help(lapply(list, function))
help(lapply
)
sample(letters(1:15))
sample(letters[1:4],15)
sample(letters[1:4],15,replace=TRUE)
sample(letters[1:4],15,replace=TRUE)
10.9 +0.23*73
29 - (10.9 +0.23*73)
27.69 - 29
10.9 +0.23*70
library(MASS)
data(birthwt)
str(birthwt)
library(fpp)
install.packages("fpp2")
install.packages("fpp2")
library(fpp2)
sessionInfo()
library(fpp2)
data(ausair)
air <- window(ausair,start=1990, end=2004)
fit1 <- holt(air, alpha=0.8, beta=0.2, initial="simple", h=5)
accuracy(fit1,ausair)
fit2 <- holt(air, alpha=0.8, beta=0.2, initial="simple", exponential=TRUE, h=5)
accuracy(fit2,ausair)
fit3 <- holt(air, alpha=0.8, beta=0.2, damped=TRUE, initial="simple", h=5)
fit3 <- holt(air, alpha=0.8, beta=0.2, damped=TRUE, h=5)
accuracy(fit3,ausair)
help("accuracy")
library(fpp2)
data(usdeaths)
data("sunspotarea")
autoplot(usdeaths)
ggseasonplot(usdeaths)
ggsubseriesplot(usdeaths)
autoplot(sunspotarea)
ggseasonplot(sunspotarea)
ggsubseriesplot(sunspotarea)
sample(1:19,19)
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install()
library(biomaRt)
BLOSUM50
BLOSSUM50
??"BLOSSUM"
data(BLOSUM50)
library(Biostrings)
data(BLOSUM50)
BLOSUM50
data(PAM120)
PAM120
install.packages("KScorrect")
library(KScorrect)
x <- runif(200)
Lc <- LcKS(x, cdf="pnorm", nreps=999)
hist(Lc$D.sim)
abline(v = Lc$D.obs, lty = 2)
print(Lc, max=50)
BiocManager::install("spikeLI", version = "3.8")
library(spikeLI)
SPIKE_IN
library(affy)
BiocManager::install(c("affy","ALLMLL","gcrma","affypdnn"))
library(affy)
library(ALLMLL)
data(MLL.B)
MLLsubset = MLL.B[,c(2,1,3:5,14,6,13)]
pData(MLLsubset)
phenoData(MLLsubset)
dim(exprs(MLLsubset))
annotation(MLLsubset)
length(probeNames(MLLsubset))
length(probeNames(MLLsubset))
sampleNames(MLLsubset)
par(mar=c(2.0,2.1,1.6,1.1),oma=c(1,1,0,0))
palette.gray <- c(rep(gray(0:10/10),times=seq(1,41,by=4)))
image(MLLsubset[,1],transfo=function(x) x,col=palette.gray) ## No transform
image(MLLsubset[,1],col=palette.gray)  ## Log transform
sampleNames(MLLsubset) = letters[1:8]
cols = brewer.pal(9, “Set1”)[-6] ## Fancy Colors - taking out yellow
boxplot(MLLsubset, col=cols)
hist(MLLsubset,col=cols,lty=1,lwd=3,xlab="Log(base 2) intensities")
legend(12,1.0,letters[1:8],lty=1,col=cols)
cols = brewer.pal(9, "Set1")[-6] ## Fancy Colors - taking out yellow
boxplot(MLLsubset, col=cols)
hist(MLLsubset,col=cols,lty=1,lwd=3,xlab="Log(base 2) intensities")
library(RColorBrewer)
cols = brewer.pal(9, "Set1")[-6] ## Fancy Colors - taking out yellow
boxplot(MLLsubset, col=cols)
hist(MLLsubset,col=cols,lty=1,lwd=3,xlab="Log(base 2) intensities")
legend(12,1.0,letters[1:8],lty=1,col=cols)
par(mfrow=c(2,4))
MAplot(MLLsubset,cex=0.75)
help(MLL.B)
MLL.B
BiocManager::install("ALL", version = "3.8")
library(ALL)
ALL
data(ALL)
ALL
ages <- c(13, 13, 22, 26, 33, 33, 59, 72, 72, 72, 77, 78, 78, 80, 81, 82, 85, 85, 85, 86, 88
)
install.packages('nortest')
library(nortest)
help(lillie.test)
lillie.test(ages)
install.packages('KScorrect')
library(KScorrect)
help(LcKS)
LcKs(ages,cdf='pnorm')
LcKS(ages,cdf='pnorm')
library(EMA)
help(EMA)
library(affy)
browseVignettes()
