewise_profit = data%>% group_by(State)%>% summarise(totalP= sum(Profit))%>% arrange(desc(totalP)) Statewise_profit ggplot(data, aes( x= State, y= Profit, fill= State),options(scipen=99)) + geom_col()+ ggtitle("Statewise Profit Analysis") + coord_flip() + theme(legend.position = "None", axis.text.y = element_text(size=6)) regionwise_profit = data %>% group_by(Region) %>% summarize(totalP= sum(Profit)) %>% arrange(desc(totalP)) regionwise_sales ggplot(data, aes( x= Region, y= Profit, fill= Region),options(scipen=99)) + geom_col()+ ggtitle("Regionwise Profit Analysis") + theme(legend.position = "None", axis.text.y = element_text(size=6)) BarPlot = data %>% group_by(State) %>% summarize(sales_profit_ratio= sum(Profit)/sum(Sales)) %>% arrange(desc(sales_profit_ratio)) BarPlot ggplot(BarPlot, aes( x= sales_profit_ratio, y= State, fill= State),options(scipen=99)) + geom_col()+ ggtitle("Statewise Sales-Profit Ratio Analysis ") + theme(legend.position = "None", axis.text.y = element_text(size=6)) Segment_analysis = data %>% group_by(Segment) %>% summarize(ratio= sum(Profit)/sum(Sales)) %>% arrange(desc(ratio)) %>% ggplot( aes( x= Segment, y= ratio, fill= Segment),options(scipen=99)) + geom_col()+ ggtitle("Profit-Sales Ratio analysis for each Segment ") Segment_analysis category_s = data %>% group_by(Category) %>% summarize(Sales=sum(Sales)) pct <- round((category_s$Sales/sum(category_s$Sales))*100) lbls <- paste(category_s$Category , pct) lbls <- paste(lbls, "%", sep = " ") pie(category_s$Sales, labels = lbls, main =" Percentage sales by Category ", col= c('darkmagenta','plum','plum4')) treemap(data, index = c("Category","Sub.Category"),title='Sales treemap for categories', vSize = "Sales",vColor ="Profit", type= "value",palette = "RdYlGn", range=c(-20000,60000),mapping= c(-20000,10000,60000)) Price_per_product = ggplot(data, aes( x= Sub.Category, y=sum(Sales)/sum(Quantity), fill= Sub.Category),options(scipen=99)) + geom_col()+ ggtitle("Price per product") + coord_flip() + theme(legend.position = "None", axis.text.x = element_text(size=6)) Price_per_product profit_per_product = ggplot(data, aes( x= Sub.Category, y=sum(Profit)/sum(Quantity), fill= Sub.Category),options(scipen=99)) + geom_col()+ ggtitle("Profit per product") + coord_flip()+ theme(legend.position = "None", axis.text.x = element_text(size=6)) profit_per_product ggplot(data, aes(x = Quantity, y = Sales, fill = Ship.Mode),options(scipen=99) )+ geom_bar(stat = "identity") ggplot(data, aes( x=Ship.Mode, y= Profit, fill= Ship.Mode),options(scipen=99)) + geom_col()+ ggtitle("Profit by Shipment mode and Segment") + theme(legend.position = "None", axis.text.x = element_text(angle = 70 ,size=6)) + facet_wrap(~Segment) Sales_with_discount = data %>% filter(Discount != 0) %>% summarize(totals=sum(Sales)) Sales_with_discount Sales_without_discount = data %>% filter(Discount == 0) %>% summarize(totals=sum(Sales)) Sales_without_discount profit_with_discount = data %>% filter(Discount != 0) %>% summarize(totalp=sum(Profit)) profit_with_discount profit_without_discount = data %>% filter(Discount == 0) %>% summarize(totalp=sum(Profit)) profit_without_discount

d10a1ae4e300a41ee3cd16aeaf842c9742070ec5

197590555db25e2b43692e4a89c3c8388c03fdf1

/tests/testthat/test-custom-noncompliance.R

59ec62e8ea9bd85d48414f3d619d3d879b2e3fb7

[]

no_license

yadevi/DeclareDesign-1

f843ef77d937d1dd0975b99f8ab5914c20461c1c

badcd6e6edbb2e0bb3cf51b3da1047664acea789

refs/heads/master

2021-06-14T03:53:23.472797

2016-12-05T06:16:05

null

0

null

UTF-8

R

false

1,698

r

test-custom-noncompliance.R

rm(list=ls()) library(testthat) library(DeclareDesign) context("Noncompliance") test_that("test whether noncompliance works", { population <- declare_population(noise = "rnorm(n_)", size = 1000) sampling <- declare_sampling(n = 500) potential_outcomes <- declare_potential_outcomes(formula = Y ~ 5 + .5*D, condition_names = c(0, 1), assignment_variable_name = "D") my_noncompliance_function <- function( data, baseline_condition, assignment_variable_name, prob_non_comply){ N <- nrow(data) D <- data[,assignment_variable_name] non_comply <- rbinom(N,1,prob_non_comply)==1 D[non_comply] <- baseline_condition return(D) } noncompliance <- declare_noncompliance( noncompliance_function = my_noncompliance_function, condition_names = c(0,1), assignment_variable_name = "Z", baseline_condition = 0, prob_non_comply = .1) assignment <- declare_assignment(condition_names = c(0,1)) # mock data --------------------------------------------------------------- pop_draw <- draw_population(population = population, potential_outcomes = list(potential_outcomes), noncompliance = noncompliance) smp_draw <- draw_sample(data = pop_draw, sampling = sampling) smp_draw <- assign_treatment(data = smp_draw, assignment = assignment) smp_draw <- draw_outcome(data = smp_draw, potential_outcomes = potential_outcomes, noncompliance = noncompliance) head(pop_draw) head(smp_draw) with(smp_draw, table(Z, D)) })

54a63a56079e6878b5a5c3aead24b5afecf3ec0b

f6d8e20dec1641dae4d8dcbeacea647ffede8a5f

/R/SvalbardGradientButtonData/iButtonParse.R

7c73ace69a37472f6f4559198b389c7e124de766

[]

no_license

Plant-Functional-Trait-Course/PFTC_Data

0f440f97cd92814d60dba8a6508e5e1c4988fa46

c436657f33226bd53b565ad90be422d7343de78b

refs/heads/master

2022-12-16T07:25:48.797221

2020-09-10T14:56:33

150,146,451

2

1

null

UTF-8

R

false

1,327

r

iButtonParse.R

iButt <- read.csv("Celevation_TEMPfile.txt") fluxes <- read.csv("Cflux_SV_Gradient_2018.csv") iButt$Datetime <- as.character(iButt$Datetime) iButt$Temperature <- iButt$Temp + (iButt$Decimal/1000) datestimes <- strsplit(iButt$Datetime, split = " ") iButt$Date <- sapply(datestimes, FUN="[[", 1) iButt$Time <- sapply(datestimes, FUN="[[", 2) times <- strsplit(iButt$Time, split=":") iButt$Hour <- as.numeric(sapply(times, FUN="[[", 1)) iButt$Minute <- as.numeric(sapply(times, FUN="[[", 2)) iButt$Second <- sapply(times, FUN="[[", 3) #Use the first iButton measurement if fluxes are too early base_temp = 13.098 flux_temp <- rep(0, nrow(fluxes)) fluxtime <- strsplit(as.character(fluxes$StartTime), split=":") fluxtime[[27]] <- c(0, 0, 0) #yea hours <- as.numeric(sapply(fluxtime, FUN="[[", 1)) minutes <- as.numeric(sapply(fluxtime, FUN="[[", 2)) for(i in seq(1, nrow(fluxes))) { if(hours[i] <= 11) { if((hours[i] == 11 && minutes[i] < 14) || hours[i] < 11) { flux_temp[i] = base_temp next } } ind <- which(iButt$Hour == hours[i] & iButt$Minute == minutes[i]) if(length(ind) == 0) print(i) flux_temp[i] = mean(iButt[ind,]$Temperature) } print(flux_temp) ##Write the final merged output file using temperature data and site metadata #write.csv(flux_temp, file="Svalbard_Gradient_iButtonTemps.csv")

f7aa03e7e7362ca3cdaa05c1ab82e1775cf0f3e0

2d1a2eef9ed2df3670c24a80378cb5e8acd7003d

/man/print.Cosinor.Rd

af475f95b61b6a0b6ed92ca6af6dd913a08d8304

[]

no_license

agbarnett/season

bd7b5a12ccab2c9337ab2961204b512f2fee4f42

6a707fccc0bdef472446b758e548e259f48e7b10

refs/heads/master

2022-05-20T06:30:33.394625

2022-03-20T09:48:02

126,654,287

1

null

2022-02-16T02:59:35

2018-03-25T01:20:28

R

UTF-8

R

false

true

618

rd

print.Cosinor.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.Cosinor.R \name{print.Cosinor} \alias{print.Cosinor} \title{Print the Results of a Cosinor} \usage{ \method{print}{Cosinor}(x, ...) } \arguments{ \item{x}{a \code{Cosinor} object produced by \code{cosinor}.} \item{\dots}{optional arguments to \code{print} or \code{plot} methods.} } \description{ The default print method for a \code{Cosinor} object produced by \code{cosinor}. } \details{ Uses \code{print.glm}. } \seealso{ \code{cosinor}, \code{summary.Cosinor}, \code{glm} } \author{ Adrian Barnett \email{a.barnett@qut.edu.au} }

0d99b79b11551ce32895e489fd01f30500a12ecb

bcfdc2cd1d0ff50ce66662a1a37fd9f5adc90f68

/global.R

60697023949ab1d98dcf05102748bea61633c286

[]

no_license

alstoc/LM_vs_SEM_shiny

d82197060e361129f38840b69ed64345d0230b41

ce223d78a98b40d7e52523bf492a946918a1d5e5

refs/heads/master

2023-05-03T10:21:10.500114

2021-05-17T11:27:22

365,478,458

0

null

UTF-8

R

false

1,724

r

global.R

load("./data/sim_res_study_1.Rda") load("./data/sim_res_study_2.Rda") # Create table containing error variance and corresponding reliability rel_err <- tibble(reliability = c(0.5, 0.7, 0.8, 0.9, 0.95, 1.0), #error_var = c(1, 0.428571, 0.25, 0.111111, 0.052632), error_var = c(225, 96.428571, 56.25, 25, 11.842105, 0)) # Create custom ggplot theme theme_fruits <- function() { font <- "sans" #assign font family up front theme_fivethirtyeight() + #replace elements we want to change theme( # panel elements rect = element_rect(fill = "#F8F8F8"), panel.background = element_rect(fill = "#F8F8F8"), # plot elements plot.title = element_text( family = font, size = 30, face = "bold"), plot.subtitle = element_text( family = font, size = 20), plot.caption = element_text( family = font, size = 12), # axis elements axis.title = element_text( family = font, size=20, face = "bold"), axis.text.x = element_text( family = font, size = 12), axis.text.y = element_text( family = font, size = 12), # legend elements legend.title = element_text( family = font, size = 20, face = "bold"), legend.text = element_text( family = font, size = 18), legend.position = "right", legend.direction = "vertical", # other elements aspect.ratio = 1, ) }

f71c61d27c803bb2b78759485cd8cfd149b9e30e

fcbe81388b0f883ff36a9e7173277f80789fa871

/man/pd_sector_var.Rd

60b926d4676232ffd31c9d5979bf3faeb92f5cd0

[]

no_license

cran/crp.CSFP

15b76b1860f47153a0909b1b9520f4357738d383

b94ad004acd7cfb33210b5f3c74976ca7561c04a

refs/heads/master

2021-01-10T21:24:19.961192

2016-09-11T18:35:59

17,695,304

1

3

null

UTF-8

R

false

363

rd

pd_sector_var.Rd

\name{pd_sector_var} \docType{data} \alias{pd_sector_var} \title{Sector variances for the Credit Suisse example portfolio} \description{ The file contains the sector variances corrsponding to the example portfolio from the Credit Suisse First Boston for CreditRisk+. } \references{First Boston Financial Products, "CreditRisk+", 1997} \keyword{datasets}

de160f6e3a4b04ef1a177185b425fb58ef68b16d

8eda0c211d86fba9ea1abec607de84127d34ffeb

/lib/image_df.R

380b5b2de492bb6014d09501890fb442ed4ab910

[]

no_license

NinaShao1230/Fall2017-project3-grp9

f2c942cf31d201346301d920143220b348d81908

bbda084d60b89bd559affc117958f6f291a4d6de

refs/heads/master

2021-07-25T01:21:49.922482

2017-11-02T04:42:17

null

0

null

UTF-8

R

false

1,025

r

image_df.R

install.packages("OpenImageR") # library(EBImage) library("OpenImageR") setwd("/Users/ninashao/Desktop/Fall2017-project3-fall2017-project3-grp9-master 2") img_labels<-read.csv("/Users/ninashao/Desktop/Fall2017-project3-fall2017-project3-grp9-master 2/data/training_set/label_train.csv",header = T) img_dir <- "/Users/ninashao/Desktop/Fall2017-project3-fall2017-project3-grp9-master 2/data/image3/" dir_names <- list.files(img_dir) n_files <- length(list.files(img_dir)) labels<-img_labels$label..0.for.muffin..1.for.chicken..2.for.dog. # Set up df df <- data.frame() # Set image size. In this case 28x28 img_size <- 28*28 for(i in 1:n_files){ img <- as.matrix(readImage(paste0(img_dir, dir_names[i]))) # Get the image as a matrix img_matrix <- img@.Data # Coerce to a vector img_vector <- as.vector(t(img_matrix)) # Bind rows df <- rbind(df,img_vector) # Print status info print(paste("Done ", i, sep = "")) } # dim(df) df_label<-cbind(labels,df) write.csv(df_label, file="df_nn.csv", row.names = FALSE)

519772641356cbcc1c270f3eb18cbeda2db06142

95510affefea922b31ae8f8141f373d2274b7b17

/3 - Sampling.R

dc97443c8e24304d81535275972a74c43f99f6c6

[]

no_license

armandyne/Data-Science-Capstone-Project

ed50c83b42dd477604e5e2b102e9ca19b9dc49e4

57afda53168fd46cb4a6618109fde8b2e888afa2

refs/heads/master

2020-03-10T08:27:45.372241

2018-04-14T18:29:24

129,285,925

0

null

UTF-8

R

false

296

r

3 - Sampling.R

load(paste0(data_dir, "/", "raw_datasets.rda")) blogs_sample <- sample(blogs_vec, size = 10000) news_sample <- sample(news_vec, size = 1000) twitter_sample <- sample(twitter_vec, size = 20000) save(blogs_sample, news_sample, twitter_sample, file = paste0(data_dir, "/", "sampled_datasets.rda"))

ce5ddc7b01574f54929cc062081bcb976bc446d7

4c32c6c3cf83a05cbc1ff323977872233c4637f1

/R/Stitcher.R

13e1d0d9c0fe4eca9ebb97b8199799f0bdd9fda2

[]

no_license

icorroramos/COMPAR-EU_diabetes_model

fb4f343d563c1179d7e0d7256b94f1953d098415

8e7bf1445daafee7655135c75bc02b73e99695f5

refs/heads/master

2022-08-29T21:08:00.103803

2022-08-06T09:21:19

207,499,341

0

2

null

2021-03-17T08:34:57

2019-09-10T08:00:48

R

UTF-8

R

false

3,091

r

Stitcher.R

### Function to combine the results of two model runs. ### Maintains names and structure of output objects so that analysis scripts can be used stitcher <- function(comp.name, location.1, location.2){ load(paste0(location.2, comp.name, '.RData')) npats.loc2 <- ifelse(exists('sim.vars'), sim.vars[[2]], sim.vars.uc[[2]]) sim.names <- ls()[startsWith(ls(), 'sim.results')] for (i in 1:length(sim.names)) { assign(paste0(sim.names[i], '.extend'), get(sim.names[i])) } load(paste0(location.1, comp.name, '.RData')) npats.loc1 <- ifelse(exists('sim.vars'), sim.vars[[2]], sim.vars.uc[[2]]) for (i in 1:length(sim.names)) { loc2.data <- get(paste0(sim.names[i], '.extend')) loc2.data[[1]]$SIMID <- loc2.data[[1]]$SIMID + npats.loc1 loc1.data <- get(sim.names[i]) loc1.data[[1]] <- rbind(loc1.data[[1]], loc2.data[[1]]) loc1.data[-1] <- (npats.loc1 * as.data.frame(loc1.data[-1]) + npats.loc2 * as.data.frame(loc2.data[-1])) / (npats.loc1 + npats.loc2) assign(sim.names[i], loc1.data, envir = .GlobalEnv) } } ### Function to combine n model runs stored in same directory ### Maintains names and structure of output objects so that analysis scripts can be used sewing_machine <- function(n.run, filename) { n.pats.run <- rep(NA, n.run) run.seed <-rep(NA, n.run) load(paste0(filename, '1.RData')) # Get first output to check which simulations were run sim.names <- ls()[startsWith(ls(), 'sim.results')] list.names <- names(get(sim.names[1])) n.sims <- length(sim.names) all.means <- lapply(1:n.sims, matrix, data = NA, nrow = 27, ncol = n.run) all.patients <- rep(list(data.frame()), n.sims) for (i in 1:n.run) { load(paste0(filename, i, '.RData')) sim.names.run <- ls()[startsWith(ls(), 'sim.results')] if (!identical(sim.names,sim.names.run[1:n.sims])) stop(paste('Simulations in run', i, 'not the same as in first run')) n.pats.run[i] <- ifelse(exists('sim.vars'), sim.vars[[2]], sim.vars.uc[[2]]) run.seed[i] <- ifelse(exists('sim.vars'), sim.vars[[1]], sim.vars.uc[[1]]) for (j in 1:n.sims) { curr.sim <- get(sim.names[j]) all.means[[j]][, i] <- unlist(curr.sim[2:28]) curr.sim[[1]]$SIMID <- curr.sim[[1]]$SIMID + sum(n.pats.run[1:i-1]) # Increase SIMIDs with total pats in previous runs so that SIMIDS remain unique all.patients[[j]] <- rbind.fill(all.patients[[j]], curr.sim[[1]]) } } run.weight <- n.pats.run / sum(n.pats.run) weighed.means <- lapply(all.means, '*', run.weight) unnamed.means <- lapply(weighed.means, rowSums) results.means <- lapply(unnamed.means, function(x) {names(x) <- list.names[2:28]; return(x)}) for (i in 1:n.sims){ results.patient <- list(all.patients[[i]]) names(results.patient) <- list.names[1] assign(sim.names[i], c(results.patient, as.list(results.means[[i]])), envir = .GlobalEnv) print(paste('Created object', sim.names[i], 'in global environment')) } assign('sewing.seeds', run.seed, envir = .GlobalEnv) print('Created object sewing.seeds containing all seed values') }

