fourYears400long <- fourYears400long %>%
mutate(LogBestTime = log(BestTime)) %>%
mutate(Year = Grade-9) %>%
mutate(GenInd = if_else(Gender=="Boys", 1, 0))
fourYears800long <- fourYears800long %>%
mutate(LogBestTime = log(BestTime)) %>%
mutate(Year = Grade-9) %>%
mutate(GenInd = if_else(Gender=="Boys", 1, 0))
fourYears1600long <- fourYears1600long %>%
mutate(LogBestTime = log(BestTime)) %>%
mutate(Year = Grade-9) %>%
mutate(GenInd = if_else(Gender=="Boys", 1, 0))
fourYears200longB <- subset(fourYears200long, Gender=="Boys")
fourYears200longG <- subset(fourYears200long, Gender=="Girls")
fourYears400longB <- subset(fourYears400long, Gender=="Boys")
fourYears400longG <- subset(fourYears400long, Gender=="Girls")
fourYears800longB <- subset(fourYears800long, Gender=="Boys")
fourYears800longG <- subset(fourYears800long, Gender=="Girls")
fourYears1600longB <- subset(fourYears1600long, Gender=="Boys")
fourYears1600longG <- subset(fourYears1600long, Gender=="Girls")
mixed200rsg =  lmer(LogBestTime ~ Year + (1 + Year | Name), data = fourYears200longG)
summary(mixed200rsg)
library(lme4)
mixed200rsg =  lmer(LogBestTime ~ Year + (1 + Year | Name), data = fourYears200longG)
summary(mixed200rsg)
plot(fitted(mixed200rsg), resid(mixed200rsg, type = "pearson"))# this will create the plot
abline(0,0, col="red")
qqnorm(resid(mixed200rsg))
qqline(resid(mixed200rsg), col = "red")
performance::icc(mixed200rsg)
mixed200rsb =  lmer(LogBestTime ~ Year + (1 + Year | Name), data = fourYears200longB)
summary(mixed200rsb) # REML = -6064
plot(fitted(mixed200rsb), resid(mixed200rsb, type = "pearson"))# this will create the plot
abline(0,0, col="red") # looks as good as one can expect.
qqnorm(resid(mixed200rsb))
qqline(resid(mixed200rsb), col = "red")
performance::icc(mixed200rsb)
mixed200rs2fint =  lmer(LogBestTime ~ Year + GenInd + Year:GenInd + (1 + Year | Name), data = fourYears200long)
summary(mixed200rs2fint) # REML = -6533
performance::icc(mixed200rs2fint) # Adjusted ICC = 0.778
mixed200girlsLog <- lmer(LogBestTime ~ poly(Year,2, raw=FALSE) +
(poly(Year,1, raw=FALSE) || Name),
data = fourYears200longG, REML = TRUE)
summary(mixed200girlsLog)
pred200 <- predicted(mixed200girlsLong)
pred200 <- predict(mixed200girlsLong)
??lmer
citation(package="lmer")
citation(package="lme4")
RShowDoc("lmerperf", package = "lme4")
library("grid")
zmargin <- theme(panel.spacing=unit(0,"lines"))
library("lattice")
data(sleepstudy)
str(sleepstudy=)
str(sleepstudy)
head(sleepstudy)
str(fourYears1600longG)
1248/4
str(fourYears1600long)
samp
n1600 <- nrows(fourYears1600long)
fourYears1600long$Athlete <- as.factor(rep(1:n1600, each=4))
samp1600 <- sample(1:n1600,18)
n1600 <- nrow(fourYears1600long)
fourYears1600long$Athlete <- as.factor(rep(1:n1600, each=4))
samp1600 <- sample(1:n1600,18)
n1600 <- nrow(fourYears1600long)/4
fourYears1600long$Athlete <- as.factor(rep(1:n1600, each=4))
samp1600 <- sample(1:n1600,18)
sel1600 = fourYears1600long %>%
mutate(select = factor(Athlete %in% samp1600),
sz = c(.5, 1)[select]) %>%
group_by(Athlete, select) %>%
filter(select == TRUE)
str(sel1600)
sel1600 = fourYears1600long %>%
mutate(Sampled = factor(Athlete %in% samp1600),
sz = c(.5, 1)[Sampled]) %>%
group_by(Athlete, Sampled)
sel1600 <- sel1600[,Sampled == TRUE]
names(sel1600)
table(sel1600$Sampled)
sel1600 <- sel1600[,"Sampled" == TRUE]
help("select")
sel1600 <- sel1600[,"Sampled" == TRUE] %>%
select(Athlete, BestTime, LogBestTime, Year, GenInd)
n1600 <- nrow(fourYears1600long)/4
fourYears1600long$Athlete <- as.factor(rep(1:n1600, each=4))
samp1600 <- sample(1:n1600,18)
sel1600 = fourYears1600long %>%
mutate(Sampled = factor(Athlete %in% samp1600),
sz = c(.5, 1)[Sampled]) %>%
group_by(Athlete, Sampled)
sel1600 <- sel1600[,"Sampled" == TRUE] %>%
select(Athlete, BestTime, LogBestTime, Year, GenInd)
n1600 <- nrow(fourYears1600long)/4
fourYears1600long$Athlete <- as.factor(rep(1:n1600, each=4))