install.packages("npsm")
library(npsm)
data(sievers)
boxplot(weight.gain~group,data=sievers)
x <- with(sievers,weight.gain[group==‘Control’])
y <- with(sievers,weight.gain[group==‘Ozone’])
fk.test(x,y)
x <- with(sievers,weight.gain[group=='Control'])
y <- with(sievers,weight.gain[group=='Ozone'])
fk.test(x,y)
dev.x <- x - median(x)
dev.y <- y - median(y)
dev.x
dev.y
log(with(dev.x,dev.x > 0))
log(dev.x[dev.x > 0])
poslogx <- log(dev.x[dev.x > 0])
poslogy <- log(dev.y[dev.y > 0])
folded <- c(poslogx,poslogy)
rankF <- rank(folded)
rankF
length(poslogx)
length(poslogy)
length(rankF)
fkScore <- rankF[12:22]
sievers
fligner.test(weight.gain~group,data=sievers)
dev.x
absdev.x <- abs(dev.x)
absdev.y <- abs(dev.y)
folded <- c(absdev.x,absdev.y)
rankF <- rank(folded)
rankF
summary(sievers)
sievers$group <- as.factor(sievers$group)
summary(sievers)
fkScore <- function(x){}
fkScore <- function(x){
}
dev.x
dex.y
absdev.x
absdev.y
folded <- c(absdev.x,absdev.y)
logFolded <- log(folded)
rankF <- rank(logFolded)
rankF
rankY <- rankF[24:45]
p <- rankY/(2*23) + 0.5
p
qnorm(p)
help(pnorm)
rankFlog <- rank(logFolded)
rankF <- rank(folded)
rankY <- rankF[24:45]
p <- rankY/(2*23) + 0.5
p
pnorm(p)
sum(pnorm(p)^2)
fk.test
scoreA <- sum(pnorm(p)^2)
mu <- mean(scoreA)
v <- sd(scoreA)
(scoreA - mu)/sqrt(v)
v
mu
scoreA
(scoreA - mu)/v
dev.y
zed <- c(abs(dev.x),abs(dev.y))
zed
rank(zed)
rankY <- rank(zed)[24:45]
length(rankY)
summary(sievers)
length(dev.y)
rankY/(2*23)
rankY/(2*46)+0.5
qnorm(rankY/(2*46)+0.5)
sum(qnorm(rankY/(2*46)+0.5)^2)
mean(qnorm(rankY/(2*46)+0.5))
mean(qnorm(rankY/(2*46)+0.5)^2)
sd(qnorm(rankY/(2*46)+0.5)^2)
(28.7113 - 1.305059)/1.360324
fkscores
fk.test
help("getScores")
library(coin)
?ks.test
install.packages("RVAideMemoire")
library(RVAideMemoire)
x <- rpois(30,2)
y <- rpois(30,3)
CvM.test(x,y)
lg <- c(293,291,289,430,510,353,318)
hg <- c(227,250,277,290,297,325,337,340)
CvM.test(lg,hg)
CvM.test(hg,lg,alt="g")
help(CvM.terst)
help(CvM.test)
CvM.test(hg,lg,alternative="g")
CvM.test(hg,lg,alternative="greater")
ad.test(hg,lg)
??ad.test
install.packages("kSamples")
library(kSamples)
help(kSamples)
ad.test(hg,lg)
Diabetic <- c(42,44,38,52,48,46,34,44,38)
Normal <- c(34,43,35,33,34,36,30,31,27,28,27,30,37,38,32,32,36,32,32,38,42,36,44,33,38)
conover <- c(21,20,17,25,29,21,32,18,32,31)
bradley <- c(45,14,13,31,35,20,58,41,64,25)
peciesA <- c(131,134,137,127,128,118,134,129,131,115)
speciesB <- c(107,122,144,131,108,118,122,127,125,124)
BiocManager::install("flowCore")
library(flowCore)
file.name <- system.file("extdata","0877408774.B08", package="flowCore")
x <- read.FCS(file.name, transformation=FALSE)
summary(x)
library(ggcyto)
autoplot(x, "FL1-H", "FL2-H")
install.packages("ggcyto")
BiocManager::install("ggcyto")
library(ggcyto)
autoplot(x,"FL1-H","FL2-H")
autoplot(x, "FL1-H")
frames <- lapply(dir(system.file("extdata", "compdata", "data",
package="flowCore"), full.names=TRUE),
read.FCS)
as(frames, "flowSet")
names(frames) <- sapply(frames, keyword, "SAMPLE ID")
fs <- as(frames, "flowSet")
fs
phenoData(fs)$Filename <- fsApply(fs,keyword, "$FIL")
pData(phenoData(fs))