8494cb3ce5fd4785b16e27a3450eb27b7e3a731d

4f14e7e4a3a91ca32330362158028e65f3f75011

/slice_b_gamma.R

04071e699be45d58564e7678c02f933843ecbb91

[]

no_license

drvalle1/resist

31f5231ce944d9b9c86b4f6dd63cfa327c80c116

4cc588b91a8a82ca2ba698c8001b3fc6cc18bde8

refs/heads/master

2021-02-26T06:25:37.518977

2020-08-31T16:37:06

245,502,435

0

1

null

UTF-8

R

false

2,602

r

slice_b_gamma.R

Doubling_bgamma=function(soma.media,w,b.gamma,yslice,MaxIter,ysoma){ b.gammaLo=b.gamma-w*runif(1) b.gammaLo=ifelse(b.gammaLo<0.0000001,0.0000001,b.gammaLo) b.gammaHi=b.gammaLo+w #calculate llk ylo=llk_bgamma(soma.media=soma.media,ysoma=ysoma,b.gamma=b.gammaLo) yhi=llk_bgamma(soma.media=soma.media,ysoma=ysoma,b.gamma=b.gammaHi) cond=F #keep doubling until ylo<yslice and yhi<yslice oo=0 while ((ylo>yslice) & (oo<MaxIter)){ b.gammaLo=b.gammaLo-w if (b.gammaLo<0.0000001) { #avoid negative values b.gammaLo=0.0000001 break; } ylo=llk_bgamma(soma.media=soma.media,ysoma=ysoma,b.gamma=b.gammaLo) oo=oo+1 } if (oo >= MaxIter) cond=T oo=0 while ((yhi>yslice) & (oo<MaxIter)){ b.gammaHi=b.gammaHi+w yhi=llk_bgamma(soma.media=soma.media,ysoma=ysoma,b.gamma=b.gammaHi) oo=oo+1 } if (oo >= MaxIter) cond=T if (cond) return(c(b.gamma,b.gamma)) c(b.gammaLo,b.gammaHi) } #------------------------------------------- llk_bgamma=function(soma.media,ysoma,b.gamma){ a1=b.gamma*soma.media sum(dgamma(ysoma,a1,b.gamma,log=T)) } #------------------------------------------- Shrink_Sample_bgamma=function(rango1,yslice,MaxIter,soma.media,ysoma,b.gamma){ diff1=rango1[2]-rango1[1] yfim=-Inf oo=0 while ((yfim<yslice) & (diff1 > 0.00000001) & (oo<MaxIter)){ x=runif(1,min=rango1[1],max=rango1[2]) yfim=llk_bgamma(soma.media=soma.media,ysoma=ysoma,b.gamma=x) if (yfim<yslice){ #shrink the slice if x falls outside DistLo=abs(rango1[1]-x) DistHi=abs(rango1[2]-x) if (DistLo>DistHi) rango1[1]=x if (DistLo<DistHi) rango1[2]=x diff1=rango1[2]-rango1[1] } oo=oo+1 } if (diff1 <= 0.00000001 | oo >=MaxIter) return(b.gamma) x } #------------------------------------------- Sample_bgamma=function(nparam,xmat,ysoma,betas,b.gamma,w,MaxIter,seg.id,nagg){ media=exp(xmat%*%betas) soma.media1=GetSomaMediaOneGroup(media=media, nagg=nagg,SegID=seg.id-1) # teste=data.frame(media=media,seg.id=seg.id) # teste1=aggregate(media~seg.id,data=teste,sum) #define upper bound upper1=llk_bgamma(soma.media=soma.media1,ysoma=ysoma,b.gamma=b.gamma) yslice=upper1-rexp(1); #define slice rango1=Doubling_bgamma(soma.media=soma.media1,w=w,b.gamma=b.gamma, yslice=yslice,MaxIter=MaxIter,ysoma=ysoma) #sample this particular parameter b.gamma=Shrink_Sample_bgamma(rango1=rango1,yslice=yslice,MaxIter=MaxIter, soma.media=soma.media1,ysoma=ysoma, b.gamma=b.gamma) b.gamma }

73a08da5c1e32d79005da50a44058963dc4f76a8

3581b1fb06e1455db0cbafd1c43e2ac71a250734

/R/doNlme.R

29b9e51df125d57df326517ceb0f78c3a935b158

[]

no_license

cran/mixlow

a4f85e942ef212bfbed81551c2d68b9197e2dc99

779a43f75afd02322ade30e575a45a2d1bd81e79

refs/heads/master

2016-09-05T13:24:41.248119

2012-03-20T00:00:00

null

0

null

UTF-8

R

false

6,176

r

doNlme.R

`doNlme` <- function(mixlowData, nlsData, drugs=getDrugs(mixlowData), analysis="multiple", varFunction=1, method="ML", verbose=FALSE) { ## outer loop that sets up calls to NLME if (!inherits(nlsData, "nlsData")) stop("use only with \"nlsData\" objects") if (analysis=="single") { if (is.list(varFunction)==FALSE) stop("VarFunction must be a list of vectors named by drug") if (length(names(varFunction)) == 0) stop("VarFunction must be a list of vectors named by drug") } varFunction0 = varFunction nls.estimates = nlsData$nlsEstimates data00 = mixlowData$concentrationResponse drugRatios = mixlowData$drugRatios # check for number of drugs newDrugVector = drugs[1] # default, for single drug if (length(drugs) !=1) { # length will be 1 if a single drug is being analyzed alone. Else, the drug set will contain a mixture. # order drugs so mixture is last newDrugVector = numeric(0) mixFlag = 0 for (i in seq(1,length(drugs))) { dr = drugs[i] ratio = drugRatios[,dr] if (all(ratio==0)) { if (mixFlag == 1) stop("Drugs for analysis can contain only one mixture") mixFlag = 1 mix = dr } if (any(ratio>0)) newDrugVector = c(newDrugVector,dr) } if (mixFlag != 0) { #stop("Drugs must contain at least two drugs and one mixture. No mixture present.") newDrugVector = c(newDrugVector,mix) } } drugs0 = newDrugVector # remove entries for blanks data00 <- data00[data00$label=="rx",] nlmeResults = list(0) nlmeGraph = list(0) nlmeModels = list(0) numberOfAnalysis = 1 if (analysis == "single") numberOfAnalysis = length(drugs0) # loop over each set of drugs to be modeled ---------------------------------------------------------------------- for (setNum in seq(1,numberOfAnalysis)) { drugs = drugs0 if (numberOfAnalysis > 1) { drugs = drugs0[setNum] varFunction = varFunction0[[drugs]] } #dr1 = drugs[1] cell = unique(as.vector(drugRatios$cell[drugRatios$drug==drugs[1]])) useTrays = drugRatios[(drugRatios$drug %in% drugs),] # add drug and cell data to data0 data0 = merge(data00, useTrays, by= "tray") data0$tray = as.factor(data0$tray) # collect data for drugs in desired set inlist <- data0$drug %in% drugs dat1 <- data0[inlist & data0$cell== cell, ] # set order for assessing drugs ord = drugs ord2 = paste(ord,"|",sep="",collapse="") ord2 = substr(ord2,1,nchar(ord2)-1) if (verbose==TRUE) writeLines("######################################################################\n") if (verbose==TRUE) writeLines(paste("\n **** NLME analysis, drugs for round ", setNum, " are: ", ord2, " ****",sep="")) # if only one tray, then duplicate data new = 20000 for (dr in ord) { utray = unique(dat1$tray[dat1$drug==dr]) if (length(utray) == 1) { if (verbose==TRUE) writeLines("\n********** duplicating data, only one tray present ***************\n") tmp <- dat1[dat1$drug==dr,] tray = unique(tmp$tray) # assumes that no tray number is called new20000+ tmp$tray <- paste("new", new,sep="") new = new + 1 dat1 <- rbind(dat1,tmp) } } # order drugs dat1 <- dat1[order(dat1$cell,dat1$drug, dat1$tray, dat1$conc),] dat1$drg <- rep(1,length(dat1$adj_resp)) for (i in 1:length(ord)) dat1$drg[dat1$drug==ord[i]] <- i dat1$drg = as.factor(dat1$drg) # make parameter list tmp1 = unique(dat1[,c("drug","tray")]) combo = merge(nls.estimates,tmp1, by= "tray") tmp5 = cbind(as.numeric(as.vector(combo$g)),as.numeric(as.vector(combo$p)),as.numeric(as.vector(combo$u)),as.numeric(as.vector(combo$lambda))) if (is.null(combo$drug)==FALSE) {param = aggregate(tmp5, list(drug=combo$drug) ,mean)} if (is.null(combo$drug) == TRUE) {param = aggregate(tmp5, list(drug=combo$drug.x) ,mean)} names(param) = c("drug","g","p","u","lambda") paramList = as.list(param) ord3 = rep(0,length(ord)) for (i in 1:length(ord)) ord3[ord == paramList$drug[i]] <- i #for (i in 1:length(ord)) {ord3[paramList$drug==ord[i]] <- i paramList$drug = paramList$drug[ord3] paramList$g = paramList$g[ord3] paramList$p = paramList$p[ord3] paramList$u = paramList$u[ord3] paramList$lambda = paramList$lambda[ord3] if (length(paramList$drug) != length(ord)) {stop("** Mismatch in length of drugs and parameter estimates")} if (any(paramList$drug != ord)) { stop("** Drugs/param list in wrong order")} paramList$u = mean(as.numeric(as.vector(combo$u))) paramList$p = paramList$p # p is in log form paramList$g = paramList$g # g is in log form # setup call to NLME and then call NLME, return NLME results for best model meth = method best2 <- NlmeSetupCall(ord, paramList, dat1, method, varFunction, cell, analysis, verbose) if(best2$best==0) { if (verbose==TRUE) print('no models were suitable') next } # collect results temp <- as.list(c(nam =best2$best, method=meth, drug= ord2, setNum=setNum, cell= cell, lik= logLik(best2$mbest), bic = BIC(best2$mbest), sig = best2$mbest$sigma, modelstruct = unlist(best2$mbest$modelStruct), rc = best2$rcdrug, r2= best2$r2drug, se=unlist(summary(best2$mbest)$tTable[,2]), coeff = unlist(best2$mbest$coefficients), df = unlist(best2$mbest$fixDF)[1], covv=as.list(best2$mbest$varFix) )) nlmeResults[[setNum]] <- temp # collect results for graphing nlmeGraph[[setNum]] = list(pred0=best2$pred0, dat1=dat1, ord=ord, residu=best2$residu, best=best2$best) # alter formula so that anova can be called on models #best2$mbest$call$model = y~1 nlmeModels[[setNum]] = best2$mbest } returnList = list(nlmeResults=nlmeResults, nlmeGraph=nlmeGraph, nlmeModels=nlmeModels) class(returnList) <- c("nlmeData") return (returnList) }

1ae1a18a31abfb2a116cc827ee61c01500910b3a

1f0a75ecce94daf0003f6545e9ef4ac4c56ccf6d

/man/PictureRadialGradient-class.Rd

d40b93c11dd85ad57bae66563b8a09f7b25fc349

[]

no_license

sjp/grImport2

767e71b961204b9b25389c7e07a629e2c9d6e069

00e310e43fa9bc33db2d4eef81340069493a2150

refs/heads/master

2021-01-19T04:55:45.773061

2015-02-27T10:41:20

24,830,205

6

3

null

UTF-8

R

false

2,124

rd

PictureRadialGradient-class.Rd

\name{PictureRadialGradient-class} \docType{class} \alias{PictureRadialGradient-class} \alias{applyTransform,PictureRadialGradient,matrix-method} \alias{grobify,PictureRadialGradient-method} \title{Class "PictureRadialGradient" } \description{ A description of a radial gradient that is meant to be used as part of a larger picture (as it does not directly draw anything itself). This object will have no effect unless it is used in conjunction with the gridSVG package. } \section{Slots}{ \describe{ \item{\code{x}:}{ Object of class \code{"numeric"}. The x-location of the radial gradient. } \item{\code{y}:}{ Object of class \code{"numeric"}. The y-location of the radial gradient. } \item{\code{r}:}{ Object of class \code{"numeric"}. The radius of the radial gradient. } \item{\code{fx}:}{ Object of class \code{"numeric"}. The x-location of the focal point of the radial gradient. } \item{\code{fy}:}{ Object of class \code{"numeric"}. The y-location of the focal point of the radial gradient. } \item{\code{spreadMethod}:}{ Object of class \code{"character"} that specifies what happens when a gradient ends within its bounds. Must be one of "pad", "reflect" or "repeat". See \code{"radialGradient"} in the gridSVG package for more information. } \item{\code{stops}:}{ Object of class \code{"list"} that is a list of objects of class \code{"PictureGradientStop"}. } } } \section{Extends}{ Class \code{"PictureContent"}. } \section{Methods}{ \describe{ \item{applyTransform}{ \code{signature(object = "PictureRadialGradient", tm = "matrix")}: transform the locations that represent the bounds and direction of the gradient by a 3x3 transformation matrix. } \item{grobify}{\code{signature(object = "PictureRadialGradient")}: convert to a gradient object for use with the gridSVG package. } } } \author{ Simon Potter } \seealso{ \code{\link{Picture-class}}, \code{\link{grid.picture}}. } \keyword{classes}

c1807d4e1bae8307844e831cdcfe8de043b052f9

90e52d80527b046428fe779017e880a3845e76fb

/CourseProjectv2/plot3.R

acff0f945dd4fa79d414376f1e2841e2febf5bfd

[]

no_license

raneykat/ExploratoryAnalysis

f28099ef1a00e8616aa64f090aae220499c71358

4481571a50cbcbc5959fd7583604197ebbe76496

refs/heads/master

2020-05-17T23:50:02.005582

2014-12-21T23:10:11

null

0

null

UTF-8

R

false

1,399

r

plot3.R

# Katherine Raney # Coursera Exploratory Data Analysis # December 2014 # Course Project 2, Plot 3 # setwd("C:\\raneykat_git\\ExploratoryDataAnalysis\\ExploratoryAnalysis\\CourseProject") #get the data file we need and unzip it library(downloader) url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" download(url,dest="dataset.zip", mode = "wb") unzip ("dataset.zip",exdir = ".") # read in the datasets NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") # Of the four types of sources indicated by the type (point, nonpoint, # onroad, nonroad) variable, which of these four sources have seen # decreases in emissions from 1999–2008 for Baltimore City? # Which have seen increases in emissions from 1999–2008? # Use the ggplot2 plotting system to make a plot answer this question. # plot emissions by year and type for the given city - GIVEN CITY FOR BOTH QUESTIONS? library(dplyr) nei2 <- NEI %>% filter(fips == "24510") %>% group_by(year, type) %>% mutate(TotalEmissions=sum(Emissions)) %>% select(year, type, TotalEmissions) library(ggplot2) # send plot to a png file png(file="plot3.png",bg="transparent",width = 800, height = 800) qplot(year, TotalEmissions, data = nei2, facets =.~ type,ylab="Total PM2.5 Emissions", xlab="Year" ,main="Baltimore City PM2.5 Emissions by Type",geom="line") dev.off()

fa6863e412d9ce93d9387730bf5b4e414d230869

0a906cf8b1b7da2aea87de958e3662870df49727

/grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610052075-test.R

dd8f158adaaf7d2f6c81d867f251948ab0398b32

[]

no_license

akhikolla/updated-only-Issues

a85c887f0e1aae8a8dc358717d55b21678d04660

7d74489dfc7ddfec3955ae7891f15e920cad2e0c

refs/heads/master

2023-04-13T08:22:15.699449

2021-04-21T16:25:35

360,232,775

0

null

UTF-8

R

false

959

r

1610052075-test.R

testlist <- list(rates = 4.14452302922905e-317, thresholds = NA_real_, x = c(-2.11966640218428e-289, Inf, 9.70418706714725e-101, 7.55109302986233e-308, 9.70418706357123e-101, 9.70418706716095e-101, 9.70418706716128e-101, 9.70418706716128e-101, 9.70418706716128e-101, 7.55652533908718e-308, 7.91826926828267e-101, 9.70395179359032e-101, 9.70418704587148e-101, 9.70418706716128e-101, 9.70418706716128e-101, 9.70418706716128e-101, 7.91826926828272e-101, 5.35249266634154e+116, 5.35249213585531e+116, 9.70466856459303e-101, -9.77580118946814e-150, 3.23687261230869e-314, NaN, NaN, 2.81199605989981e-312, 6.59473782987034e-96, 6.59473782982525e-96, 6.59473782982525e-96, 6.59473782982525e-96, 6.5947378298542e-96, 6.59473782982525e-96, 6.59473782982525e-96, 6.59473782982525e-96, NaN, 2.39958945526328e-310, 1.44804023176556e+190, NaN, 1.39067049844331e-309, NaN, 1.25986739689518e-321, 0, 0)) result <- do.call(grattan::IncomeTax,testlist) str(result)

88b04bd7443f8b18a3178d57910147c47e6dbfaa

dd5df187c9f002ff6eae9d70b7c6f69b8ebe15c4

/R入门小例子.R

7041af6b471e3835ce211562629e741a078fa136

[]

no_license

liliwin/Rproject

6429d0209425ca633c3bec59289836f37828c9fa

12be261051b543131ff90daaf192b27444e407ed

refs/heads/master

2021-01-20T22:29:04.905614

2016-07-29T15:46:08

64,316,708

0

null

UTF-8

R

false

2,953

r

R入门小例子.R

############################################################################# #一组学生参加了数学、科学和英语考试。 #为了给所有的学生确定一个单一的成绩衡量指标，需要将这些科目的成绩组合起来。 #另外，还想将前20%的学生评定为A，接下来20%的学生评定为B，以此类推。 #最后，希望按字母顺序对学生排序。 ############################################################################# Stuld <- c(1:10) StuName <- c('John Davis','Angela Williams','Bull Jones','Cheryl Cushing', 'Reuven Ytzrhak','Joel Knox','Mary Rayburn','Greg England','Brad Tmac','Tracy Mcgrady') Math <- c(502,465,621,575,454,634,576,421,599,666) Science <- c(95,67,78,66,96,89,78,56,68,10) English <- c(25,12,22,18,15,30,37,12,22,38) mydata <- data.frame(Stuld,StuName,Math,Science,English) colnames(mydata) mydata <- mydata[,-1] ##数据中心化、标准化## #1、数据的中心化：指数据集中的各项数据减去数据集的均值 #2、数据的标准化：值中心化后的数据在除以数据集的标准差 #数据中心化和标准化的意义是一样的，为了消除量纲对数据结构的影响 ##scale(data,center=T,scale=T) center=T 表示数据中心化 scale=T 表示数据标准化 z <- scale(mydata[3:5]) ##求每个学生的所有成绩均值## #apply(X,MARGIN,FUN,...) #X:阵列包括矩阵,MARGIN:1表示矩阵行，2表示矩阵列，也可以是c(1,2) FUN:表示具体的运算方式 score <- apply(z,1,mean) mydata$score<-apply(z,1,mean) ##姓名处理，将姓名列拆分为FirName,LatName两列 #使用strsplit(data,split="") data是一组向量,split表示分割方式 name<-strsplit(mydata$StuName,split=" ") #data是向量所以这一句是报错的# name<-strsplit(StuName,split=" ") #把strsplit()应用到一个字符串组成的向量上会返回一个列表# name<-unlist(strsplit(StuName,split=" "))#将strsplit的list形式转成向量格式# name<-matrix(name,ncol=2,byrow=T) #将name转化为矩阵，这么做是为了拆分方便# FirName<-name[,1] LasName<-name[,2] ##合并 mydata<-cbind(mydata,FirName,LasName) ##去掉mydata多余的列 mydata<-mydata[-2] ##计算前20%等用四分位来计算 quanile(data,c(.8,.6,.4,.2)) q<-quantile(mydata$score,c(.8,.6,.4,.2)) ##按照四分位分等级 mydata$level[score>=q[1]] <- "A" mydata$level[score>=q[2] & score<q[1]] <- "B" mydata$level[score>=q[3] & score<q[2]] <- "c" mydata$level[score>=q[4] & score<q[3]] <- "D" mydata$level[score<q[4]] <- "E" ##按姓名排序 mydata<-mydata[order(mydata$LasName,mydata$FirName),] options(digits=2) ---------------------------------------------------------------- mydata1 <- data.frame(Stuld,StuName,Math,Science,English) name<-strsplit((StuName)," ") lastname<-sapply(name,"[",2) firstname<-sapply(name,"[",1) sapply() 提取列表中每个成分的第一个元素， “[”是一个可以提取某个对象一部分的函数