samp1600 <- sample(1:n1600,18)
sel1600 = fourYears1600long %>%
mutate(Sampled = factor(Athlete %in% samp1600),
sz = c(.5, 1)[Sampled]) %>%
group_by(Athlete, Sampled)
names(sel1600)
sel1600 <- sel1600 %>%
select(BestTime:Sampled)
names(sel1600)
str(sel1600)
sel1600 <- sel1600[, "Sampled"==TRUE]
dim(sel1600)
sel1600 = fourYears1600long %>%
mutate(Sampled = factor(Athlete %in% samp1600),
sz = c(.5, 1)[Sampled]) %>%
group_by(Athlete, Sampled)
sel1600 <- sel1600 %>%
select(BestTime:Sampled)
help("subset")
sel1600 = fourYears1600long %>%
mutate(Sampled = factor(Athlete %in% samp1600),
sz = c(.5, 1)[Sampled]) %>%
group_by(Athlete, Sampled)
sel1600 <- subset(sel1600, Sampled == TRUE, select = BestTime:Sampled)
dim(sel1600)
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "xy",
layout = c(9, 2), type = c("g", "p", "r"),
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
zmargin <- theme(panel.spacing=unit(0,"lines"))
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "xy",
layout = c(9, 2), type = c("g", "p", "r"),
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
help("xyplot")
zmargin <- theme(panel.spacing=unit(0,"lines"), layout.width=9, layout.height=5.5)
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "xy",
layout = c(9, 2), type = c("g", "p", "r"),
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
help(par)
par(mar(c(1, 1, 1, 1) + 0.1))
par(mar = c(1, 1, 1, 1) + 0.1)
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "xy",
layout = c(9, 2), type = c("g", "p", "r"),
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
help(xyplot)
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "iso",
layout = c(9, 2), type = c("g", "p", "r"),
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "fill",
layout = c(9, 2), type = c("g", "p", "r"),
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
xyplot(BestTime ~ Year | Athlete, sel1600, aspect = "fill",
layout = c(9, 2), type = c("g", "p", "r"), groups= GenInd,
index.cond = function(x, y) coef(lm(y ~ x))[2],
xlab = "Year in High School",
ylab = "Best Time",
as.table = TRUE)
rs1600 <- lmer(BestTime ~ Gender + Year + (Year | Athlete), fourYears1600long)
str(fourYears1600long)
as.data.frame(VarCorr(rs1600))
rs1600 <- lmer(BestTime ~ Gender + Year + Gender:Year + (Year | Athlete), fourYears1600long)
as.data.frame(VarCorr(rs1600))
summary(rs1600)
fourYears1600longB <- subset(fourYears1600long, Gender=="Boys")
fourYears1600longG <- subset(fourYears1600long, Gender=="Girls")
rs1600orthogG <- lmer(BestTime ~ polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600longG, polyDays <- poly(Year, 2)))
rs1600orthogG <- lmer(BestTime ~ polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600longG, polyYear <- poly(Year, 2)))
anova(rs1600orthogG)
plot(fitted(rs1600), resid(rs1600, type = "pearson"))
plot(fitted(rs1600orthogG), resid(rs1600orthogG, type = "pearson"))
rs1600orthogG <- lmer(LogBestTime ~ polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600longG, polyYear <- poly(Year, 2)))
plot(fitted(rs1600orthogG), resid(rs1600orthogG, type = "pearson"))
anova(rs1600orthogG)
rs1600orthogG <- lmer(BestTime ~ polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600longG, polyYear <- poly(Year, 2)))
qqnorm(resid(rs1600orthogG))
qqline(resid(rs1600orthogG), col = "red")
qqnorm(resid(rs1600))
qqline(resid(rs1600), col = "red")
rs1600orthogG <- lmer(LogBestTime ~ polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600longG, polyYear <- poly(Year, 2)))
qqnorm(resid(rs1600orthogG))
qqline(resid(rs1600orthogG), col = "red")
rs1600orthog <- lmer(BestTime ~ Gender + polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600long, polyYear <- poly(Year, 2)))
qqnorm(resid(rs1600orthog))
qqline(resid(rs1600orthog), col = "red")
anova(rs1600orthog)
anova(rs1600orthogG)
rs1600orthLin <- lmer(BestTime ~ Gender + polyYear  +
(polyYear | Athlete),