read.flowSet(path = system.file("extdata", "compdata", "data",
package="flowCore"))
fs <- read.flowSet(path=system.file("extdata", "compdata", "data",
package="flowCore"), name.keyword="SAMPLE ID",
phenoData=list(name="SAMPLE ID", Filename="$FIL"))
fs
pData(phenoData(fs))
## ----fsApply1, echo=TRUE, results='markup'-------------------------------
fsApply(fs, each_col, median)
## ----fsApply2, echo=TRUE,results='markup'--------------------------------
fsApply(fs,function(x) apply(x, 2, median), use.exprs=TRUE)
install.packages("nullabor")
library(nullabor)
browseVignettes()
d <- lineup(null_permute("mpg"), mtcars)
head(d)
attr(d, "pos")
ggplot(data=d, aes(x=mpg, y=wt)) + geom_point() + facet_wrap(~ .sample)
library(ggplot2)
ggplot(data=d, aes(x=mpg, y=wt)) + geom_point() + facet_wrap(~ .sample)
help(mtcars)
head(null_dist("mpg", dist = "normal")(mtcars))
d2 <- null_dist("mpg", dist = "normal")(mtcars)
ggplot(data=d2, aes(x=mpg)) + geom_histogram(color="darkblue", fill="lightblue") + facet_wrap(~ .sample)
ggplot(data=d2, aes(x=mpg)) + geom_histogram(color="darkblue", fill="lightblue")
class(d)
class(d2)
dim(D)
dim(d)
dim(d2)
help("null_dist")
test <- rorschach(null_dist("mpg",dist="normal"),mtcars,n=3)
attr(test,pos)
attr(test, "pos")
ggplot(data=test, aes(x=mpg)) + geom_histogram(color="darkblue", fill="lightblue",bins=10)+ facet_wrap(~ .sample)
test <- rorschach(null_dist("mpg",dist="normal"),mtcars,n=3,p=0)
ggplot(data=test, aes(x=mpg)) + geom_histogram(color="darkblue", fill="lightblue",bins=10)+ facet_wrap(~ .sample)
attr(test, "pos")
test <- rorschach(null_dist("mpg",dist="normal"),mtcars,n=3,p=1)
attr(test, "pos")
ggplot(data=test, aes(x=mpg)) + geom_histogram(color="darkblue", fill="lightblue",bins=10)+ facet_wrap(~ .sample)
df <- data.frame(rnorm(150))
dim(df)
dframe <- data.frame(x = rnorm(150))
ggplot(data=rorschach(method=null_dist("x", "norm"), n = 3, true=dframe)
) + geom_histogram(aes(x=x, y=..density..), binwidth=0.25) +
facet_grid(.~.sample) +
geom_density(aes(x=x), colour="steelblue", size=1)
dframe$x = runif(150)
ggplot(data=rorschach(method=null_dist("x", "uniform", params=list(min=0, max=1)),
n = 3, true=dframe)) +
geom_histogram(aes(x=x, y=..density..), binwidth=0.1) +
facet_grid(.~.sample) +
geom_density(aes(x=x), colour="steelblue", size=1)
dframe <- data.frame(x=runif(50,min=0,max=1))
ggplot(data=rorschach(method=null_dist("x", "norm"), n = 9, true=dframe, p = 1)
) + geom_histogram(aes(x=x, y=..density..), binwidth=0.25) +
facet_grid(.~.sample) +
geom_density(aes(x=x), colour="steelblue", size=1)
dframe <- data.frame(x=runif(50,min=0,max=1))
ggplot(data=rorschach(method=null_dist("x", "norm"), n = 9, true=dframe, p = 1)
) + geom_histogram(aes(x=x, y=..density..), binwidth=0.25) +
facet_wrap(.~.sample) +
geom_density(aes(x=x), colour="steelblue", size=1)
library(MASS)
data(wasps)
class(wasps)
dim(wasps)
help(wasps)
wasp.lda <- lda(Group~., data=wasps[,-1])
wasp.ld <- predict(wasp.lda, dimen=2)$x
true <- data.frame(wasp.ld, Group=wasps$Group)
wasp.sim <- data.frame(LD1=NULL, LD2=NULL, Group=NULL, .n=NULL)
for (i in 1:19) {