ad7f33022bc2416fe05b4d866b642342a64c965c

4c14bcc37fa428673536b87083afb734866f947c

/man/array.resistance.Rd

a6a09f5359aadd87c99d1f06275cca08f79366d4

[]

no_license

RobinHankin/ResistorArray

9c06802cb867eb3c40014ae5552ae8b8420411d1

fe8588cc44b3c5afd91033efd768ce9846860087

refs/heads/master

2021-09-28T17:31:52.431138

2021-09-18T22:25:41

168,077,182

0

null

UTF-8

R

false

1,556

rd

array.resistance.Rd

\name{array.resistance} \alias{array.resistance} \title{Resistance between two arbitrary points on a regular lattice of unit resistors } \description{ Given two points on a regular lattice of electrical nodes joined by unit resistors (as created by \code{makefullmatrix()}), returns the resistance between the two points, or (optionally) the potentials of each lattice point when unit current is fed into the first node, and the second is earthed. } \usage{ array.resistance(x.offset, y.offset, rows.of.resistors, cols.of.resistors, give.pots = FALSE) } \arguments{ \item{x.offset}{Earthed node is at \eqn{(0,0)}{(0,0)}, second node is at \code{(x.offset, y.offset)}} \item{y.offset}{Earthed node is at \eqn{(0,0)}{(0,0)}, second node is at \code{(x.offset, y.offset)}} \item{rows.of.resistors}{Number of rows of resistors in the network (positive integer)} \item{cols.of.resistors}{Number of columns of resistors in the network (positive integer)} \item{give.pots}{Boolean, with \code{TRUE} meaning to return a matrix of potentials of the electrical nodes, and \code{FALSE} meaning to return the resistance between the origin and the current input node} } \details{ Note that the electrical network is effectively toroidal. } \author{Robin K. S. Hankin} \seealso{\code{\link{makefullmatrix}}} \examples{ jj.approximate <- array.resistance(1,2,15,17,give=FALSE) jj.exact <- 4/pi-1/2 print(jj.exact - jj.approximate) persp(array.resistance(4,0,14,16,give=TRUE),theta=50,r=1e9,expand=0.6) } \keyword{array}

fbb68e8f420780d1f3799a6fb4e5a0dd84c3c85e

c08e6b516a3d341d1fdb893448922082dc3626cf

/man/dimsum_stage_fastqc.Rd

0e685f04d1427d73d9f96019279ce66fad0bdfff

[ "MIT" ]

permissive

lehner-lab/DiMSum

eda57459bbb450ae52f15adc95d088747d010251

ca1e50449f1d39712e350f38836dc3598ce8e712

refs/heads/master

2023-08-10T17:20:39.324026

2023-07-20T15:29:47

58,115,412

18

5

null

UTF-8

R

false

true

754

rd

dimsum_stage_fastqc.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dimsum_stage_fastqc.R \name{dimsum_stage_fastqc} \alias{dimsum_stage_fastqc} \title{dimsum_stage_fastqc} \usage{ dimsum_stage_fastqc( dimsum_meta, fastqc_outpath, report = TRUE, report_outpath = NULL, save_workspace = TRUE ) } \arguments{ \item{dimsum_meta}{an experiment metadata object (required)} \item{fastqc_outpath}{FASTQC output path (required)} \item{report}{whether or not to generate FASTQC summary plots (default: TRUE)} \item{report_outpath}{FASTQC report output path} \item{save_workspace}{whether or not to save the current workspace (default: TRUE)} } \value{ an updated experiment metadata object } \description{ Run FASTQC on all fastq files. }

0002a42fc5f39ff7145d768a6280ed4e7e03f1ff

4ba85b2f9f4c11255905ed3228101248595986ad

/k-Nearest-Neighbors and Boosting/DigitRecognition/DigitRecognitionkNN.R

0caeb2f236aae63fb24b920067797074d5ed9cea

[]

no_license

prabhuvashwin/Machine-Learning

97cd7b0260e9f921839a87931725010cfa6d0df0

c63bfadae9a421d093ad8647c9f9b95cf95abdd6

refs/heads/master

2021-06-14T01:29:24.255604

2017-04-08T06:17:06

87,610,303

0

1

null

UTF-8

R

false

1,137

r

DigitRecognitionkNN.R

## Author: Ashwin Venkatesh Prabhu ## Implement k-NN algorithm for Optical recognition for handwritten digits dataset require(class) ## Getting the train and test data train_data <- read.csv("optdigits_raining.csv", header = FALSE) test_data <- read.csv("optdigits_test.csv", header = FALSE) ## Getting the class variable for train and test data train_class_variable <- train_data$V65 test_class_variable <- test_data$V65 digit_accuracy <- numeric() for(i in seq(1, 100, 1)) { ## Generating the model and output for the testing data knnTest <- knn(train_data, test_data, train_class_variable, k = i) accuracy <- mean(knnTest == test_class_variable) ## Accuracy for each k value is stored in this list digit_accuracy <- c(digit_accuracy, accuracy) print(paste("Accuracy at k = ", i, " is: ", accuracy), quote = F) } ## Error rate graph plotted plot(1 - digit_accuracy, type = "l", ylab = "Error Rate", xlab = "K", main = "Error Rate with K values") ## Accuracy graph plotted plot(digit_accuracy, type = "l", ylab = "Accuracy", xlab = "K", main = "Accuracy with K values")

bff663b5250684c5397280bc3168b36457322319

30a8fe01e73e6483e99daddab56a8f97af3b60fd

/GenomeBrowser2_Plot.R

5eebd3a5a2897c08e39e7326565d8774c6290e8f

[ "MIT" ]

permissive

Jonna-Heldrich/meiosis_scripts

3e60552cd3b17ef0104e90f3eb4c0a4e1b09fb89

e75b05d44d21a29c84eff22e879dc252eb4e05aa

refs/heads/master

2020-08-11T07:44:08.162751

2019-10-12T04:44:38

214,519,955

0

MIT

2019-10-12T04:16:34

2019-10-11T20:04:48

R

UTF-8

R

false

1,393

r

GenomeBrowser2_Plot.R

# Genome Browser View plotting # Purpose: Plot whole chromosome or region of chromosome as genome browser view # input: 2 outputs from GenomeBrowser1_Chr.R plot_genomeview <- function(df_samp1,df_samp2,position1,position2,name1,name2,chrnum,color1,color2) { par(las=1) par(mfrow=c(2,1)) ax=c(min(df_samp1[df_samp1$position>=position1 & df_samp1$position<=position2,1]),df_samp1[df_samp1$position>=position1 & df_samp1$position<=position2,1],max(df_samp1[df_samp1$position>=position1 & df_samp1$position<=position2,1])) ay=c(0,df_samp1[df_samp1$position>=position1 & df_samp1$position<=position2,2],0) bx=c(min(df_samp2[df_samp2$position>=position1 & df_samp2$position<=position2,1]),df_samp2[df_samp2$position>=position1 & df_samp2$position<=position2,1],max(df_samp2[df_samp2$position>=position1 & df_samp2$position<=position2,1])) by=c(0,df_samp2[df_samp2$position>=position1 & df_samp2$position<=position2,2],0) plot(df_samp1[df_samp1$position>=position1 & df_samp1$position<=position2,], xlab=paste0('Position on chromosome ',chrnum,' (kb)'), ylab=name1, type='h',col=color1,frame.plot=F) polygon(ax,ay,col=color1,border = NA) plot(df_samp2[df_samp2$position>=position1 & df_samp2$position<=position2,], xlab=paste0('Position on chromosome ',chrnum,' (kb)'), ylab=name2, type='h',col=color2,frame.plot=F) polygon(bx,by,col=color2,border = NA) par(mfrow=c(1,1)) }

1cf1e61d134f5269ddeb905cf83be4f479be4b8f

4056e89d2e74109f4938051dbe09017ab0c48f43

/man/flat_habitat.Rd

9c57f7ea916459db53ec1d37aa13c90b3cff15be

[]

no_license

petrelharp/landsim

af4209dd8f5ffdd8a32a367d8e58b1b1f632e885

c4e7cb8cb637c58bcacc1d92e4a01023eb0b281a

refs/heads/master

2021-01-19T04:32:44.496828

2017-11-22T22:54:10

47,908,664

6

2

null

UTF-8

R

false

true

734

rd

flat_habitat.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/random_habitat.R \name{flat_habitat} \alias{flat_habitat} \title{Create a Flat Habitat} \usage{ flat_habitat(diam = 20000, res = 100, value = 1, width = diam, height = diam) } \arguments{ \item{diam}{Diameter of the desired habitat (in real units, not number of cells).} \item{res}{Spatial resolution of the Raster.} \item{value}{The value(s) to populate the habitat with (will be recycled).} \item{width}{Width of the region (default: diam).} \item{height}{Height of the region (default: diam).} } \value{ A RasterLayer with nonnegative and missing values. } \description{ Simulates a random environment, with holes and varying carrying capacity. }

d059805170d2f5bd961c15e3e2ef91078d3f49f5

69b68868434fce241a10b100bcc8eb7ad564ecb2

/R/SepsisProjection.R

ecfbef9612597b151ca030f3b17f3d17b5246d14

[ "MIT" ]

permissive

jknightlab/SepstratifieR

1481d5777b6d179719ec3cf921512d1c7dbd7271

0ad77fb3447b41f0464b5577b8d2186bdbc31c54

refs/heads/main

2023-04-18T08:53:08.735906

2022-03-14T13:32:32

397,996,598

4

1

null

UTF-8

R

false

1,470

r

SepsisProjection.R

# Defining SepsisProjection class setClass("SepsisProjection", slots=list(gene_set="character", predictors="numeric", cosine_similarities = "data.frame", SRS = "factor", SRS_probs = "data.frame", SRSq = "numeric") ) # Defining methods associated with the SepsisProjection class setMethod("show", "SepsisProjection", function(object) { cat("SepsisProjection\n\n") cat("Gene set used: ", object@gene_set, "\n") cat(length(object@predictors)," predictor variables\n\n", sep="") if(length(object@predictors) > 0) { cat("Predictor variables: ") cat(utils::head(names(object@predictors), n=4),"...", sep=", ") cat("\nSRS: ") cat(as.character(object@SRS)) cat("\nSRSq: ") cat(as.numeric(object@SRSq)) cat("\n") } } ) # Creating a constructor SepsisProjection method SepsisProjection <- function(gene_set=NULL, predictors=NULL, cosine_similarities=NULL, SRS=NULL, SRS_probs=NULL, SRSq=NULL) { methods::new( "SepsisProjection", gene_set = as.character(gene_set), predictors = as.numeric(predictors), cosine_similarities = data.frame(cosine_similarities), SRS = factor(SRS), SRS_probs = data.frame(SRS_probs), SRSq = as.numeric(SRSq) ) }

d2b368823a0339228497eaa6800bf0225ae8b9fc

5bf426ec23846b07687b4bbbe7f29a655001aa2d

/ExistingStrokeRiskExternalValidation/extras/codeToRun.R

624975daee556ba09a9d7a3fb8f85c447cf3bc65

[]

no_license

julwa/StudyProtocolSandbox

14a0ad48897f977878859e87e97ac2ff6ced173e

a2030d9706222ebb9eb955c51f12263b7559617f

refs/heads/master

2023-07-10T21:03:58.762135

2021-08-13T12:40:12

null

0

null

UTF-8

R

false

1,394

r

codeToRun.R

#file to run study library(PredictionComparison) library(ExistingStrokeRiskExternalValidation) options(fftempdir = 'T:/yourFftemp') dbms <- yourDbms user <- yourUsername pw <-yourPassword server <- Sys.getenv('server') port <- Sys.getenv('port') connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = dbms, server = server, user = user, password = pw, port = port) databaseName = 'friendlyDatabaseName' cdmDatabaseSchema <- 'yourCdmDatabaseSchema' cohortDatabaseSchema <- 'yourCohortDatabaseSchema' cohortTable <- 'existingStrokeVal' outputLocation <- 'C:/existingStrokeVal' ExistingStrokeRiskExternalValidation::main( connectionDetails=connectionDetails, oracleTempSchema = NULL, databaseName=databaseName, cdmDatabaseSchema=cdmDatabaseSchema, cohortDatabaseSchema=cohortDatabaseSchema, outputLocation=outputLocation, cohortTable=cohortTable, createCohorts = F, runAtria = F, runFramingham = F, runChads2 = F, runChads2Vas = F, runQstroke = F, summariseResults = F, packageResults = F, N=10) #submitResults(exportFolder=outputLocation, # dbName= databaseName, key, secret)

945a2c7d67f7df9d396e67cf71255764c41222d6

1fa9f418caddb65fb8feaa9b236764c98c61fd17

/src/libRbind/testEst.R

e29efe821ef5d726e8fa863bc30fc8cffb9150c5

[ "MIT" ]

permissive

MADAI/MADAIEmulator

485a19013de8d6160e50e2ed1886f94d3b0f8592

7d926ad04a791c7694defd88db41c13f7ee4e6aa

refs/heads/master

2016-09-01T19:39:03.556529

2012-11-27T15:16:39

2012-11-27T15:19:50

4,770,211

2

0

null

UTF-8

R

false

8,518

r

testEst.R

## some functions for testing the estimation parts of the emulator ## ## ccs, cec24@phy.duke.edu, aug-2011 ## ## aims: ## 1: plot lhood over a plane while keeping other hyperparams fixed (lhoodcompare) ## 2: compare max lhood for various regression and cov fn models (not implemented yet) ## 3: do a k-sets withold test to build up a distribution of deviates (holdbacktest) ## ## inputs: ## modelData, trainingData. ## these will be supplied in model.in{training=trainingData, xmodel=modelData} ## ## these fns are for a single observable, we're not using multidim directly ## ## rangeMatrix: 2x2, { { minA, maxA}, {minB, maxB} } ## thetaVec, vector of the "best" thetas for this function ## we'll run thetaA and thetaB over the supplied ranges while holding the other values ## fixed lhoodOverRange <- function(thetaA, thetaB, rangeMatrix, thetaVec, nthetas, nEvalPts, model.in) { lhood.mat <- matrix(0, nrow=nEvalPts, ncol=nEvalPts) nmodelPoints <- dim(model.in$xmodel)[1] nparams <- dim(model.in$xmodel)[2] cat("nparams: ", nparams, "\n") cat("nmodelPoints: ", nmodelPoints, "\n") stepA <- abs((rangeMatrix[1,1] - rangeMatrix[1,2]) / nEvalPts) stepB <- abs((rangeMatrix[2,1] - rangeMatrix[2,2]) / nEvalPts) pointList <- matrix(0, nrow=nEvalPts**2, ncol=nthetas) for(i in 1:nEvalPts){ for(j in 1:nEvalPts){ subValA <- rangeMatrix[1,1] + i * stepA subValB <- rangeMatrix[2,1] + j * stepB subVec <- thetaVec subVec[thetaA] <- subValA subVec[thetaB] <- subValB pointList[j+nEvalPts*(i-1),] = subVec } } lhoodRes <- callEvalLhoodList(model.in, pointList, nEvalPts**2, nmodelPoints, nparams=nparams, nthetas=nthetas) lhoodRes } ## ## vary 2 hyperparams and plot the lhood while keeping the remaining hyperparams fixed ## plot this as a set of images lhoodCompare <- function(estim.result=estimResult, obsIndex=1, ngridPts=32, titleAdditional=NULL){ namesfirst <- c("scale", "nugget") nthetas <- length(estim.result$thetas[obsIndex,]) par(mfrow=c(nthetas,nthetas), mar=c(2,2,0,0), oma=c(2,2,2,2), cex.axis=0.8, cex.lab=0.8, cex.main=0.8) # compute the lhood of the initial theta set model <- list(xmodel=estim.result$des.scaled, training=estim.result$train.scaled) lhoodMain <- callEvalLhoodList(model, estim.result$thetas[obsIndex,], 1, nmodelPoints=dim(model$xmodel)[1], nparams=dim(model$xmodel)[2], nthetas=nthetas) print(lhoodMain) # all this to make a grid for(i in 1:nthetas){ for(j in 1:nthetas){ # don't plot in the upper right corner if(j > i){ plot(c(0,1),c(0,1),ann=F,bty='n',type='n',xaxt='n',yaxt='n') } else if(j == i){ # on the diagonals plot the names of the variables plot(c(0,1),c(0,1),ann=F,bty='n',type='n',xaxt='n',yaxt='n') if(i <= 2){ buffer <- namesfirst[i] } else { buffer <- paste("theta:", (i-2)) } text(x=0.5, y=0.5, buffer, cex=1.5) } else { # actually plot a slice in the lhood plane plotLhoodSlice(estim.result, j, i, obsIndex, ngridPts) } } } par(mfrow=c(1,1), mar=c(2,2,2,2)) buffer<-paste(titleAdditional, "L = ", round(lhoodMain$lhood,digits=2)) title(main=buffer, line=-1) } plotLhoodSlice <- function(estim.result=estimResult, dimA=5, dimB=6, obsIndex=1, ngridPts=32, nconts=10) { ranges <- matrix(0, nrow=2, ncol=2) if(dimA > 2){ ranges[1,] <- c(-10,10) } else if (dimA == 2){ ranges[1,] <- c(0.00001, 0.5) } else { ranges[1,] <- c(0.00001, 5) } if(dimB > 2){ ranges[2,] <- c(-10,10) } else if (dimB == 2){ ranges[2,] <- c(0.00001, 0.5) } else { ranges[2,] <- c(0.00001, 5) } # used the estimated thetas for the first bin fixedValVec <- estim.result$thetas[obsIndex,] print(fixedValVec) cat("ranges A: " , ranges[1,], "\n") cat("ranges B: " , ranges[2,], "\n") model <- list(xmodel=estim.result$des.scaled, training=estim.result$train.scaled[,obsIndex]) res <- lhoodOverRange(dimA,dimB ,ranges, fixedValVec, nthetas=length(fixedValVec), nEvalPts=ngridPts, model) xrange <- seq(from=ranges[1,1], to=ranges[1,2], length.out=ngridPts) yrange <- seq(from=ranges[2,1], to=ranges[2,2], length.out=ngridPts) lhoodMatrix <- t(matrix(res$lhood, ncol=ngridPts, nrow=ngridPts)) bufferX <- paste("theta :", dimA) bufferY <- paste("theta :", dimB) #levels=c(-300, -200,-150, -100, -75, -65, -50, -40, -32, -25, -15, -10) #image(xrange, yrange, lhoodMatrix, xlab=bufferX, ylab=bufferY, col=topo.colors(length(levels)-1), breaks=levels) image(xrange, yrange, lhoodMatrix, xlab=bufferX, ylab=bufferY) contour(xrange, yrange, lhoodMatrix, nlevels=nconts, add=TRUE, labcex=0.8) points(fixedValVec[dimA], fixedValVec[dimB], pch=3, cex=1.5, lwd=2) } # partition the nmodelpoints into nSets subsets, estimate the model # for the complement of each subset and then compute the deviation on the # witheld points HoldBackTest <- function(des.scaled, train.scaled, nSets=7, fixNuggetOption=NULL){ nmodelPts.tot <- dim(des.scaled)[1] nparams <- dim(des.scaled)[2] nthetas <- nparams + 2 ## currently # the number of points in one of the withold sets subSetLength <- floor( nmodelPts.tot / nSets) if(nmodelPts.tot %% nSets != 0){ nSets <- nSets + 1 # add an additional set to hold the remainder } model.list <- vector("list", nSets) partial.des <- vector("list", nSets) partial.train <- vector("list", nSets) test.pts <- vector("list", nSets) true.values <- vector("list", nSets) subList <- vector("list", nSets) thetas.full <- matrix(0, ncol=nthetas, nrow=nSets) lhoods <- rep(NA, nSets) # loop over all the sets creating the partial designs, we make sure that the # final set contains the remainder for(i in 1:nSets){ if(i < nSets){ subList[[i]] <- seq(from=((i-1)*subSetLength+1), to=(i*subSetLength)) } else { subList[[i]] <- seq(from=((i-1)*subSetLength+1), nmodelPts.tot) } # these will be the design points for each partial run partial.des[[i]] <- des.scaled[-(subList[[i]]), ] # the training values for each partial run partial.train[[i]] <- train.scaled[-(subList[[i]])] # the test locations test.pts[[i]] <- des.scaled[subList[[i]], ] # the values at the test locations true.values[[i]] <- train.scaled[subList[[i]]] model.list[[i]] <- list(xmodel=partial.des[[i]], training=partial.train[[i]]) } # estimate the hyper params for each model # we support the fixedNugget option here. # for(i in 1:nSets){ npts <- length(model.list[[i]]$training) thetas.full[i,] <- callEstimate(model.list[[i]], nmodelpts=npts, nparams=nparams, nthetas=nthetas, fixedNugget=fixNuggetOption) ## it's also fun to compute the lhood for each set temp <- callEvalLhoodList(model.list[[i]], thetas.full[i,], nevalPoints=1, nmodelPoints=npts, nparams=nparams, nthetas=nthetas) lhoods[i] <- temp$lhood } # hold the results and the deviates emu.res <- vector("list", nSets) devs <- c() # now we want to compute the deviates for each set for(i in 1:nSets){ nEmuPts <- length(true.values[[i]]) npts <- length(model.list[[i]]$training) # start by emulating each partial model at its test.pts locations emu.res[[i]] <- callEmulateAtList(model.list[[i]], thetas.full[i,], test.pts[[i]], nemupts=nEmuPts, nmodelpoints=npts, nparams=nparams, nthetas=nthetas) ## we quantify the deviations as the squared diff from the real value scaled by ## the predicted variance at this locn devs <- c(devs, (emu.res[[i]]$mean - true.values[[i]])**2 / (emu.res[[i]]$var)) } ## we return the vector of deviates, the test points where we compute the deviates ## the emulated means and vars at these test points, the true values at the points ## the lhoods of each of the models and the thetas specifying each model ## and also the models themselves final.res <- list(deviates=devs, test.pts=test.pts, emu.res=emu.res, true.values=true.values, lhoods=lhoods, thetas=thetas.full, models=model.list) } # use this to test holdbacktest testWT <- function(){ options(error=utils::recover) source("emuYields.R") res <- HoldBackTest(estimResult$des.scaled, estimResult$train.scaled[,1], nSets=20) invisible(res) }

a68f6e445c72567683821a81044ec25880b7fcfc

7fd749dc1a52e201dfe433fa0da403414687f5c0

/R/chi_diag.boot.R

35812ed81c2c6eb5a1dba7864d917f2a96327aa4

[]

no_license

cran/r4lineups

a179b83afdc8b8c68ac812cd600e86bc0ece48e8

6753d4662d34ea39261878f9f0584788be7c23f7

refs/heads/master

2020-03-27T03:58:08.061355

2018-07-18T12:20:02

145,902,377

0

null

UTF-8

R

false

109

r

chi_diag.boot.R

chi_diag.boot <- function(df, d=d){ q <- df[d] %>% sum((df[2,]-log(d_bar(df))/(df[1,]))) return(q) }

e1cf90d2e1d2bda9121e176023db74392e9de362

568db5b2c448e80d459f4f9c923d2efbd0acc120

/R/BCIPlot.R

324a3c48c79bcbf719d799889cf01117defffe85

[]

no_license

bromsk/NCRNbirds

0becc5be7b1ee521fe4ee14d6c7e6f6751047b10

25b2c87700ded8f96800193aa9a94add9667bac8

refs/heads/master

2020-04-21T15:09:45.937162

2018-12-06T21:16:10

null

0

null

UTF-8

R

false

7,587

r

BCIPlot.R

#' @include NCRNbirds_Class_def.R #' @include BCI.R #' #' @title BCIPlot #' #' @importFrom dplyr case_when group_by n_distinct pull right_join summarize #' @importFrom ggplot2 aes annotate element_line geom_point ggplot ggtitle guides guide_legend labs #' @importFrom ggplot2 scale_color_manual scale_x_continuous scale_y_continuous theme theme_classic #' @importFrom magrittr %>% #' @importFrom purrr map pmap #' @importFrom tidyr full_seq #' @importFrom viridis viridis_pal #' @importFrom tidyr replace_na #' #' @description Plots BCI score over time. #' #' @param object An \code{NCRNbirds} object a \code{list} of such objects, or a \code{data.frame} like that produced by \code{birdRichness()}. #' @param years A numeric vector. Indicates which years should be graphed. #' @param points A character vector of point names. Only these points will be used. #' @param visits A length 1 numeric vector, defaults to NA. Returns data only from the indicated visits. #' @param times A numeric vector of length 1. Returns only data from points where the number of years that a point has been vistied is greater or equal to the value of \code{times}. This is determined based on the data found in the \code{Visits} slot. #' @param plot_title Optional, A title for the plot. #' @param point_num An optional numeric vector indicating the number of points sampled each year. If \code{object} is a \code{NCRNbirds} object #' then this will be calculated automatically. If \code{object} is a \code{data.frame} or a \code {list} than this can be provided by the user. #' @param output Either "total" (the default) or "list". Only used when \code{object} is a \code{list}. #' @param ... Additional arguments passed to \code{\link{birdRichness}} #' #' @details This function produces a graph the Bird community Index over time. It does this by using the output of #' the \code{\link{BCI}} function, and averging the plot values for the park's yearly value. The data is then passed on #' to ggplot2 for graphing. Typically this is done automatically by providing an \code{NCRNbirds} object or a \code{list} #' of such objects. If the user wishes to provide their own \code{data.frame} it should have 3 columns, \code{Year, BCI, BCI_Category} #' and each row should be data from single year. #' #' @export setGeneric(name="BCIPlot",function(object,years=NA, points=NA,visits=NA, times=NA, plot_title=NA, point_num=NA,output="total", ...){standardGeneric("BCIPlot")}, signature="object") setMethod(f="BCIPlot", signature=c(object="list"), function(object,years,points,visits, times, plot_title, point_num, output, ...) { switch(output, total={ visits<-if(anyNA(visits)) getDesign(object,info="visits") %>% unlist %>% max %>% seq else visits years<-if(anyNA(years)) getVisits(object, points=points, visits=visits, times=times) %>% pull(Year) %>% unique %>% sort %>% full_seq(1) else years graphdata<-data.frame(Year=years,BCI=NA, BCI_Category=NA) graphdata$BCI<-(years %>% map(~BCI(object=object, years=.x, points=points, visits=visits, times=times,output="dataframe",...),...) %>% map("BCI") %>% map(mean) %>% unlist %>% round(0)) graphdata<-graphdata %>% mutate (BCI_Category=case_when( BCI <40.1 ~"Low Integrity", BCI>=40.1 & BCI<52.1 ~ "Medium Integrity", BCI>=52.1 & BCI < 60.1 ~ "High Integrity", BCI>=60.1 ~ "Highest Integrity" ), BCI_Category=factor(BCI_Category, levels=c("Low Integrity","Medium Integrity", "High Integrity","Highest Integrity"))) if (all(is.na(point_num))) point_num<-map(visits, function(visits){ map(years, function(years) getVisits(object=object, years=years, visits=visits, times=times) %>% nrow) %>% unlist(F)}) return(BCIPlot(object=graphdata, plot_title=plot_title, point_num = point_num)) }, list={ return(lapply(X=object, FUN=BCIPlot, years=years, points=points, visits=visits, times=times, plot_title=plot_title, point_num=point_num)) } ) }) setMethod(f="BCIPlot", signature=c(object="NCRNbirds"), function(object,years,points,visits, times, plot_title=NA, ...){ visits<-if(anyNA(visits)) 1:getDesign(object,info="visits") else visits years<-if(anyNA(years)) getVisits(object, points=points, visits=visits, times=times) %>% pull(Year) %>% unique %>% sort %>% full_seq(1) else years graphdata<-data.frame(Year=years,BCI=NA, BCI_Category=NA) graphdata$BCI<-(years %>% map(~BCI(object=object, years=.x, points=points, visits=visits, times=times,...),...) %>% map("BCI") %>% map(mean) %>% unlist %>% round(0)) graphdata<-graphdata %>% mutate (BCI_Category=case_when( BCI <40.1 ~"Low Integrity", BCI>=40.1 & BCI<52.1 ~ "Medium Integrity", BCI>=52.1 & BCI < 60.1 ~ "High Integrity", BCI>=60.1 ~ "Highest Integrity" ), BCI_Category=factor(BCI_Category, levels=c("Low Integrity","Medium Integrity", "High Integrity","Highest Integrity"))) if (all(is.na(point_num))) point_num<-map(visits, function(visits){ map(years, function(years) getVisits(object=object, years=years, visits=visits, times=times) %>% nrow) %>% unlist(F)}) plot_title<-if(is.na(plot_title)) paste0("Bird Community Index for ",getParkNames(object, name.class="long")) else plot_title return(BCIPlot(object=graphdata, plot_title=plot_title, point_num = point_num)) }) setMethod(f="BCIPlot", signature=c(object="data.frame"), function(object, plot_title, point_num){ BCIColors<-viridis_pal()(4) names(BCIColors)<-c("Low Integrity", "Medium Integrity", "High Integrity", "Highest Integrity") SampEffort<-if(!all(is.na(point_num))) pmap(point_num, paste, sep=",") %>% unlist else NA integer_breaks<-min(object$Year):max(object$Year) YearTicks<- if(!all(is.na(point_num))) paste0(integer_breaks, "\n(", SampEffort,")") else integer_breaks GraphOut<-ggplot(data=object, aes(x=Year, y=BCI, color=BCI_Category)) + annotate("rect", ymin=0, ymax=40, xmin=-Inf, xmax=Inf, fill=BCIColors[1], alpha=0.3) + annotate("rect", ymin=40, ymax=52, xmin=-Inf, xmax=Inf, fill=BCIColors[2], alpha=0.3) + annotate("rect", ymin=52, ymax=60, xmin=-Inf, xmax=Inf, fill=BCIColors[3], alpha=0.3) + annotate("rect", ymin=60, ymax=80, xmin=-Inf, xmax=Inf, fill=BCIColors[4], alpha=0.3) + geom_point(size=4) + scale_color_manual(values=BCIColors, drop=FALSE) + guides(color=guide_legend(reverse=T, title ="BCI Category"))+ scale_x_continuous(breaks=integer_breaks, minor_breaks=integer_breaks, labels=YearTicks) + scale_y_continuous(limits=c(0,80), expand=c(0,0)) + labs(y=" Bird Community Index", caption="Values in parentheses indicate the number of points monitored each visit of each year.") + {if(!is.na(plot_title)) ggtitle(plot_title)} + theme_classic()# + return(GraphOut) })

09afc44965bc26503d11f8b7152510363635fab3

6dba9fa38b058cee5e4dbfa6b3b7ac5e3c88c0f0

/loop2.R

32d3677e9217100b360af8d3f7ae23bbe94e1eb0

[]

no_license

AngelicaValdes/Tesis

2c7ea3889b8d720ec7f31d5475e7c40f0ed4ba56

82d0d5dec09bb016256b18aee13feec864695bea

refs/heads/main

2023-08-24T13:08:38.825748

2021-10-08T17:12:13

413,637,703

0

null

UTF-8

R

false

7,114

r

loop2.R

dotR <- file.path(Sys.getenv("HOME"), ".R") if (!file.exists(dotR)) dir.create(dotR) M <- file.path(dotR, "Makevars") if (!file.exists(M)) file.create(M) cat("\nCXXFLAGS=-O3 -Wno-unused-variable -Wno-unused-function", file = M, sep = "\n", append = TRUE) cat('Sys.setenv(BINPREF = "C:/Rtools/mingw_$(WIN)/bin/")', file = file.path(Sys.getenv("HOME"), ".Rprofile"), sep = "\n", append = TRUE) cat("\nCXXFLAGS += -Wno-ignored-attributes -Wno-deprecated-declarations", file = M, sep = "\n", append = TRUE) #Setear directorio library("rstan") rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores()) load('/mydata_server_3.RData') mod<-stan_model("delay2.stan") for (j in clientes$child_id[(count_clientes+1):(677)]){ cliente<-subset(BD,child_id==j) cliente<-cliente[order(cliente$Periodo_final, cliente$route_id),] #inicializacion de variables h<-length(cliente$Periodo_final) theta<-rep(param[1],h) sigma<- rep(param[2],h) xi<-rep(param[3],h) mean_rhat<-rep(0,h) max_rhat<-rep(0,h) se_mean<-rep(0,h) thetar<-data.frame(theta1=c(rep(param[4],h)),theta2=c(rep(param[4],h)),theta3=c(rep(param[4],h)),theta4=c(rep(param[4],h))) #algoritmo para obtener pronostico for (i in 1:h){ nRoutes <- cliente$child_route_num[1] nObs <- nrow(cliente[1:i,]) obs <- as.matrix(cliente[1:i,6:(5+nRoutes)], nrow = nrow(cliente[1:i,])) y <- as.matrix(cliente[1:i,12:(11+nRoutes)], nrow = nrow(cliente[1:i,])) stan.data = list(nRoutes = nRoutes, nObs = nObs, obs = obs, y = y) reg_fit <- sampling(mod, data=stan.data, iter=50000*nObs^(-0.5), chains=2, save_warmup = FALSE) summary <- summary(reg_fit, pars = c("theta","sigma","xi","thetar"), probs = c(0.025,0.5,0.975))$summary theta[i]<-summary[1] sigma[i]<-summary[2] xi[i]<-summary[3] mean_rhat[i]<-mean(summary[,8]) max_rhat[i]<-max(summary[,8]) se_mean[i]<-summary[1,2] if (nRoutes >= 1){thetar$theta1[i]<-summary[4]} if (nRoutes >= 2){thetar$theta2[i]<-summary[5]} if (nRoutes >= 3){thetar$theta3[i]<-summary[6]} if (nRoutes >= 4){thetar$theta4[i]<-summary[7]} files <- list.files(tempdir(), full.names = T, pattern = "^file") file.remove(files) } #Introducir el valor de "diferencia" en la columna correspondiente for(i in 1:h){ d<-cliente$Periodo_final[i] if(cliente$route_id[i]==1){ Base_pronostico$diff_1[d+f]<-cliente$difference[i] } if(cliente$route_id[i]==2){ Base_pronostico$diff_2[d+f]<-cliente$difference[i] } if(cliente$route_id[i]==3){ Base_pronostico$diff_3[d+f]<-cliente$difference[i] } if(cliente$route_id[i]==4){ Base_pronostico$diff_4[d+f]<-cliente$difference[i] } } #datos para el cliente sin periodos repetidos cliente$Periodos_repetidos<-1 cliente$Diferencia_prom<-rep(NA,h) p<-rep(1,h) for (i in 1:(h-1)){ if(cliente$Periodo_final[i]==cliente$Periodo_final[i+1] & cliente$route_id[i]==cliente$route_id[i+1]){ p[i+1]<-p[i]+1 cliente$Periodos_repetidos[i+1]<-cliente$Periodos_repetidos[i]+1 cliente$Diferencia_prom[i+1]<-mean(cliente$difference[(i+2-p[i+1]):(i+1)]) cliente$Periodo_final[i]<-0 theta[i]<-NA sigma[i]<-NA xi[i]<-NA mean_rhat[i]<-NA max_rhat[i]<-NA se_mean[i]<-NA thetar[i,]<-NA } } cliente<-subset(cliente,Periodo_final>0) theta<-theta[is.na(theta)==F] sigma<-sigma[is.na(sigma)==F] xi<-xi[is.na(xi)==F] mean_rhat<-mean_rhat[is.na(mean_rhat)==F] max_rhat<-max_rhat[is.na(max_rhat)==F] se_mean<-se_mean[is.na(se_mean)==F] thetar<-subset(thetar,is.na(theta1)==FALSE) #algoritmo para introducir los datos en las variables h<-length(cliente$Periodo_final) #datos para primer periodo Base_pronostico$theta[1+f]<-param[1] Base_pronostico$sigma[1+f]<-param[2] Base_pronostico$xi[1+f]<-param[3] Base_pronostico$theta_1[1+f]<-param[4] if(cliente$child_route_num[1]>=2){Base_pronostico$theta_2[1+f]<-param[4]} if(cliente$child_route_num[1]>=3){Base_pronostico$theta_3[1+f]<-param[4]} if(cliente$child_route_num[1]>=4){Base_pronostico$theta_4[1+f]<-param[4]} #datos para pronostico for(i in 1:h){ d<-cliente$Periodo_final[i] if(cliente$child_route_num[1]>=1){ Base_pronostico$theta_1[d+1+f]<-thetar$theta1[i] } if(cliente$child_route_num[1]>=2){ Base_pronostico$theta_2[d+1+f]<-thetar$theta2[i] } if(cliente$child_route_num[1]>=3){ Base_pronostico$theta_3[d+1+f]<-thetar$theta3[i] } if(cliente$child_route_num[1]>=4){ Base_pronostico$theta_4[d+1+f]<-thetar$theta4[i] } if(is.na(cliente$Diferencia_prom[i])==FALSE){ if(cliente$route_id[i]==1){ Base_pronostico$diff_1[d+f]<-cliente$Diferencia_prom[i] } if(cliente$route_id[i]==2){ Base_pronostico$diff_2[d+f]<-cliente$Diferencia_prom[i] } if(cliente$route_id[i]==3){ Base_pronostico$diff_3[d+f]<-cliente$Diferencia_prom[i] } if(cliente$route_id[i]==4){ Base_pronostico$diff_4[d+f]<-cliente$Diferencia_prom[i] } } Base_pronostico$theta[d+1+f]<-theta[i] Base_pronostico$sigma[d+1+f]<-sigma[i] Base_pronostico$xi[d+1+f]<-xi[i] Base_pronostico$mean_rhat[d+1+f]<-mean_rhat[i] Base_pronostico$max_rhat[d+1+f]<-max_rhat[i] Base_pronostico$se_mean[d+1+f]<-se_mean[i] Base_pronostico$route[d+f]<-cliente$route_id[i] Base_pronostico$count_period[d+f]<-cliente$Periodos_repetidos[i] Base_pronostico$route_num[d+f]<-cliente$child_route_num[i] } for(i in 2:(max(BD$Periodo_final)+1)){ if(is.na(Base_pronostico$theta[i+f]))Base_pronostico$theta[i+f]<-Base_pronostico$theta[i-1+f] if(is.na(Base_pronostico$sigma[i+f]))Base_pronostico$sigma[i+f]<-Base_pronostico$sigma[i-1+f] if(is.na(Base_pronostico$xi[i+f]))Base_pronostico$xi[i+f]<-Base_pronostico$xi[i-1+f] for (r in 1:mean(BD$child_route_num[BD$child_id==j])){ if(r==1){ if(is.na(Base_pronostico$theta_1[i+f]))Base_pronostico$theta_1[i+f]<-Base_pronostico$theta_1[i-1+f] } if(r==2){ if(is.na(Base_pronostico$theta_2[i+f]))Base_pronostico$theta_2[i+f]<-Base_pronostico$theta_2[i-1+f] } if(r==3){ if(is.na(Base_pronostico$theta_3[i+f]))Base_pronostico$theta_3[i+f]<-Base_pronostico$theta_3[i-1+f] } if(r==4){ if(is.na(Base_pronostico$theta_4[i+f]))Base_pronostico$theta_4[i+f]<-Base_pronostico$theta_4[i-1+f] } } } f<-f+max(BD$Periodo_final)+1 count_clientes<-count_clientes+1 print("CLIENTE NUMERO:") print(count_clientes) unlink('/mydata_server_3.RData', recursive = TRUE) save.image(file = '/mydata_server_3.RData') rm(list = ls()) gc() load('/mydata_server_3.RData') files <- list.files("\\AppData\\Local\\Temp\\3", full.names = T, pattern = "Rtmp") unlink(files, recursive = TRUE) }