x <- wasps
x$Group <- sample(x$Group)
x.lda <- lda(Group~., data=x[,-1])
x.ld <- predict(x.lda, dimen=2)$x
sim <- data.frame(x.ld, Group=x$Group, .n=i)
wasp.sim <- rbind(wasp.sim, sim)
}
pos <- sample(1:20, 1)
d <- lineup(true=true, samples=wasp.sim, pos=pos)
ggplot(d, aes(x=LD1, y=LD2, colour=Group)) +
facet_wrap(~.sample, ncol=5) +
geom_point() + theme(aspect.ratio=1)
pos
head(wasps)
fdat <- system.file("Test", package="PROcess")
fs <- list.files(fdat, pattern="\\.*csv\\.*", full.names=TRUE)
f1 <- read.files(fs[1])
fcut <- f1[f1[,1]>0,]
bseoff <-bslnoff(fcut,method="loess",plot=TRUE, bw=0.1)
title(basename(fs[1]))
library(PROcess)
BiocManager::install("PROcess", version = "3.8")
library(PROcess)
fdat <- system.file("Test", package="PROcess")
fs <- list.files(fdat, pattern="\\.*csv\\.*", full.names=TRUE)
f1 <- read.files(fs[1])
fcut <- f1[f1[,1]>0,]
bseoff <-bslnoff(fcut,method="loess",plot=TRUE, bw=0.1)
title(basename(fs[1]))
f1
fs
pkgobj <- isPeak(bseoff,span=81,sm.span=11,plot=TRUE)
testM <- rmBaseline(fdat)
rtM <- renorm(testM, cutoff=1500)
help("rmBaseline")
eakfile <- paste(tempdir(), "testpeakinfo.csv", sep = "/")
getPeaks(rtM, peakfile)
peakfile <- paste(tempdir(), "testpeakinfo.csv", sep = "/")
getPeaks(rtM, peakfile)
getwd()
knitr::opts_chunk$set(echo = TRUE)
setwd("~/Dropbox/2019 Spring/FlowThroughCentrality")
library(coin) # load the package in R
library(tidyverse) # for tidying the data
library(xtable) # for obtaining tables in LaTex format
library(PMCMRplus) # For Nashimoto-Wright NPM-test for ordered means of non-normal datajo
setwd("./Simulations/Nodes50Deg6Pert3")
# Create variable names for all data sets
varNames <- c("Network",paste0(c("Cent","Tier"), rep(1:50,each=2)))
dataBW <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Betweenness.csv",skip=4,nrows=101,header=FALSE)
dataBW <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Betweenness.csv",skip=4,nrows=101,header=FALSE)
getwd()
setwd("~/Dropbox/2019 Spring/FlowThroughCentrality")
getwd()
setwd("~/Dropbox/2019 Spring/FlowThroughCentrality")
getwd()
setwd("./Simulations/Nodes50Deg6Pert3")
getwd()
dataBW <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Betweenness.csv",skip=4,nrows=101,header=FALSE)
dataBW <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Betweenness.csv",skip=4,nrows=101,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_if(is.numeric, .funs =function(x) 100*(x-x[1])/x[1])
dataBW <- dataBW[-1,]
dataBW$Measure <- rep("BW",100)
dataBWLong <- gather(dataBW,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Network,Measure,Centrality,CentLevel)
dataCL <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Closeness.csv",skip=4,nrows=101,header=FALSE)
dataCL <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Closeness.csv",skip=4,nrows=101,header=FALSE)
colnames(dataCL) <- varNames
dataCL$Network<-str_replace_all(dataCL$Network, c(" " = "" ))
dataCL <- dataCL %>% mutate_if(is.numeric, .funs =function(x) 100*(x-x[1])/x[1])
dataCL <- dataCL[-1,]
dataCL$Measure <- rep("CL",100)
dataCLLong <- gather(dataCL,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Network,Measure,Centrality,CentLevel)