c75540601561f4f6a5acf511317aafbde5f4db89

bdadf9e6d2f86a00793fad1806b743cb755b4277

/La Palma Skript.R

64a00a1df7d98a1f3f5a8b30ea62db61781cb495

[]

no_license

s2jsvoge/R-scripts-La-Palma

f050dc8a94b2416f9d1842c8bc5757ad4537ddc2

2b2755689a8a973eb4ec9d6cc2e7fd6490c6a417

refs/heads/master

2021-01-01T03:52:49.785217

2016-05-26T12:55:54

59,143,614

1

0

null

2016-05-28T16:47:02

2016-05-18T19:06:37

R

ISO-8859-1

R

false

5,521

r

La Palma Skript.R

###################################### ##### Fog precipitation efficiency ### ##### in different forest types ###### ##### on La Palma, Canary Islands##### ###################################### # Hypothesis: Fog precipitation efficiency in cloud influenced forests on La Palma # is determinded by vertical vegetation structure and leaf morphology. # Pine forest is most efficient in absorbing precipitation from cloud layer. # Dear La Palma Mates, unter "##"Überschriften bitte den Code einfügen. # Falls euch noch weitere Dinge einfallen, die wir berechnen könnten, einfach mit einer Überschrift hinzufügen. # Bitte nicht vergessen kurz mit "#" zu schreiben, was die neu kreierte Variable ist # z.b.: b1 - fog prec. Standort 1 Tag 1 oder entsprechende Überschriften ;) #################### ### Load in Data ### #################### setwd("D:/Studium/Master Bayreuth/Science Schools/La Palma Schience School/results/csv") descr_nets<-read.table(file="descriptions_nets.csv",sep=";",dec=",", head=TRUE) descr_control<-read.table(file="descriptions_control.csv",sep=";",dec=",", head=TRUE) descr_devices<-read.table(file="descriptions_devices.csv",sep=";",dec=",", head=TRUE) ######################### ### Site descriptions ### ######################### # subsets per location dev_site1<-subset(descr_devices, descr_devices$Location==1) dev_site2<-subset(descr_devices, descr_devices$Location==2) dev_site3<-subset(descr_devices, descr_devices$Location==3) ## Altitude alt_site1_mean<-mean(dev_site1$Altitude.device) # 1225.11 m alt_site2_mean<-mean(dev_site2$Altitude.device) # 1241.33 m alt_site3_mean<-mean(dev_site3$Altitude.device) # 1482.44 m ## Exposition exp_site1<-dev_site1$Aspect.of.Location exp_site2<-dev_site2$Aspect.of.Location exp_site3<-dev_site3$Aspect.of.Location ## average Canopy cover cc_site1_mean<-mean(dev_site1$Canopy.density) #79.28 cc_site2_mean<-mean(dev_site2$Canopy.density) # 82.19 cc_site3mean<-mean(dev_site3$Canopy.density) # 51.86 cc_site1<-subset(descr_devices$Canopy.density, descr_devices$Location==1) cc_site2<-subset(descr_devices$Canopy.density, descr_devices$Location==2) cc_site3<-subset(descr_devices$Canopy.density, descr_devices$Location==3) ## stand density sd_site1_mean<-mean(dev_site1$Stand.density..Bitterlich.) # 15.33 sd_site2_mean<-mean(dev_site2$Stand.density..Bitterlich.) # 20.78 sd_site3_mean<-mean(dev_site3$Stand.density..Bitterlich.) # 18.89 sd_site1<-subset(descr_devices$Stand.density..Bitterlich., descr_devices$Location==1) sd_site2<-subset(descr_devices$Stand.density..Bitterlich., descr_devices$Location==2) sd_site3<-subset(descr_devices$Stand.density..Bitterlich., descr_devices$Location==3) ## volume of crown ## average height of trees ## variability in height of trees ## average vertical structure ## variability in vertical structure ## number of species ## which species #################################### ### Measured daily precipitation ### #################################### prec<-read.csv("prec_devices.csv",header=TRUE,sep=";", dec=",") ## daily precipitation per device daily_prec_dev<-subset(prec, prec$Precipitation..ml.>0) # daily_prec_dev is subset of precipitation measurements for each day # for sampling devices without NA and without 0ml # !!! CHANGE: include 0 ml but exclude NA plot(daily_prec_dev$Precipitation..ml.~daily_prec_dev$Device, col=as.factor(daily_prec_dev$Date)) ## mean daily precipitation per site ?tapply tapply(X=daily_prec_dev$Precipitation..ml., INDEX=as.numeric(daily_prec_dev$Location), as.numeric(daily_prec_dev$Date), FUN=mean) # funktioniert noch nicht ## mean precipitation per device over all measurement days ## daily precipitation in control devices # Are there differences in control devices of each site? (canpoy cover correlation?) ## mean daily precipitation in control devices per site ## device prec. substracted with control device average per day of corresponding site ## daily fog precipitation per device ## mean daily fog precipitation per site ## mean fog precipitation per device over all measurement days ################################################################ ### Correlations of vegetation parameters with precipitation ### ############################################################### ## with canopy cover ## with species ## with vertical structure --> Index??? # meterzählungen joscha # tree hights ## with crown volume ## with forest type ## with altitude ## with observed weather conditions ###################################################################### ### Precipitation captured by nets as validation of cloud presence ### ###################################################################### # As discussed with Anke we need to standardize somehow the potential fog precipitaion # of each site and day to cheack whether the cloud presence/ density etc is comparable # at a certain day between the different sites to exclude the possibility that # we captured differences of meso-meteorology instead of vegetation driven differences. ## Correlation between net density and fog precipitation? ## with exposition of nets

a39281cf974115bf941c5899cbe24597fbab737f

f9f42e00cdc3ddb75f4496733fbba28ca1f740b0

/R/actinoUtils.R

833bf40550e1492fc03fbf462f57509d9fb70a69

[]

no_license

richardsprague/actino

20f36f951cb7a45294225005de8c1e69fc4a7fdd

f4bbaa1c7cf3f520de34e53ef340877ebc9ee07d

refs/heads/master

2021-12-09T21:08:21.017115

2021-12-03T23:37:40

80,489,917

0

2

null

UTF-8

R

false

1,062

r

actinoUtils.R

# actinoUtils.R #' @title Dataframe at a specific rank #' @description turn a uBIome data frame into a matrix at a specific rank #' @param df a well-formed dataframe created by join_all_ubiome_files_full #' @param rank tax_rank (default = "genus") #' @importFrom stats na.omit #' @export dataframe_at_rank <- function(df, rank="genus" ){ z <- lapply(df[,1],function(x) { l = tax_rank_of_full_taxa(x) return(c(l[[1]],l[[2]])) }) names(z) = NULL z.df = t(as.data.frame(z)) row.names(z.df) = NULL new.df <- na.omit(data.frame(z.df, df[,-1])) colnames(new.df)[1:2] = c("tax_rank","tax_name") return(new.df[new.df$tax_rank==rank,][-1]) } #' @title Matrix version of a dataframe at a specific rank #' @description turn a uBIome data frame into a matrix at a specific rank #' @param df a well-formed dataframe created by join_all_ubiome_files_full #' @param rank tax_rank (default = "genus") #' @export matrix_at_rank <- function(df, rank = "genus"){ mf <- dataframe_at_rank(df,rank) m <- as.matrix(mf[,-1]) rownames(m) <- mf[,1] m }

3bf32773724e318d5841b54ece315e9973bdfbba

40a72b0add98f0ddadac4e07e64287b2d2dbb22a

/r/result_plots_v2.r

4a5704cdf401a4b3666836dba4525a96151b74bf

[]

no_license

andydawson/stepps-calibration

a5e8c1a2fafef07b070cbab444967fcbebb66602

c72cd26600fb1d82666264f962c5176fa0f8beb4

refs/heads/master

2020-05-27T17:23:08.311656

2018-04-13T14:04:11

null

0

null

UTF-8

R

false

6,443

r

result_plots_v2.r

library(rstan) library(ggplot2) library(fields, quietly=TRUE) library(RColorBrewer) wd = getwd() ##################################################################################### # user pars ##################################################################################### path_utils = 'r/utils' path_data = 'r/dump' path_out = 'output' path_figs = 'figures' suff_dat = '12taxa_mid_comp_v0.1' save_plots = TRUE rescale = 1e6 ##################################################################################### # read in data and source utils ##################################################################################### source(file.path(path_utils, 'process_funs.r')) source(file.path(path_utils, 'plot_funs.r')) load(sprintf('%s/cal_data_%s.rdata', path_data, suff_dat)) path_out = 'output' run1 = list(suff_fit = 'cal_g_v0.3', kernel = 'gaussian', one_psi = TRUE, one_gamma = TRUE, EPs = FALSE) run2 = list(suff_fit = 'cal_g_Kpsi_EPs_v0.3', kernel = 'gaussian', one_psi = FALSE, one_gamma = TRUE, EPs = TRUE) run3 = list(suff_fit = 'cal_g_Kpsi_Kgamma_EPs_v0.3', kernel = 'gaussian', one_psi = FALSE, one_gamma = FALSE, EPs = TRUE) run4 = list(suff_fit = 'cal_g_Kgamma_EPs_v0.3', kernel = 'gaussian', one_psi = TRUE, one_gamma = FALSE, EPs = TRUE) run5 = list(suff_fit = 'cal_pl_v0.3', kernel = 'pl', one_a = TRUE, one_b = TRUE, one_gamma = TRUE, EPs = FALSE) run6 = list(suff_fit = 'cal_pl_Kgamma_EPs_v0.3', kernel = 'pl', one_a = TRUE, one_b = TRUE, one_gamma = FALSE, EPs = TRUE) # runs = list(run1, run2, run3, run4, run5, run6) runs = list(run1, run5) fname = sprintf('%s/%s.csv', path_out, run5$suff_fit) fit_pl = read_stan_csv(fname) post_pl = rstan::extract(fit_pl, permuted=FALSE, inc_warmup=FALSE) fname = sprintf('%s/%s.csv', path_out, run1$suff_fit) fit_g = read_stan_csv(fname) post_g = rstan::extract(fit_g, permuted=FALSE, inc_warmup=FALSE) limits <- get_limits(centers_veg) # ##################################################################################### # # trace plots # ##################################################################################### # trace_plots(post_g, suff, save_plots=save_plots, fpath='figures') ##################################################################################### # read in data and source utils ##################################################################################### # power law sum_w_pl = build_sumw_pot(post_pl, K, N_pot, d_pot, run5) sum_w_pl # gaussian sum_w_g = build_sumw_pot(post_g, K, N_pot, d_pot, run1) sum_w_g col_substr_g = substr(colnames(post_g[,1,]), 1, 3) col_substr_pl = substr(colnames(post_pl[,1,]), 1, 3) one_psi = run1$one_psi if (one_psi){ psi = rep(mean(post_g[,1,which(col_substr_g == 'psi')]), K) } else { psi = colMeans(post_g[,1,which(col_substr_g == 'psi')]) } one_a = run5$one_a if (one_a){ a = rep(mean(post_pl[,1,which(col_substr_pl == 'a')]), K) } else { a = colMeans(post_pl[,1,which(col_substr_pl == 'a')]) } one_b = run5$one_b if (one_b){ b = rep(mean(post_pl[,1,which(col_substr_pl == 'b')]), K) } else { b = colMeans(post_pl[,1,which(col_substr_pl == 'b')]) } dx = 0.008 dr = 0.001 dvec = seq(0, 1, dr) # power law C_pl=build_sumw_pot(post_pl, K, N_pot, d_pot, run5)*dx^2 C_pl # gaussian C_g=build_sumw_pot(post_g, K, N_pot, d_pot, run1)*dx^2 C_g p_g = gaussian(dvec, psi[1]) p_pl = power_law(dvec, a[1], b[1]) pdf('figures/kernel_pdfs.pdf') plot(dvec*1e3, p_pl*dvec/C_pl[1], type='l', xlab='Radius', ylab='Density') lines(dvec*1e3, p_g*dvec/C_g[1], lty=2, col='blue') legend('topright', c('Power law', 'Gaussian'), col=c('black', 'blue'), lty=c(1,2)) dev.off() # cdf c_pl = cumsum(p_pl*dvec/C_pl[1]*2*pi)*dr c_g = cumsum(p_g*dvec/C_g[1]*2*pi)*dr pdf('figures/kernel_cdfs.pdf') plot(dvec*1e3, c_pl, type='l', xlab='Radius', ylab='Estimated cumulative density') lines(dvec*1e3, c_g, col='blue', lty=2) legend('bottomright', c('Power law', 'Gaussian'), col=c('black', 'blue'), lty=c(1,2)) dev.off() sum(p_pl*dvec*2*pi)*dr/C_pl sum(p_g*dvec*2*pi)*dr/C_g ############################################################################################################ # dispersal cdf ############################################################################################################ radius = seq(8000,1000000, by=4000) x_pot = seq(-528000, 528000, by=8000) y_pot = seq(-416000, 416000, by=8000) coord_pot = expand.grid(x_pot, y_pot) dmat = t(rdist(matrix(c(0,0), ncol=2), as.matrix(coord_pot, ncol=2))/rescale) # power law sum_w_pl = build_sumw_pot(post_pl, K, N_pot, d_pot, run5) r_int_pl = dispersal_decay(post_pl, dmat, sum_w_pl[1], radius/rescale, kernel='pl') # gaussian sum_w_g = build_sumw_pot(post_g, K, N_pot, d_pot, run1) r_int_g = dispersal_decay(post_g, dmat, sum_w_g[1], radius/rescale, kernel='gaussian') fifty = which.min(abs(r_int - 0.5)) ninety = which.min(abs(r_int - 0.9)) # segments = data.frame(x=c(0, 0, radius[fifty]/1e3, radius[ninety]/1e3), # xend=c(radius[fifty]/1e3, radius[ninety]/1e3, radius[fifty]/1e3, radius[ninety]/1e3), # y=c(r_int[fifty], r_int[ninety], 0.2, 0.2), # yend=c(r_int[fifty], r_int[ninety], r_int[fifty], r_int[ninety])) library(reshape) dat = data.frame(radius=radius/1e3, Gaussian=r_int_g, InversePowerLaw=r_int_pl) dat = melt(dat, id='radius') p <- ggplot(dat) + geom_line(aes(x=radius, y=value, color=factor(variable), linetype=variable)) p <- p + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + xlab('Radius') + ylab('Proportion of pollen') p <- p + theme(axis.text= element_text(size=rel(1)), axis.title=element_text(size=14)) # p <- p + geom_segment(data=segments, aes(x=x, y=y, xend=xend, yend=yend), linetype=2, colour='royalblue4') p <- p + xlim(0, 600) + ylim(0,1.0) p <- p + labs(color='Kernel', linetype='Kernel') p ggsave(file='figures/dispersal_vs_distance.pdf', scale=1) ggsave(file='figures/dispersal_vs_distance.eps', scale=1)

dd4e9d4d8ea37d2af98470b697ce1456040d8903

2d35f719a15e28eebe7b819c1fe9fac9d7c37862

/fig/CIcover10h.R

1e990438b810d1fd83facf73494ce8eb3a5bed86

[]

no_license

JDC-Shimada/bayesbook

f7fe5fcb40fd4a5cb2de6ce1ba53df05fd1830f9

9202919453c7fc575191ae7b950b53c6d9d328e8

refs/heads/master

2023-04-18T15:59:41.525953

2021-05-06T00:57:12

null

0

null

UTF-8

R

false

1,234

r

CIcover10h.R

args = commandArgs() basename = sub(".R$", "", sub("^--file=(.*/)?", "", args[grep("^--file=", args)])) if (length(basename) != 0) pdf(file=paste0(basename, "_tmp.pdf"), colormodel="gray", width=7, height=3) par(family="Palatino") par(mgp=c(2,0.8,0)) # title and axis margins. default: c(3,1,0) par(mar=c(3,3,2,2)+0.1) # bottom, left, top, right margins. default: c(5,4,4,2)+0.1 pb = function(x) (2/pi)*asin(sqrt(x)) # pbeta(x,0.5,0.5) qb = function(z) (sin(pi*z/2))^2 # qbeta(z,0.5,0.5) binomHPD = function(n, y) { a = y + 0.5 b = n - y + 0.5 f = function(x) pb(qbeta(x+0.95,a,b))-pb(qbeta(x,a,b)) x = optimize(f, c(0,0.05), tol=1e-8)$minimum qbeta(c(x,x+0.95), a, b) } HPD = sapply(0:10, function(y) binomHPD(10,y)) HPD[1,1] = 0 f = function(x) { p = dbinom(0:10, 10, x) sum(p * (HPD[1,] <= x & x <= HPD[2,])) } vf = Vectorize(f) curve(pb(vf(qb(x))), n=10001, xlab="", ylab="", xaxt="n", yaxt="n", ylim=c(0.72,1)) abline(h=pb(0.95), lty=3) axis(1, at=pb((0:10)/10), labels=(0:10)/10) axis(2, at=pb(seq(0.85,1,0.05)), labels=seq(0.85,1,0.05), las=1) dev.off() embedFonts(paste0(basename, "_tmp.pdf"), outfile=paste0(basename, ".pdf"), options="-c \"<</NeverEmbed []>> setdistillerparams\" -f ")

d825b5012d0648c79463c4dfe89db8e28474f527

b447d3c0c2b6a24cd0c4dce87e1626a92cf58dde

/ui.R

3821461fb7d76569853875a9f5167b9899371e31

[]