dataFB <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_FlowBetween.csv",skip=4,nrows=101,header=FALSE)
dataFB <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_FlowBetween.csv",skip=4,nrows=101,header=FALSE)
colnames(dataFB) <- varNames
dataFB$Network<-str_replace_all(dataFB$Network, c(" " = "" ))
dataFB <- dataFB %>% mutate_if(is.numeric, .funs =function(x) 100*(x-x[1])/x[1])
dataFB <- dataFB[-1,]
dataFB$Measure <- rep("FB",100)
dataFBLong <- gather(dataFB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Network,Measure,Centrality,CentLevel)
dataFT <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Flowthrough.csv",skip=4,nrows=101,header=FALSE)
dataFT <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_Flowthrough.csv",skip=4,nrows=101,header=FALSE)
colnames(dataFT) <- varNames
dataFT$Network<-str_replace_all(dataFT$Network, c(" " = "" ))
dataFT <- dataFT %>% mutate_if(is.numeric, .funs =function(x) 100*(x-x[1])/x[1])
dataFT <- dataFT[-1,]
dataFT$Measure <- rep("FT",100)
dataFTLong <- gather(dataFT,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Network,Measure,Centrality,CentLevel)
dataSB <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_StableBetween.csv",skip=4,nrows=101,header=FALSE)
dataSB <- read.csv(file="Nodes50_AvgDeg06_UW_01_Pert03_StableBetween.csv",skip=4,nrows=101,header=FALSE)
colnames(dataSB) <- varNames
dataSB$Network<-str_replace_all(dataSB$Network, c(" " = "" ))
dataSB <- dataSB %>% mutate_if(is.numeric, .funs =function(x) 100*(x-x[1])/x[1])
dataSB <- dataSB[-1,]
dataSB$Measure <- rep("SB",100)
dataSBLong <- gather(dataSB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Network,Measure,Centrality,CentLevel)
n50deg6pert3 <- bind_rows(dataFTLong, dataFBLong, dataCLLong, dataBWLong, dataSBLong)
n50deg6pert3$Measure <- ordered(n50deg6pert3$Measure, levels=c("FT","FB","CL","BW","SB"))
n50deg6pert3lev1 <- subset(n50deg6pert3, CentLevel == "Cent1", select = c("Network","Measure","Centrality"))
npmLev1 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev1)
n50deg6pert3lev2 <- subset(n50deg6pert3, CentLevel == "Cent2", select = c("Network","Measure","Centrality"))
npmLev2 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev2)
n50deg6pert3lev3 <- subset(n50deg6pert3, CentLevel == "Cent3", select = c("Network","Measure","Centrality"))
npmLev3 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev3)
n50deg6pert3lev4 <- subset(n50deg6pert3, CentLevel == "Cent4", select = c("Network","Measure","Centrality"))
npmLev4 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev4)
n50deg6pert3lev5 <- subset(n50deg6pert3, CentLevel == "Cent5", select = c("Network","Measure","Centrality"))
npmLev5 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev5)
npmLev1
npmLev2
npmLev3
npmLev4
npmLev5
cv <- function(x){
cv=100*(sd(x)/mean(x))
}
n50deg6pert3all <- bind_rows(n50deg6pert3lev1,n50deg6pert3lev2,n50deg6pert3lev3,n50deg6pert3lev4,n50deg6pert3lev5)
ggplot(n50deg6pert3all, aes(x=Network,y=Centrality,colour=Measure)) + geom_line() + facet.grid(CentLevel~.)