no_license

MrConradHarrison/CLEFT-Q-CAT-Score-Checker

3b4e693ee2deda97ab818690c6c57ffcb4631914

02d1deeb5ae896c9f08812c1281831b135ff71f5

refs/heads/main

2023-08-19T10:16:41.390055

2021-10-01T12:02:48

400,149,247

0

null

UTF-8

R

false

7,531

r

ui.R

shinyUI( navbarPage("CLEFT-Q CAT Score Checker", tabPanel( "Radar Plot", fluidPage( titlePanel("Radar Plot"), # Text br(), h4("Please use this Score Checker to compare CLEFT-Q CAT scores to median scores from the CLEFT-Q field test (Klassen", em("et al.,"), "2018)."), fluidRow( plotOutput(outputId = "RadarPlot") %>% withSpinner(color = "darkmagenta") ), br(), br(), br(), br(), br(), br(), br(), br(), br(), br(), fluidRow( column( 3, selectInput( inputId = "age_radar", label = "Age range (years)", choices = c("All ages (8-29)", "8-11", "12-15", "16-19", "20+") ), selectInput( inputId = "Sex_radar", label = "Gender", choices = c("All genders", "Male", "Female") ), selectInput( inputId = "Cleft_type_radar", label = "Cleft type", choices = c( "All cleft types", "Cleft lip", "Cleft palate", "Cleft lip and alveolus", "Cleft lip, alveolus and palate" ) ), selectInput( inputId = "Laterality_radar", label = "Laterality", choices = c("Unilateral or Bilateral", "Unilateral", "Bilateral") ) ), column(3, numericInput( inputId = "facescore", label = "Face Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "nosescore", label = "Nose Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "nostrilscore", label = "Nostrils Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "jawscore", label = "Jaws Score", value = 50, min = 1, max = 100 ) ), column(3, numericInput( inputId = "lipscore", label = "Lips Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "teethscore", label = "Teeth Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "scarscore", label = "Scar Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "schoolscore", label = "School Score", value = 50, min = 1, max = 100 ) ), column(3, numericInput( inputId = "socialscore", label = "Social Function Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "psychscore", label = "Psychological Function Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "sfscore", label = "Speech Function Score", value = 50, min = 1, max = 100 ), numericInput( inputId = "sdscore", label = "Speech Distress Score", value = 50, min = 1, max = 100 ) ) ) ) ), tabPanel( "Population Density", fluidPage( # Application title titlePanel("Population Density"), # Text br(), h4("Please use this Score Checker to compare a CLEFT-Q CAT score to scores obtained in the CLEFT-Q field test (Klassen", em("et al.,"), "2018)."), br(), br(), # Sidebar sidebarLayout( sidebarPanel( numericInput( inputId = "score", label = "Score", value = 50, min = 1, max = 100 ), selectInput( inputId = "scale", label = "CLEFT-Q CAT scale", choices = c( "Face", "Nose", "Nostrils", "Jaws", "Teeth", "Lips", "Scar", "School", "Speech function", "Speech distress", "Psychological function", "Social function" ) ), selectInput( inputId = "age", label = "Age range (years)", choices = c("All ages (8-29)", "8-11", "12-15", "16-19", "20+") ), selectInput( inputId = "Sex", label = "Gender", choices = c("All genders", "Male", "Female") ), selectInput( inputId = "Cleft_type", label = "Cleft type", choices = c( "All cleft types", "Cleft lip", "Cleft palate", "Cleft lip and alveolus", "Cleft lip, alveolus and palate" ) ), selectInput( inputId = "Laterality", label = "Laterality", choices = c("Unilateral or Bilateral", "Unilateral", "Bilateral") ) ), # Show a plot of the generated distribution mainPanel( plotOutput("Plot") %>% withSpinner(color = "darkmagenta"), div(textOutput("Text"), style = "font-size:20px;") ) ) ) ), footer = tags$footer(column(12, p("Klassen, A. F. et al. (2018) ‘Psychometric findings and normative values for the CLEFT-Q based on 2434 children and young adult patients with cleft lip and/or palate from 12 countries’,", em("CMAJ"), ". doi: 10.1503/cmaj.170289.", style = "font-size:13px;" ), br(), p("The CLEFT-Q CAT Score Checker has been developed by Conrad Harrison at the University of Oxford. This represents independent research funded by the NIHR. The views expressed are those of the author and not necessarily those of the University of Oxford, the NHS, the NIHR or the Department of Health and Social Care. The CLEFT-Q CAT Score Checker is provided with absolutely no warranty. The author, the University of Oxford and the NIHR disclaim any and all express and implied warranties including without limitation the implied warranties of title, fitness for a particular purpose, merchantability and noninfringement. The CLEFT-Q Score Checker is licensed under BSD_3_clause + file LICENSE. To obtain a copy of the license please", a(href = "https://github.com/MrConradHarrison/CLEFT-Q-CAT-Score-Checker/blob/main/LICENSE", "click here"), "or contact the author on conrad.harrison@medsci.ox.ac.uk.", style = "font-size:11px;") )) ) )

806178f67e1b432300391f8213e4ad57c47329fa

4f6acb159819268d7108f8e43e4c40e777af9612

/man/dynfrail_dist.Rd

953217f9043f4c7bf033326fc9e9c4612250b1b9

[]

no_license

cran/dynfrail

54d2e3fb9c9e0ae5d8b25191db2c0b8e726c8132

4a89b5deba02a9a6f28797abf7ce2672f465701c

refs/heads/master

2021-07-23T00:57:01.678044

2017-10-30T09:11:49

108,834,132

0

null

UTF-8

R

false

true

1,845

rd

dynfrail_dist.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dynfrail_arguments.R \name{dynfrail_dist} \alias{dynfrail_dist} \title{Distribution parameters for dynfrail} \usage{ dynfrail_dist(dist = "gamma", theta = 2, pvfm = -1/2, lambda = 0.1, n_ints = NULL, times = NULL) } \arguments{ \item{dist}{One of 'gamma', 'stable' or 'pvf'.} \item{theta}{Frailty distribution parameter. Must be >0.} \item{pvfm}{Only relevant if \code{dist = 'pvf'} is used. It determines which PVF distribution should be used. Must be larger than -1 and not equal to 0.} \item{lambda}{Frailty autocorrelation parameter. Must be >0.} \item{n_ints}{For piece-wise constant frailty, the number of intervals. With \code{n_ints = 0}, the classical shared frailty scenario is obtained.} \item{times}{A vector of time points which determine the piecewise-constant interval for the frailty. Overrides \code{n_ints}.} } \value{ An object of the type \code{dynfrail_dist}, which is mostly used to denote the supported frailty distributions in a consistent way. } \description{ Distribution parameters for dynfrail } \details{ The \code{theta} and \code{lambda} arguments must be positive. In the case of gamma or PVF, \code{theta} is the inverse of the frailty variance, i.e. the larger the \code{theta} is, the closer the model is to a Cox model. When \code{dist = "pvf"} and \code{pvfm = -0.5}, the inverse Gaussian distribution is obtained. For the positive stable distribution, the \eqn{\gamma} parameter of the Laplace transform is \eqn{\theta / (1 + \theta)}, with the \eqn{alpha} parameter fixed to 1. } \examples{ dynfrail_dist() # Compound Poisson distribution: dynfrail_dist(dist = 'pvf', theta = 1.5, pvfm = 0.5) # Inverse Gaussian distribution: dynfrail_dist(dist = 'pvf') } \seealso{ \code{\link{dynfrail}, \link{dynfrail_control}} }

6c8bb0da63c86863907e79ea71d50a2af33b5c2d

04cd0fbc561e73b4a8e4912c5253681715faa3ef

/Source_data_files/Preclinical_study/weight_change_day_plotting_stats.R

dee208a10da44d4d47fdb59872f4f48101c8e45e

[]

no_license

raymondkiu/Infant-Clostridium-perfringens-Paper

50a30f717187e65d5c83d403ecc5954ae2df2a07

aeec074baec297019641969ba696d09456554d13

refs/heads/main

2023-06-10T17:03:54.348138

2023-03-21T12:27:26

312,297,407

1

0

null

2023-05-30T14:13:37

2020-11-12T14:14:53

R

UTF-8

R

false

2,533

r

weight_change_day_plotting_stats.R

setwd("~/") library(tidyverse) library(reshape2) # for melt function library(Rmisc) # for summarySE data <- read.csv("weight_change_day_data.csv", header = TRUE, stringsAsFactors = TRUE) head(data) #data1 <- melt(data, id.vars ="Group" , variable.name ="Day") #data1 #write.csv(data1,"weight-change-melted.csv") # Input format # Time Group Value # Day 0 CP 0 # Day 0 CP 0 # Use theme cowplot - minimalist library('cowplot') theme_set(theme_cowplot()) # Prep stats for error bar data <- summarySE(data, measurevar="value", groupvars=c("Group","Day")) data p <- data %>% mutate(Group = fct_relevel(Group, "Control","ABX","pfoA+","pfoA-")) %>% ggplot(aes(x=Day, y=value, color=Group, group=Group)) + geom_line(size=0.6)+ scale_color_manual(values=c("Control"="lightgreen","ABX"="mediumpurple1","pfoA+"="Red2","pfoA-"="Grey"))+ scale_y_continuous(breaks=c(-2,-1,0,1,2,3,4,5), limits=c(-2,6))+ stat_summary(fun=mean, geom="line") + # only scale_color_manual goes with stat_summary geom_errorbar(size=0.6, aes(ymin=value-se, ymax=value+se), width=.5) # Width of the error bar p + theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1)) p + theme(axis.text.x = element_blank(),axis.ticks.x=element_blank()) +facet_grid(~ Group) + ylab("Weight Changes (%)") #theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1)) ## Stats Data <- read.csv("weight_change_day_data.csv", header = TRUE, stringsAsFactors = TRUE) head(Data) shapiro.test(Data$value) # seem to be different from normal distribution # filter subsets Day1 <-filter(Data, Day == "Day 1") Day2 <-filter(Data, Day == "Day 2") Day3 <-filter(Data, Day == "Day 3") Day4 <-filter(Data, Day == "Day 4") Day5 <-filter(Data, Day == "Day 5") Day6 <-filter(Data, Day == "Day 6") # Run statistical analysis library(FSA) Summarize(value~ Group, data = Day1) Summarize(value~ Group, data = Day2) Summarize(value~ Group, data = Day3) Summarize(value~ Group, data = Day4) Summarize(value~ Group, data = Day5) Summarize(value~ Group, data = Day6) kruskal.test(value ~ Group, data=Day1) kruskal.test(value ~ Group, data=Day2) kruskal.test(value ~ Group, data=Day3) kruskal.test(value ~ Group, data=Day4) kruskal.test(value ~ Group, data=Day5) kruskal.test(value ~ Group, data=Day6) # Test which group is statistically different (if overall KW test P<0.05), suitable for unequal number of observations library(rstatix) dunn_test(Day1, value ~ Group, p.adjust.method = "BH") dunn_test(Day2, value ~ Group, p.adjust.method = "BH")

1125d31a5384f12a870407d13572742a6bb75e9c

917c9867f05b73b37c7d68d28fe4a1c57702fd28

/ReadFiles/zooObject.R

b2009c6d1a77ec95c60a4279ba7cb7c82b2ac79f

[]

no_license

xiabofei/rlanguage

e06f58dd25629bd11206369a4189220605bfe550

809435e5da08427f33223dee367d161e20a0424c

refs/heads/master

2020-12-06T17:14:31.562219

2016-09-27T06:20:29

66,824,872

0

null

UTF-8

R

false

576

r

zooObject.R

# 1. zoo这个包当时专门为了处理daily的finacial data # 2. 有处理时间序列数据相关的需求可以再具体去查zoo包的用法 # http://ftp.auckland.ac.nz/software/CRAN/doc/vignettes/zoo/zoo-quickref.pdf A <- data.frame(date=c("1995-01-01", "1995-01-02", "1995-01-03", "1995-01-06"), x=runif(4), y=runif(4)) A$date <- as.Date(A$date) # 构建一个zoo的object matrix library(zoo) B <- A B$date <- NULL z <- zoo(as.matrix(B), order.by = A$date) rm(A, B) C <- coredata(z) rownames(C) <- as.character(time(z)) str(C) str(z)

54f70fac02bf992ecc8385d64dd663dfa9165de9

2fa58d99bebdb471fc7b81fab879b9c93560e8f7

/predicting_prices/ui.R

8b4bdb973d13a84f5c1530e9afb838b8071f0077

[]

no_license

albert2828/coursera_ddp_final_project

86fe5df78b5575c7a9480b5c2873ff9fc35b0bba

ef300f5f1868273fc06d7989a42c6a8ba9509317

refs/heads/main

2023-04-21T21:00:11.693000

2021-05-19T21:26:06

368,397,096

0

null

UTF-8

R

false

2,283

r

ui.R

library(shiny) # Define UI for application that draws a histogram shinyUI(fluidPage( # Application title titlePanel("Compute the price of your car"), sidebarLayout( sidebarPanel( sliderInput("age", "Age of your car:", min = 1, max = 50, value = 5), selectInput(inputId = "Fuel_Type", label = "Select the type of fuel your cars uses", choices = c("Petrol", "Diesel"), selected = "Petrol"), sliderInput("Kms_Driven", "Kilometers Driven", min = 1, max = 500000, value = 5000), selectInput("Transmission", "Select the type of Transmission of your car", choices = c("Automatic", "Manual"), selected = "Automatic"), selectInput("Seller_type", "Do you want to sell it yourseld or by a third party?", choices = c("Dealer", "Individual"), selected =" Dealer"), selectInput("Owner", "Number of previous owners", choices = 0:5, selected = 0), submitButton("Submit") ), mainPanel( h3("Aproximated price of your car"), textOutput("pred"), br(), p("This app is made so you can aproximate the price of your car if you want to sell it"), p("You only need to fill a few gaps"), tags$ul( tags$li("Age: age of your car"), tags$li("Fuel Type: Petrol or Diesel (for the moment we are not prepared for electric cars)"), tags$li("Killometers Driven: if your cars runs in miles, multiply miles by 1.60934"), tags$li("Transmission: Automatic or Manual"), tags$li("Do you plan to sell it yourself of to go to an agency?"), tags$li("Number of owners: How may persons have had the car before you?") ), p("Finally, just click the Submit buttom!"), ) ) ) )

34d4362ea8162d273afd4dd9202974b768a8e09f

b8a0bf8c783e5454a6c5bbb76385c6e852bbe577

/quant v1.3/Visualize.R

65b84e7d045dcb3f028d419e4b76fae4792fd456

[]

no_license

highandhigh/Quantitative-Finance-Project

20ee30f1a79f19989d54ffc70046128946d7646b

9bc5179b036a624164865ffa8291ea58febc6895

refs/heads/master

2021-01-13T16:33:40.910622

2016-06-24T16:11:57

null

0

null

UTF-8

R

false

1,723

r

Visualize.R

# 量化金融项目 # By: JiaRu, jiaru2014@126.com # R version 3.2.3 # Platform: x86_64-apple-darwin13.4.0 (64-bit) # Date: 2016-06-22 # # Version 1.3 # =================================================================== # 可视化 # # 输入 tradebook(with attr: out-sample period) dt # 输出 ggplot object # layers: # 1. EndPrc # 2. Buy / Sell signal # 3. Profit / Stop # 4. Volumn Visualize <- function(tradebook, dt) { # merge date_range <- attr(tradebook, "out_sample_range") # date range dt_out <- dt[TDate >= date_range[1] & TDate <= date_range[2]] trade_plot <- melt( tradebook, id.vars = c("date_time"), measure.vars = c("Buy", "Sell", "Profit", "Stop") ) trade_plot <- trade_plot[!is.na(value)] # 从 dt_out 中添加 EndPrc trade_plot_2 <- merge( x = trade_plot, y = dt_out[, .(DateTime ,EndPrc)], by.x = "date_time", by.y = "DateTime", all.x = TRUE, all.y = TRUE ) # 因为x是datetime，存在大量非交易时间， # 需要把x做一个转换，不然画出来图很难看 trade_plot_2[, timeindex := seq_along(date_time)] # 绘图参数 b <- 20 # 20个break m <- round(nrow(trade_plot_2) / b) # 开始画图 g1 <- ggplot(data = trade_plot_2, aes(x = timeindex, y = EndPrc)) + geom_line() + geom_point(data = trade_plot_2[!is.na(variable)], aes(colour = variable), alpha = 0.5) + scale_x_continuous( breaks = trade_plot_2[timeindex %% m == 0, timeindex], labels = trade_plot_2[timeindex %% m == 0, format(date_time, format = "%m/%d")] ) + ggtitle("Trade") + theme_bw() return(g1) } # # 单元测试 # Visalize(tradebook, dt)

15f6c1b6b16a98911a726ff1fcc724e3419917e1

34b1404a1ff8c8abe0be16f9d0bc29f633043e35

/tests/setCellLazy.R

3741d18fc87f489c68d7ae3e3202bd70f1e7ca93

[]

no_license

duncantl/RExcelXML

729183fdc549951d986dd730ee9b16846a3a3d86

bb278b9e15ae44bf538b86d04f0490c128db4a31

refs/heads/master

2020-06-07T07:09:44.596626

2012-01-18T23:22:58

3,922,555

5

1

null

UTF-8

R

false

448

r

setCellLazy.R

library(RExcelXML) fn = system.file("templateDocs", "Sample3sheets.xlsx", package="RExcelXML") w = excelDoc("bbb.xlsx", create = TRUE, template = fn) wb = workbook(w) sh = wb[["Sheet1"]] c1 = cells(sh) styles = getDocStyles(w) ft = Font(sz = 10L, face=c("b","i")) newSt = createStyle(sh, format = 14, font = ft, halign="center", update = FALSE, fg = "FF0000", styleDoc = styles) setCellStyle(c1[[3]], newSt, update = FALSE) update(sh, styles)

633c2e944f1b5016cf22016e68270f5bf87a3f28

44e281a60264d4a48b40f6fdf1005395a6103dc8

/R/dnegocc.R

ec94b95dcf8b980710d9740f8cef23bd19fefeee

[]

no_license

cran/occupancy

7f0bd71c4f30ecf4b1cc73864177de5e2ceeb67e

4421086015ad2103131cd18e514706913d49ea8f

refs/heads/master

2023-06-05T13:28:09.280196

2021-06-24T10:00:02

379,975,604

0

null

UTF-8

R

false

4,659

r

dnegocc.R

#' @rdname dnegocc dnegocc <- function(x, space, occupancy, prob = 1, approx = FALSE, log = FALSE) { #Check that argument and parameters are appropriate type if (!is.numeric(x)) stop('Error: Argument x is not numeric') if (!is.numeric(space)) stop('Error: Space parameter is not numeric') if (!is.numeric(occupancy)) stop('Error: Occupancy parameter is not numeric') if (!is.numeric(prob)) stop('Error: Probability parameter is not numeric') if (!is.logical(approx)) stop('Error: approx option is not a logical value') if (!is.logical(log)) stop('Error: log option is not a logical value') #Check that parameters are atomic if (length(space) != 1) stop('Error: Space parameter should be a single number') if (length(occupancy) != 1) stop('Error: Occupancy parameter should be a single number') if (length(prob) != 1) stop('Error: Probability parameter should be a single number') if (length(approx) != 1) stop('Error: approx option should be a single logical value') if (length(log) != 1) stop('Error: log option should be a single logical value') #Set parameters if (space == Inf) { m <- Inf } else { m <- as.integer(space) } k <- as.integer(occupancy) #Check that parameters are in allowable range if (space != m) stop('Error: Space parameter is not an integer') if (m <= 0) stop('Error: Space parameter must be positive') if (occupancy != k) stop('Error: Occupancy parameter is not an integer') if (k < 0) stop('Error: Occupancy parameter must be non-negative') if (k > m) stop('Error: Occupancy parameter is larger than space parameter') if (prob < 0) stop('Error: Probability parameter must be between zero and one') if (prob > 1) stop('Error: Probability parameter must be between zero and one') #Create output vector max.x <- floor(max(x)) NEGOCC <- rep(-Inf, length(x)) #Compute for trivial case where k = 0 if (k == 0) { for (i in 1:length(x)) { xx <- x[i] if (xx == 0) { NEGOCC[i] <- 0 } } if (log) { return(NEGOCC) } else { return(exp(NEGOCC)) } } #Compute for trivial case where prob = 0 if (prob == 0) { if (k > 0) { for (i in 1:length(x)) { xx <- x[i] if (xx == Inf) { NEGOCC[i] <- 0 } } if (log) { return(NEGOCC) } else { return(exp(NEGOCC)) } } } #Compute for special case where m = Inf if (m == Inf) { NEGOCC <- dnbinom(x, size = k, prob = prob, log = TRUE) if (log) { return(NEGOCC) } else { return(exp(NEGOCC)) } } #Compute for non-trivial cases where k > 0 and prob > 0 #Compute log-probablities using recursion if (!approx) { #Create base vector for recursion if(prob == 1) { LOGS <- c(0, rep(-Inf, max.x)) } else { LOGS <- log(prob) + (0:max.x)*log(1-prob) } #Update via recursion r <- 2 while (r <= k) { NEWLOGS <- rep(-Inf, max.x+1) LLL <- (0:max.x)*log(1-prob*(m-r+1)/m) for (t in 0:max.x) { TERMS <- LLL[1:(t+1)] + LOGS[(t+1):1] NEWLOGS[t+1] <- log(prob*(m-r+1)/m) + matrixStats::logSumExp(TERMS) } LOGS <- NEWLOGS r <- r+1 } #Generate output vector for (i in 1:length(x)) { xx <- x[i] if ((as.integer(xx) == xx)&(xx >= 0)) { NEGOCC[i] <- LOGS[xx+1] } } } #Compute log-probabilities using approximation if (approx) { #Compute generalised harmonic numbers H1 <- sum(1/((m-k+1):m)) H2 <- sum(1/((m-k+1):m)^2) #Compute moments MEAN <- max(0,(m/prob)*H1 - k) VAR <- max(0,(m/prob)^2*H2 - (m/prob)*H1) #Approximation using discretised gamma distribution if (VAR == 0) { APPROX <- c(0, rep(-Inf, max.x)) } if (VAR > 0) { SHAPE <- (MEAN + 1/2)^2/VAR RATE <- m*(MEAN + 1/2)/VAR LGA <- pgamma((0:(max.x+1))/m, shape = SHAPE, rate = RATE, log.p = TRUE) LOWER <- LGA[1:(max.x+1)] UPPER <- LGA[2:(max.x+2)] APPROX <- UPPER + VGAM::log1mexp(UPPER-LOWER) } #Generate output vector for (i in 1:length(x)) { xx <- x[i] if ((as.integer(xx) == xx)&(xx >= 0)) { NEGOCC[i] <- APPROX[xx+1] } } } #Return output if (log) { NEGOCC } else { exp(NEGOCC) } }