n50deg6pert3all <- bind_rows(n50deg6pert3lev1,n50deg6pert3lev2,n50deg6pert3lev3,n50deg6pert3lev4,n50deg6pert3lev5)
ggplot(n50deg6pert3all, aes(x=Network,y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
names(n50deg6pert3all)
names(n50deg6pert3lev1)
n50deg6pert3all <- bind_rows(n50deg6pert3lev1,n50deg6pert3lev2,n50deg6pert3lev3,n50deg6pert3lev4,n50deg6pert3lev5, .id="CentLevel")
names(n50deg6pert3all)
head(n50deg6pert3all)
tail(n50deg6pert3all)
ggplot(n50deg6pert3all, aes(x=Network,y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
n50deg6pert3all[1:500,]
ggplot(n50deg6pert3all, aes(x=as.numeric(Network),y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
summary(n50deg6pert3all)
ggplot(n50deg6pert3all, aes(x=1:100,y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
dataFB$Sample <- 1:100
summary(dataFB)
dim(dataFB)
dataBW$Sample <- 1:100
dataCL$Sample <- 1:100
dataFB$Sample <- 1:100
dataFT$Sample <- 1:100
dataSB$Sample <- 1:100
n50deg6pert3 <- bind_rows(dataFTLong, dataFBLong, dataCLLong, dataBWLong, dataSBLong)
n50deg6pert3$Measure <- ordered(n50deg6pert3$Measure, levels=c("FT","FB","CL","BW","SB"))
n50deg6pert3lev1 <- subset(n50deg6pert3, CentLevel == "Cent1", select = c("Network","Measure","Centrality"))
npmLev1 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev1)
n50deg6pert3lev2 <- subset(n50deg6pert3, CentLevel == "Cent2", select = c("Network","Measure","Centrality"))
npmLev2 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev2)
n50deg6pert3lev3 <- subset(n50deg6pert3, CentLevel == "Cent3", select = c("Network","Measure","Centrality"))
npmLev3 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev3)
n50deg6pert3lev4 <- subset(n50deg6pert3, CentLevel == "Cent4", select = c("Network","Measure","Centrality"))
npmLev4 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev4)
n50deg6pert3lev5 <- subset(n50deg6pert3, CentLevel == "Cent5", select = c("Network","Measure","Centrality"))
npmLev5 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev5)
n50deg6pert3all <- bind_rows(n50deg6pert3lev1,n50deg6pert3lev2,n50deg6pert3lev3,n50deg6pert3lev4,n50deg6pert3lev5, .id="CentLevel")
ggplot(n50deg6pert3all, aes(x=Sample,y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
n50deg6pert3lev1 <- subset(n50deg6pert3, CentLevel == "Cent1", select = c("Sample","Network","Measure","Centrality"))
dataBWLong <- gather(dataBW,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataCLLong <- gather(dataCL,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataFBLong <- gather(dataFB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataFTLong <- gather(dataFT,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataSBLong <- gather(dataSB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
n50deg6pert3 <- bind_rows(dataFTLong, dataFBLong, dataCLLong, dataBWLong, dataSBLong)
n50deg6pert3$Measure <- ordered(n50deg6pert3$Measure, levels=c("FT","FB","CL","BW","SB"))
n50deg6pert3lev1 <- subset(n50deg6pert3, CentLevel == "Cent1", select = c("Sample","Network","Measure","Centrality"))
npmLev1 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev1)
n50deg6pert3lev2 <- subset(n50deg6pert3, CentLevel == "Cent2", select = c("Sample","Network","Measure","Centrality"))
npmLev2 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev2)
n50deg6pert3lev3 <- subset(n50deg6pert3, CentLevel == "Cent3", select = c("Sample","Network","Measure","Centrality"))
npmLev3 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev3)
n50deg6pert3lev4 <- subset(n50deg6pert3, CentLevel == "Cent4", select = c("Sample","Network","Measure","Centrality"))
npmLev4 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev4)
n50deg6pert3lev5 <- subset(n50deg6pert3, CentLevel == "Cent5", select = c("Sample","Network","Measure","Centrality"))
npmLev5 <- NPMTest(Centrality~Measure,data=n50deg6pert3lev5)
n50deg6pert3all <- bind_rows(n50deg6pert3lev1,n50deg6pert3lev2,n50deg6pert3lev3,n50deg6pert3lev4,n50deg6pert3lev5, .id="CentLevel")
ggplot(n50deg6pert3all, aes(x=Sample,y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
sumData <- n50deg6pertall %>% group_by(CentLevel,Measure) %>% summarise(Mean=mean(Centrality),SD=sd(Centrality),CV=cv(Centrality))
sumData <- n50deg6pert3all %>% group_by(CentLevel,Measure) %>% summarise(Mean=mean(Centrality),SD=sd(Centrality),CV=cv(Centrality))
ggplot(sumData,aes(x=CentLevel,y=CV,colour=Measure)) + geom_bar(stat="identity")
ggplot(sumData,aes(x=CentLevel,y=CV,colour=Measure)) + geom_point()
ggplot(sumData,aes(x=CentLevel,y=Mean,colour=Measure)) + geom_point()
ggplot(sumData,aes(x=CentLevel,y=Mean,colour=Measure)) + geom_line()
ggplot(sumData,aes(x=Sample,y=Mean,colour=Measure)) + geom_line()
xtable(sumData)
mad
sumData <- n50deg6pert3all %>% group_by(CentLevel,Measure) %>% summarise(Mean=mean(Centrality),SD=sd(Centrality),Median=median(Centrality),MAD=mad(Centrality))
xtable(sumData)