d47b9f92b924389badcbbff74fd4c23630d5e31b

838eef61b899bafd172b48d8689bdc0a34d2c7cd

/photographer_classifier/pkgs/PhotographerModels/man/load.model.Rd

777689f4bd4300240c239b9d5bf67da10d86586e

[]

no_license

rajatmnnit/csx415.1-project

8bc669768f43d4a87248fa006d36ba2042a31182

16a1b3e2e6a70401d841d53b40fd071594c216c2

refs/heads/master

2020-03-08T13:26:55.512439

2018-08-07T22:43:29

null

0

null

UTF-8

R

false

true

622

rd

load.model.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model.R \name{load.model} \alias{load.model} \title{Load the classification model.} \usage{ load.model(mtype = "gbm") } \arguments{ \item{mtype}{Type of the model to be returned. Valid value [svm | rf | gbm]. Defaults to the highest accuracy gbm model.} } \value{ a caret::train binary classification model object trained using specified model types. } \description{ Loads a pre-trained persistent binary classification model to classify customers as "PHOTOGRAPHERS" or "OTHERS". } \examples{ load.model() load.model(mtype="rf") }

f477f3b3fe460ca121dc0ac3ed9ba48fc4e24dd3

84dd0562ebd14dab4913a27313c3e82b792e3d76

/R/cor_RNA.R

a96354688cfd34de45265f08d47915ed5cd017d3

[ "Apache-2.0" ]

permissive

yikeshu0611/TCGAimmunelncRNA

daaca3623943b63b83ad98ef357c179ad6048c2f

9c86b20caa6674a976aca6ea3111755fd34bb5b5

refs/heads/main

2023-06-11T09:01:11.701450

2021-07-03T07:10:31

381,676,631

1

0

null

UTF-8

R

false

1,638

r

cor_RNA.R

#' correlation of mRNA and lncRNA #' #' @param data data of FPKM2df() #' @importFrom foreach %dopar% #' @export #' cor_RNA <- function(data){ mRNA <- data$mRNA[data$group$bar_code[data$group=='Tumor'],] message('\nuse ',nrow(mRNA), ' tumors for correlation analysis') message('mRNA:',ncol(mRNA)) lncRNA <- data$lncRNA[data$group$bar_code[data$group=='Tumor'],] message('lncRNA:',ncol(lncRNA)) cores <- parallel::detectCores() cl <- snow::makeSOCKcluster(cores) doSNOW::registerDoSNOW(cl) pb <- utils::txtProgressBar(max=ncol(mRNA), width = 30, style=3) progress <- function(n) utils::setTxtProgressBar(pb, n) opts <- list(progress=progress) result <- foreach::foreach(i=1:ncol(mRNA), .packages="Kendall", .options.snow=opts) %dopar% { x <- as.data.frame(t(sapply(1:ncol(lncRNA), function(j) as.data.frame(t(do.call('c',cor.test(mRNA[,i],lncRNA[,j]))))[,c(1,4,3)]))) colnames(x) <- c('t','cor','pvalue') x <- do::numeric.it(x,c('t','cor','pvalue')) x <- round(x,3) x$mRNA <- colnames(mRNA)[i] x$lncRNA <- colnames(lncRNA) x } close(pb) snow::stopCluster(cl) co <- do.call(rbind,result) co$regulate <- ifelse(co$cor>0,'positive','negative') cor <- co[,c("mRNA", "lncRNA", "t", "cor", "pvalue",'regulate')] list(cor=cor, bar_code=rownames(mRNA), mRNA=colnames(mRNA), lncRNA=colnames(lncRNA)) }

982a87f73bb5964c6b6fda2d900dd1f422fee7e0

9994253dee538a79e4c7a9d9cdcde5dccb9fe2f7

/man/readMyData.Rd

d3104869b5653328776f3e9e6713e631142654a5

[]

no_license

lehmansociology/lehmansociology

aa8d80e4a1bbb0e85c63aa25a318ef24daf69483

9ff6232f8f26ee0a5921e7f013bca7994bc2f2e1

refs/heads/master

2022-06-14T06:01:19.829363

2022-05-15T05:30:56

103,927,010

1

2

null

2019-12-31T14:45:44

2017-09-18T10:48:28

R

UTF-8

R

false

true

273

rd

readMyData.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/readmydata.R \name{readMyData} \alias{readMyData} \title{Read My Data} \usage{ readMyData(datasetname) } \description{ This function reads a webchip style dataset } \keyword{Freq} \keyword{data}

7c9b5f729d3456af979b3bfd3b1d0c9692e7275e

c5d173f7755dc27e348ef616ebafba137ee7e1da

/man/glue_fmt_chr.Rd

480fc4cbdfe920f30cf22137530c3822b41c1b35

[ "MIT" ]

permissive

Ilia-Kosenkov/RLibs

85679202753b7565d8995350e74b28a8c3d624d9

60e34778a96f5ba9705b5fa5da0bffcc3be482fd

refs/heads/master

2021-03-27T16:13:53.106985

2020-01-23T15:33:33

96,409,913

0

null

UTF-8

R

false

true

372

rd

glue_fmt_chr.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glue_fmt.R \name{glue_fmt_chr} \alias{glue_fmt_chr} \title{glue_fmt_chr} \usage{ glue_fmt_chr(..., .envir = parent.frame()) } \arguments{ \item{...}{Passed to \code{glue::glue}.} \item{.envir}{Evaluation environment.} } \value{ Format-aware interpoalted string. } \description{ glue_fmt_chr }

bc075509eb99a880e2cac4fd6162e7ee5ad2e668

269d71aab45fac39e11c44d40aaa264dc27026e6

/WHO_Life_Expectancy.R

59884cbd72a066afb848e823e9af088cfc96e9a4

[]

no_license

arun98-mohan/ML_Academic_Assignments

41013aa64616203f0bf49f8c4eef35c959e7282b

d3e2f9a1f5b0703c25c0c99bae1d40dc1fad27cb

refs/heads/master

2020-04-18T01:51:12.649239

2019-03-04T23:40:24

167,137,122

0

null

UTF-8

R

false

610

r

WHO_Life_Expectancy.R

life<-read.csv("/Users/Arun/Desktop/Datasets/life.csv") head(life) life2<-life[,c(4,5,6,7,8,9,10,11,12,13,14,15,16,17)] head(life2) life2<-na.omit(life2) cor(life2) plot(percentage.expenditure~GDP,data=life2) set.seed(110) training_row_index<-sample(1:nrow(life2),0.75*nrow(life2)) training_data<-life2[training_row_index,] test_data<-life2[-training_row_index,] linmod=lm(percentage.expenditure~GDP,data=life2) linmod summary(linmod) abline(linmod,col="green") p<-predict(linmod,test_data) summary(p) acc_pred<-data.frame(cbind(acc=test_data$percentage.expenditure,pred=p)) head(acc_pred) cor(acc_pred)

9c046dd052a91ea881a9d2d0bfba85f1fa7e705b

726573b44fd67576df1df8e916b562b2b84e5091

/man/clean_spocc.Rd

ad464b12cce51e6fc6e1e0e43df856c634411ed0

[ "MIT" ]

permissive

Libardo1/spocc

0b17b9a4c4cfe467f53b55093f70e535ccafe657

bb9379125053e902d6006198db856687bd4b0149

refs/heads/master

2021-01-17T14:37:28.115221

2014-07-01T17:14:12

null

0

null

UTF-8

R

false

581

rd

clean_spocc.Rd

% Generated by roxygen2 (4.0.1): do not edit by hand \name{clean_spocc} \alias{clean_spocc} \title{Clean spocc data} \usage{ clean_spocc(input, country = NULL, habitat = NULL) } \arguments{ \item{input}{An object of class occdat} \item{country}{(logical) Attempt to clean based on country} \item{habitat}{(logical) Attempt to clean based on habitat} } \description{ Clean spocc data } \examples{ \dontrun{ res <- occ(query = c('Ursus','Accipiter','Rubus'), from = 'bison', limit=120) res_cleaned <- clean_spocc(res) class(res_cleaned) # now with classes occdat and occclean } }

0033b0e7f1910eb08e8c8ec367edfc5fff40ef8c

ca0537a5f6bec94d7d472e5b0fa022b465f3853a

/plot5.R

7bab16ee9c77fbe076fc9ec43eeacc9d8dd91a42

[]

no_license

tjeerdluykx/ExData_CP2

8ef958f44d92ab3f939ca0e5a8d18148803769b3

fc0bafea06df3092b6cd83dbb6edb415f1cbca07

refs/heads/master

2016-09-06T12:39:33.086960

2015-07-23T06:33:15

39,489,612

0

null

UTF-8

R

false

1,512

r

plot5.R

## Title: plot5.R ## Author: Tjeerd Luykx ## Date: July 22nd 2015 ## Description: create plot question 5. library(dplyr) library(ggplot2) FileName <- "exdata-data-NEI_data.zip" # Downloading and unzipping dataset: if (!file.exists(FileName)){ FileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" download.file(FileURL, FileName, method="curl") } if (!file.exists("exdata-data-NEI_data")) { unzip(FileName) } # Load data sets from home directory: if(!exists("NEI")){ NEI <- readRDS("./summarySCC_PM25.rds") } if(!exists("SCC")){ SCC <- readRDS("./Source_Classification_Code.rds") } # Question 5: how have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City? # Merge the two data sets NEISCC <- full_join(NEI, SCC, by="SCC") # Filter large dataset NEISCC.f <- filter(NEISCC, fips == "24510" & type == "ON-ROAD") # Aggregate emissions BalEmMotor <- aggregate(Emissions ~ year, NEISCC.f, FUN = sum) colnames(BalEmMotor) <- c("Year","Emissions") # Create barplot png("plot5.png") gPlot <- ggplot(BalEmMotor, aes(factor(Year), Emissions)) gPlot <- gPlot + geom_bar(stat="identity",fill="black", colour="blue") + xlab("Year") + ylab(expression('Total PM'[2.5]*" Emissions")) + ggtitle("Baltimore Total PM'[2.5]*'Emissions from motor vehicles: 1999 to 2008") print(gPlot) dev.off() # Answer: emissions from motor vehicles have significantly decreased in Baltimore City from 1999 to 2008.

ad2099c3e56e5fd7076c9bd01b109c803f7314b5

72d9009d19e92b721d5cc0e8f8045e1145921130

/SuperGauss/R/Cholesky.R

aa9192621a72c4284fa075e0afdeb5185234b409

[]

no_license

akhikolla/TestedPackages-NoIssues

be46c49c0836b3f0cf60e247087089868adf7a62

eb8d498cc132def615c090941bc172e17fdce267

refs/heads/master

2023-03-01T09:10:17.227119

2021-01-25T19:44:44

332,027,727

1

0

null

UTF-8

R

false

1,135

r

Cholesky.R

#' Cholesky multiplication with Toeplitz variance matrices. #' #' Multiplies the Cholesky decomposition of the Toeplitz matrix with another matrix, or solves a system of equations with the Cholesky factor. #' #' @name Cholesky #' @aliases cholXZ cholZX #' #' @param X Length-`N` or `N x p` matrix of observations. #' @param Z Length-`N` or `N x p` matrix of residuals. #' @param acf Length-`N` autocorrelation vector of the Toeplitz variance matrix. #' #' @return Size `N x p` residual or observation matrix. #' #' @details If `C == t(chol(toeplitz(acf)))`, then `cholZX()` computes `C %*% Z` and `cholZX()` computes `solve(C, X)`. Both functions use the Durbin-Levinson algorithm. #' @example examples/Cholesky.R NULL #' @rdname Cholesky #' @export cholZX <- function(Z, acf) { n <- length(acf) Z <- as.matrix(Z) if(nrow(Z) != n) stop("Z and acf have incompatible dimensions.") DurbinLevinson_ZX(Z = Z, acf = acf) } #' @rdname Cholesky #' @export cholXZ <- function(X, acf) { n <- length(acf) X <- as.matrix(X) if(nrow(X) != n) stop("X and acf have incompatible dimensions.") DurbinLevinson_XZ(X = X, acf = acf) }

75b256dc6b3c7a57f522fa1e25440f51dfe8915d

2f22514a6c4b4e769d08027c9db4b8fdccac3fd7

/Rbonaut2/man/itemID2Params.Rd

1256d34c734a8388d6cf8878c5c48fdb99256985

[]

no_license

cavorit/Rbonaut2

4489bff4ac9fb4c228d9315de4c80e9fe9e0fd3d

b0ecbd74411cc2c1306b06e059c130f45a0749b5

refs/heads/master

2021-01-24T14:45:43.549973

2017-02-22T14:51:03

47,259,241

0

null

UTF-8

R

false

true

641

rd

itemID2Params.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/itemID2Params.R \name{itemID2Params} \alias{itemID2Params} \title{itemID2Params} \usage{ itemID2Params(ItemID) } \arguments{ \item{ItemID}{charakter der Länge 1, welches den Namen des Items angibt. Implementiert sind BL01:BL32} } \value{ list } \description{ Gibt Informationen an simFBN() zurück, wie der nächste Ball gespielt werden soll } \details{ Für eine genauere Beschreibung verweise ich auf das Markdown-Manual für BL32MultiTargetSimTest.md. Diese Funktion erstellt den Knoten "nextB" } \examples{ itemID2Params("BL03") } \author{ Harald Fiedler }

fa5ddcb90227b6385d405b81f50c1df650e39c97

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/crimelinkage/examples/compareCrimes.Rd.R

46f0abd64a3cde4bd259ba76f31c2bf51efd86c9

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

466

r

compareCrimes.Rd.R

library(crimelinkage) ### Name: compareCrimes ### Title: Creates evidence variables by calculating 'distance' between ### crime pairs ### Aliases: compareCrimes ### ** Examples data(crimes) pairs = t(combn(crimes$crimeID[1:4],m=2)) # make some crime pairs varlist = list( spatial = c("X", "Y"), temporal = c("DT.FROM","DT.TO"), categorical = c("MO1", "MO2", "MO3")) # crime variables list compareCrimes(pairs,crimes,varlist,binary=TRUE)

67b79784f6a5c3a584691b979b73499bda7559f4

791faa0afc9c0e613df80709016f4cb094473e5f

/MFA/man/LG_table.Rd

5e3fca9ff1e031dc01ddf73ceec03e78bffa51cb

[]

no_license

LlamaL/ShinyApp-RStudio

84f79b30c4b5a57507f52776acc5d28f0a8ce7fa

7e7ab1490c86cf89c50e9a3b2364322579a49bd3

refs/heads/master

2021-05-03T09:59:47.998108

2018-02-06T22:23:02

120,529,260

0

null

UTF-8

R

false

true

578

rd

LG_table.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/LG_table.r \name{LG_table} \alias{LG_table} \title{Lg Table} \usage{ LG_table(dataset, sets) } \arguments{ \item{dataset}{a normalized dataframe or matrix} \item{sets}{list of vector contains vector of indices of each group} } \value{ a table of Lg coefficients } \description{ Return a table of Lg coefficients between any two subsets of a normalized dataset } \examples{ # default nadtas <- scale(wine_data) LG_table(ndatas,sets=list(1:6,7:12,13:18,19:23,24:29,30:34,35:38,39:44,45:49,50:53)) }

4aea51ba5512e034ceed75d37bf94ec03a660406

1cbaa63faef96016849ec72cffb7a6f1428a30c9

/man/MakeFIFOs.Rd

86fae4cb0346572e04dd51884e363ac6493076d0

[]

no_license

cran/MultiJoin

efe3c4106ab9f3ebcf52a3ddfbebc2a6756b9816

81d3779fab11e7af59f0505b4d287390d4099683

refs/heads/master

2021-01-25T10:00:01.703163

2018-11-15T21:10:07

35,290,494

0

null

UTF-8

R

false

1,144

rd

MakeFIFOs.Rd

\name{MakeFIFOs} \alias{MakeFIFOs} \title{creates named Unix pipes, which gzipped files can be streamed to for e.g. further joins} \description{Additional filters can be implemented based upon the input arguments. This string is typically used in between pipes. } \usage{MakeFIFOs(file = "file1.txt.gz", FIFO = "/tmp/fifo1", path = ".", filterStr = " | cut -f2,3 -d\\" \\" --complement", mycat = "gunzip -cf ", verbose = 2)} \arguments{ \item{file}{Name of the file that contains the data to uncompress and filter on} \item{FIFO}{Name of the FIFO to create} \item{path}{Directory to find the files in} \item{filterStr}{various inline filters that act locally and do not need an input file,} \item{mycat}{effective cat command} \item{verbose}{level of verbosity} } \value{filter string} \author{"Markus Loecher, Berlin School of Economics and Law (BSEL)" <markus.loecher@gmail.com>} \examples{ if (0){ MakeFIFOs(verbose=2) MakeFIFOs(filterStr=" | awk '$2 > 100 && $3 > 5' | cut -f2,3 -d\" \" --complement | head -n 10000 | sort -k1,1") } }

5e51e171e6fd584f068d215d0b6d34c27543e73c

706ffaca97a7b980b960010aa96aaea81fd274a1

/02_rprog/complete.R

27e8a3e53bbdae26598d3a8dda6b8467fdd3df11

[]

no_license

szebenyib/datasciencecoursera

ea40f3550307a512a5cbd545963c822ee5017a97

b1adfaea1198875dabb619009c7e02f72499e2d3

refs/heads/master

2021-01-19T18:30:06.560198

2015-12-26T21:06:30

31,818,358

0

null

UTF-8

R

false

935

r

complete.R

complete <- function(directory, id = 1:332) { ## 'directory' is a character vector of length 1 indicating ## the location of the CSV files ## 'id' is an integer vector indicating the monitor ID numbers ## to be used ## Return a data frame of the form: ## id nobs ## 1 117 ## 2 1041 ## ... ## where 'id' is the monitor ID number and 'nobs' is the ## number of complete cases #files_df <- list.files(directory) df = data.frame(id = id, nobs = 0) line_counter <- 1 for (i in id) { full_name <- paste(sprintf("%03d", i), ".csv", sep="") full_url <- paste(directory, "/", full_name, sep="") obs_df <- read.csv(full_url) df[line_counter, 2] <- sum(complete.cases(obs_df)) line_counter = line_counter + 1 } return(df) }

e95aea46e74ce7b3c73c3b14f2eab2647ac4c877

438352c9be21915f9aa2bda4b1743e45904a5118

/man/compute_budyko2.Rd

cdea4a0915863c1efa87ff48a25356ca99d90048

[]

no_license

Lihao-CAU/ecohydroexamples

1658fce766a2d54fc70faa96caf8b9435d8e9e9a

768dd71af5b8cccb93bb20270af7a40ad746128b

refs/heads/master

2020-06-15T20:24:06.892433

2017-11-10T03:26:01

195,384,528

1

0

null

2019-07-05T09:48:09

null

UTF-8

R

false

true

508

rd

compute_budyko2.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compute_budyko2.R \name{compute_budyko2} \alias{compute_budyko2} \title{compute_budyko2} \usage{ compute_budyko2(P, PET, w = 2.63) } \arguments{ \item{P}{precipitation} \item{PET}{potential evapotranspiration} \item{w}{adjustment parameter (default 2.63)} } \value{ Evaporative index (ratio), Q (streamflow), AET (actual evapotranspration) } \description{ THis evaporative index (ratio of AET/P) using Budyko } \author{ Naomi }

56e99c455865fc54953782af3b31ba6089d1c278

7a24a59293bafdd638d2fb9cdc10aa00359f5797

/man/read.fitskey.Rd

2141a8d277143a0f094ce740146bbc25bdcfca01

[]

no_license

cran/astro

c0c92d6ff150a68ce81cc7f75e1888ee6498c36f

19710691a4a1a7d18aab7ccd31a7be31e0fe2b2a

refs/heads/master

2021-01-13T01:30:32.071603

2014-09-08T00:00:00

17,694,512

1

null

UTF-8

R

false

975

rd

read.fitskey.Rd

\name{read.fitskey} \alias{read.fitskey} \title{Read FITS Header Keyword} \description{The mid-level function 'read.fitskey' allows FITS header keywords to be read directly into R.} \usage{read.fitskey(key, file, hdu = 1, comments = FALSE, strip = c(" ","'"," "), maxlines = 50000)} \arguments{ \item{key}{header keyword (may be a vector)} \item{file}{file name} \item{hdu}{header and data unit to be read} \item{comments}{output header comments?} \item{strip}{lead/trail characters stripped from header 'value' data} \item{maxlines}{maximum number of header lines} } \details{ The mid-level function 'read.fitskey' is a wrapper around 'read.fits', and provides a more simplistic output of that routine. } \value{ A vector of data equal in length to the input key request. NA is returned where no keys have been found. } \author{ Lee Kelvin <lee.kelvin@uibk.ac.at> } \seealso{ The astronomy package: \code{\link{astro}}. } \keyword{data}

b67a9c5e1df771b9bfae0f281582157b2573d308

2800fef4eae5c523fb424ef3c672c5f3e9abe537

/data-raw/procdat.R

de8b5a073d38fdf4543368db9d2fa7183f6628c6

[]

no_license

bbuchsbaum/pcfacespace

78097fc869ffa6961a7cd867edafd8d88c5bfe8c

607c4e20ba05d99f937673703c3bb58aa84fd9a8

refs/heads/master

2023-03-08T07:58:20.171989

2021-02-18T18:22:44

302,968,567

0

null

UTF-8

R

false

915

r

procdat.R

library(rmatio) pfd_all_faces <- rmatio::read.mat("data-raw/pfd_all_faces.mat")[[1]] #usethis::use_data(pfd_all_faces, overwrite=TRUE) pfd_avg_face <- rmatio::read.mat("data-raw/pfd_avg_face.mat")[[1]] #usethis::use_data(pfd_avg_face, overwrite=TRUE) pfd_pc <- rmatio::read.mat("data-raw/pfd_pc.mat")[[1]] #usethis::use_data(pfd_pc, overwrite=TRUE) pfd_score <- rmatio::read.mat("data-raw/pfd_score.mat")[[1]] #usethis::use_data(pfd_score, overwrite=TRUE) pfd_std_score <- rmatio::read.mat("data-raw/pfd_std_score.mat")[[1]] #usethis::use_data(pfd_std_score, overwrite=TRUE) pfd_demo <- as.data.frame(rmatio::read.mat("data-raw/pfd_demographics.mat")[[1]]) names(pfd_demo) <- c("id", "pop", "sex", "age") pfd_data <- list( all_faces=pfd_all_faces, avg_face=pfd_avg_face, pc=pfd_pc, scores=pfd_score, std_scores=pfd_std_score, demographics=pfd_demo ) usethis::use_data(pfd_data, overwrite=TRUE)

eb4a2001f5d0691bceaeed8b52edea8204f9f467

eb38363e113cd1331b929276155f4dd409cd5de4

/man/one_URL_geoJSON.Rd

057d8fe13796ae5013e3d7b80a15467beaca3e66

[]

no_license

ElliottMess/pcodesOCHA

581d261de3c0cbe790055a9a8a7e5972a05a3357

3a991dc02e3694ff012694616521120d9f1a3452

refs/heads/master

2023-05-09T03:57:25.028768

2021-05-12T12:00:23

328,928,392

2

0

null

UTF-8

R

false

true

837

rd

one_URL_geoJSON.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pcodes_scrapping.R \name{one_URL_geoJSON} \alias{one_URL_geoJSON} \title{Scrap geoJSON from one URL on \href{https://gistmaps.itos.uga.edu/arcgis/rest/services/COD_External}{OCHA COD REST API}} \usage{ one_URL_geoJSON(layer_URL) } \arguments{ \item{layer_URL}{character - A valid FeatureServer layer directory URL. Example: "https://gistmaps.itos.uga.edu/arcgis/rest/services/COD_External/ZMB_pcode/FeatureServer/2"} } \value{ geoJSON - a geoJSON object containing the layer's features } \description{ Scrap geoJSON from one URL on \href{https://gistmaps.itos.uga.edu/arcgis/rest/services/COD_External}{OCHA COD REST API} } \examples{ \dontrun{ one_URL_geoJSON("https://gistmaps.itos.uga.edu/arcgis/rest/services/COD_External/ZMB_pcode/FeatureServer/2") } }

c61bb4f5aeaf314f3b53d47b93c7dd7e401b8231

f48eca83e984133c32ef38560e7015ed9ae1f4de

/r5_weather1.R

c164d2376cd636a3dbb51767b6744c8734c01b87

[]

no_license

DataScienceGB/test

d39c3e7859ae384fd48926c1a75cf4fec515088b

cf429494320727efa6ffc0e32c42326e2a23a6e8

refs/heads/master

2020-04-06T04:52:36.236085

2015-08-23T04:34:02

30,780,962

0

null

UTF-8

R

false

4,562

r

r5_weather1.R

library(sqldf) library(xtable) library(ggplot2) #Check for file existance # if exists then load it else #if not exists bring it from source and inflate it and load it #overwrite inflated file if exists csv_storm_file="./data/repdata_data_StormData.csv" bz2_storm_file="./data/repdata_data_StormData.csv.bz2" if (file.exists(bz2_storm_file) ) { stormdf=read.csv(bz2_storm_file,stringsAsFactors=F) } else { if (!file.exists(bz2_storm_file)) { fileUrl<-"https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2" download.file(fileUrl,dest=bz2_storm_file,method="curl") stormdf=read.csv(bz2_storm_file,stringsAsFactors=F) } } #Convert PROPDMG and CROPDMG to same "scale"(usd) stormdf[!(stormdf$PROPDMGEXP %in% c("b","B","k","K","m","M")),"PROPDMGEXP"] = "1" stormdf[stormdf$PROPDMGEXP %in% c("k","K"),"PROPDMGEXP"] = "1000" stormdf[stormdf$PROPDMGEXP %in% c("m","M"),"PROPDMGEXP"] = "1000000" stormdf[stormdf$PROPDMGEXP %in% c("b","B"),"PROPDMGEXP"] = "1000000000" stormdf[!(stormdf$CROPDMGEXP %in% c("b","B","k","K","m","M")),"CROPDMGEXP"] = "1" stormdf[stormdf$CROPDMGEXP %in% c("k","K"),"CROPDMGEXP"] = "1000" stormdf[stormdf$CROPDMGEXP %in% c("m","M"),"CROPDMGEXP"] = "1000000" stormdf[stormdf$CROPDMGEXP %in% c("b","B"),"CROPDMGEXP"] = "1000000000" #Convert Strings to Numeric values stormdf$PROPDMGEXP=as.numeric(stormdf$PROPDMGEXP) stormdf$CROPDMGEXP=as.numeric(stormdf$CROPDMGEXP) #summarize health_impact by event type health_grp=sqldf("select EVTYPE,count(*) as number_of_events, sum(FATALITIES+INJURIES) as total_impact, avg(FATALITIES+INJURIES) as impact_by_event from stormdf where (FATALITIES+INJURIES)!='NA' group by EVTYPE order by 3 desc limit 10") sqldf("select sum(total_impact) as Total_Impact, case EVTYPE when 'TORNADO' then 'T' else 'NT' end as grp from health_grp group by case EVTYPE when 'TORNADO' then 'T' else 'NT' end") econ_grp=sqldf("select EVTYPE, count(*) as number_of_events, sum( (PROPDMG*PROPDMGEXP) + (CROPDMG*CROPDMGEXP) ) as econ_impact , avg( (PROPDMG*PROPDMGEXP) + (CROPDMG*CROPDMGEXP) ) as econ_impact_by_event from stormdf where (PROPDMG+CROPDMG)!='NA' group by EVTYPE order by 3 desc limit 10" ) #set impact to millions of usd econ_grp$econ_impact=round(econ_grp$econ_impact/1000000 ,0) p<-ggplot(health_grp,aes(x=reorder(EVTYPE, +total_impact),y=total_impact)) + geom_bar(stat="identity",fill=1:10) + scale_fill_manual(values=c(1:10)) + coord_flip() + geom_text(aes(y=total_impact,ymax=total_impact, label=total_impact) ,position= position_dodge(width=0.2), hjust=.5 , vjust=1.2, size=rel(3),angle=90)+ theme_bw(base_family = "Arial") + theme(panel.background = element_rect(fill = "lightblue"), panel.grid.minor = element_line(linetype = "dotted")) + labs(title="Severe Weather Public Health Impact")+ labs(y="Injuries + Fatalities" ,x="Event Type") #print(p) #label=round(econ_impact/1000000,2) g<-ggplot(econ_grp,aes(x=reorder(EVTYPE, +econ_impact),y=econ_impact)) + geom_bar(stat="identity",fill=1:10) + scale_fill_manual(values=c(1:10)) + coord_flip() + geom_text(aes(y=econ_impact,ymax=econ_impact, label= econ_impact) ,position= position_dodge(width=0.2), hjust=.5 , vjust=1.2, size=rel(3),angle=90)+ theme_bw(base_family = "Arial") + theme(panel.background = element_rect(fill = "lightblue"), panel.grid.minor = element_line(linetype = "dotted")) + labs(title="Severe Weather Economic Impact")+ labs(y="Properties and Crops Damage (Millions of Dollars)" ,x="Event Type") #print(g) grid.arrange(g,p,nrow=2) #Not used in porject but useful #convert to Date type #stormdf$BGN_DATE=strptime(stormdf$BGN_DATE,"%m/%d/%Y %H:%M:%S")

49fbee138ba296868dde79411b1205b828bbb5e0

8c7dc1facdc0672df7fed535258a1d446b419c9d

/00_packages_and_helpers/helpers_plot_functions.R

1f7c4ba89358e063ac8a5149ec730a01230697d4

[ "MIT" ]

permissive

boyercb/mmc

2b0dbebe5d6f0fcce60c9fedadd520a6ddc26e7c

654fe8d8e59a503b0ac835433b270a21aaa7f47b

refs/heads/master

2020-06-26T18:11:50.404833

2020-01-21T03:30:42

199,709,960

0

null

UTF-8

R

false

4,671

r

helpers_plot_functions.R

# ggplot theme ------------------------------------------------------------ mmc_theme <- function() { theme_bw() + theme( axis.ticks = element_blank(), axis.line = element_blank(), panel.border = element_blank(), panel.grid.major = element_line(color = '#eeeeee'), strip.background = element_blank(), legend.position = "bottom", text = element_text(family = "Palatino"), plot.title = element_text(hjust = 0.5) ) } # Plotting function ------------------------------------------------------- plot_coefs <- function(plot_data, outcome_levels, type_levels){ plot_data$outcome <- factor(plot_data$outcome, levels = outcome_levels) plot_data$type <- factor(plot_data$type, levels = type_levels) ggplot(plot_data, aes(y = fct_reorder(outcome, estimate), x = estimate, shape = index)) + geom_point() + geom_vline(xintercept = 0, linetype = "dashed", size = .25) + geom_segment(aes(x = conf.low, xend = conf.high, y = outcome, yend = outcome), alpha = .3) + geom_text(data = filter(plot_data, index == "Yes"), aes(label = round(estimate, 3)), nudge_y = 0.5, family = "Palatino", size = 2.5 ) + facet_grid(type~adjusted, scales = "free_y", space = "free_y") + labs( x = "Treatment Effect", y = "" ) + # scale_y_discrete(expand = c(.2, 0)) + coord_cartesian(clip = "off") + scale_shape_manual(values = c(19, 5), guide = FALSE) + mmc_theme() + theme() } # plot treatment effects -------------------------------------------------- plot_treatment_effects <- function( fit, outcome, data, type = "individual", color = NULL, color_name = NULL, color_values = NULL, color_labels = NULL, title = NULL, ylabel = outcome, xlabel = NULL ) { plot_df <- data %>% group_by(treatment) %>% summarise(n = n()) %>% ungroup() predictions <- predict(fit, newdata = plot_df, interval = "confidence", alpha = 0.05) plot_df <- plot_df %>% mutate( pred = predictions[, 1], conf95_low = predictions[, 2], conf95_high = predictions[, 3], label = specd(pred, 3) ) p <- ggplot(plot_df, aes( x = factor(treatment, labels = c("Control", "Treatment")), y = pred, )) if (type == "cluster") { if (is.null(color)) { p <- p + geom_jitter( aes( x = factor(treatment, labels = c("Control", "Treatment")), y = get(outcome), size = n ), data = data, alpha = 0.30, width = 0.2, height = 0 ) } else { p <- p + geom_jitter( aes( x = factor(treatment, labels = c("Control", "Treatment")), y = get(outcome), size = n, color = factor(get(color)) ), data = data, alpha = 0.30, width = 0.2, height = 0 ) } } else { if (is.null(color)) { p <- p + geom_jitter( aes( x = factor(treatment, labels = c("Control", "Treatment")), y = get(outcome) ), data = data, alpha = 0.30, width = 0.2, height = 0.1 ) } else { p <- p + geom_jitter( aes( x = factor(treatment, labels = c("Control", "Treatment")), y = get(outcome), color = factor(get(color)) ), data = data, alpha = 0.30, width = 0.2, height = 0.1 ) } } p <- p + geom_point() + geom_text(aes(label = label), nudge_x = 0.075, size = 3) + geom_errorbar(aes(ymin = conf95_low, ymax = conf95_high), width = 0) + labs( title = title, x = xlabel, y = ylabel ) + mmc_theme() + scale_color_manual( name = color_name, values = color_values, labels = color_labels ) + theme( legend.position = "bottom", axis.title = element_text(size = 10), plot.title = element_text(size = 10) ) return(p) } # plot balance ------------------------------------------------------------ plot_balance <- function(plot_data, levels){ plot_data$outcome <- factor(plot_data$outcome, levels = levels) ggplot(plot_data, aes(y = outcome, x = estimate)) + geom_point() + geom_vline(xintercept = 0, linetype = "dashed", size = .25) + geom_segment(aes(x = conf.low, xend = conf.high, y = outcome, yend = outcome), alpha = .3) + facet_grid(~blocks) + labs( x = "Treatment Effect", y = "" ) + mmc_theme() }

8a4289dad507d5d63ca971a1b9ec36639602ee8c

38829c3f894aa8617bd1d914fa8ad2a8efe962e0

/R/GCAT/man/plot_data.Rd

3c5c8e60c6ba0589e469278a8c04d281ad456936

[]

no_license

ybukhman/GCAT

04304e045c6b16cc2f1e7beb14ebedc115508c70

f7be365b210f0f4c5c3851b9edf3e5e25924f19a

refs/heads/master

2021-01-21T20:22:33.938413

2019-07-16T03:14:59

34,178,856

2

5

null

2016-03-29T16:08:58

2015-04-18T19:06:00

R

UTF-8

R

false

true

1,032

rd

plot_data.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.fit.R \name{plot_data} \alias{plot_data} \title{plot_data} \usage{ plot_data(input.well, view.raw.data = F, unlog = F, scale = 1, main = paste(plate.name(input.well), well.name(input.well)), number.points = T, draw.symbols = F, constant.added, ylim, ...) } \arguments{ \item{input.well}{The well object that need to be plottedd} \item{view.raw.data}{should the raw data be plotted? (} \item{unlog}{should data be plotted on a linear (vs. logarithmic) scale?} \item{scale}{determines the font scale for the entire graph. all cex values are calculated from this.} \item{main}{...} \item{number.points}{should points be labeled with numeric indices?} \item{draw.symbols}{- should <check.slopes> be called on the well and markings drawn on the graph?} \item{constant.added}{Similar to added.constant.} \item{ylim}{...} \item{...}{additional arguments passed to plot()} } \description{ Basic function plots time vs. OD from a well object }

9b46358f257fce2a69fcee7f440a7acb047b0ddd

d141653ead8c6ab785dbfc47d402197a19fa4985

/R/shutdowns_by_geo_scope.R

73fd8cf643d0a70ec188816a79e0ade1d7d4a070

[]

no_license

bbc/vjdata.26005.internet.shutdown

8186f93de67c91bd0a9b08c7c87bb8727726641d

fa7f8931a87329f682624c5fca21047f400cda29

refs/heads/master

2022-04-06T16:07:58.621915

2020-02-13T14:33:42

228,865,144

0

1

null

UTF-8

R

false

1,786

r

shutdowns_by_geo_scope.R

pacman::p_load('tidyverse','dplyr','ggplot2', 'bbplot2', 'lubridate', 'grid') source(prepare_dataset.R) ###### by geographic scope ###### # countries and number of shutdowns by level of geographic spread geography_scope_count <- tidy_data_geography %>% group_by(continent, country, geo_scope) %>% count(geo_scope) %>% arrange(country) %>% spread(geo_scope, n) # this step is only necessary if you notice 2 Level 3 columns. Not sure why it happens # geography_scope_count <- geography_scope_count %>% # mutate("Level 3" = sum(`Level 3` + `Level 3 `)) geography_scope_count[is.na(geography_scope_count)] <-0 geography_scope_forchart <- tidy_data_geography %>% group_by(continent, country, geo_scope) %>% count(geo_scope) %>% arrange(country) %>% group_by(geo_scope) %>% summarise(total = sum(n)) # Plot scope.plot <- ggplot(geography_scope_forchart, aes(x=geo_scope, y=total, #group = continent, fill = "#4D2C7A")) + geom_bar(stat = "identity", position = "identity") + bbc_style() + scale_fill_manual(values = c("#1380A1")) + scale_y_continuous(limits = c(0,125)) + scale_x_discrete("month", labels= c("Local","Regional","Multi-regional")) + theme(panel.grid.major.x = element_blank(), legend.position = "none") + geom_hline(yintercept = 0, size = 1, colour = "#333333") + labs(title = "Most shutdowns are targeted to specific areas") scope.plot finalise_plot(scope.plot, source = "Source: Access Now, 2019", save_filepath = "~/Dropbox (BBC)/Visual Journalism/Data/2019/vjdata.26005.internet.shutdown/output/shutdowns_by_geo_scope-nc.png", width_pixels = 640)