within(fourYears1600long, polyYear <- poly(Year, 1)))
rs1600orthQuad <- lmer(BestTime ~ Gender + polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600long, polyYear <- poly(Year, 2)))
anova(rs1600orthQuad, rs1600orthLin, rs1600)
rs1600 <- lmer(BestTime ~ Gender + Year + Gender:Year + (Year | Athlete), fourYears1600long)
rs1600orthQuad <- lmer(BestTime ~ Gender + polyYear[ , 1] + polyYear[ , 2] +
(polyYear[ , 1] + polyYear[ , 2] | Athlete),
within(fourYears1600long, polyYear <- poly(Year, 2)))
rs1600orthLin <- lmer(BestTime ~ Gender + polyYear  +
(polyYear | Athlete),
within(fourYears1600long, polyYear <- poly(Year, 1)))
anova(rs1600orthQuad, rs1600orthLin, rs1600)
anova(rs1600orthQuad, rs1600orthLin)
anova(rs1600, rs1600orthLin)
qqnorm(resid(rs1600orthLin))
summary(rs1600orthQuad)
??simulate
help("bind_rows")
getwd()
varNames <- c("Network",paste0(c("Cent","Tier"), rep(1:62,each=2)))
dataBW <- read.csv(file="./HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
getwd()
list.files()
dataBW <- read.csv(file="~/HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
dataBW <- read.csv(file="./HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
getwd()
dataBW <- read.csv(file="/Users/monniemcgee/Dropbox/2022Spring/Research/SONYInnovationGrant/JDSSpecialIssue/Code/HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) 2*(x-x[1])/(abs(x)-abs(x[1])))
head(dataBW)
rm(dataBW)
rm(dataBWtest)
rm(dataBWtest2)
dataBW <- read.csv(file="/Users/monniemcgee/Dropbox/2022Spring/Research/SONYInnovationGrant/JDSSpecialIssue/Code/HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
head(dataBW)
dataBW$Cent2
dataBW$Cent2-1
dataBW$Cent2+1
x <- dataBW$Cent2
(2*(x-x[1]))/(abs(x)-abs(x[1]))
x-1
rpe = (2*(x-x[1]))/(abs(x)-abs(x[1]))
rpe -1
x <- dataBW$Cent2+1
rpe = (2*(x-x[1]))/(abs(x)-abs(x[1]))
rpe
rpe-1
head(dataBW)
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs = function(x) x + 1)
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) 2*(x-x[1])/(abs(x)-abs(x[1])))
head(dataBW)
rm(dataBW)
dataBW <- read.csv(file="/Users/monniemcgee/Dropbox/2022Spring/Research/SONYInnovationGrant/JDSSpecialIssue/Code/HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs = function(x) x + 1)
head(dataBW)
??ifelse
dataBW <- read.csv(file="/Users/monniemcgee/Dropbox/2022Spring/Research/SONYInnovationGrant/JDSSpecialIssue/Code/HijackerData/Hijack_3percent_betweennesss.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
errorCalc <- function(x){
rpe <- (2*(x-x[1]))/(abs(x)-abs(x[1]))
ifelse(is.nan(rpe),0,rpe)
}
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
head(dataBW)
dataBW <- dataBW[-1,]
dataBW$Measure <- rep("BW",10)
dataBW$Sample <- 1:10
dataBWLong <- gather(dataBW,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
head(dataBWLong)
dataBW <- read.csv(file="./HijackerData/Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
getwd()
dataBW <- read.csv(file="/HijackerData/Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
dataBW <- read.csv(file="~/HijackerData/Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
setwd("./HijackerData")
list.files()
dataBW <- read.csv(file="Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
dataBW <- dataBW[-1,] # delete first row of true values
dataBW$Measure <- rep("BW",10)
dataBW$Sample <- 1:10
dataBWLong <- gather(dataBW,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataCL <- read.csv(file="Hijack_3percent_Closeness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataCL) <- varNames
dataCL$Network<-str_replace_all(dataCL$Network, c(" " = "" ))
dataCL <- dataCL %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
dataCL <- dataCL[-1,] # delete first row of true values
dataCL$Measure <- rep("CL",10)
dataCL$Sample <- 1:10
dataCLLong <- gather(dataCL,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataFB <- read.csv(file="Hijack_3percent_FlowBetween.csv",skip=5,nrows=11,header=FALSE)
colnames(dataFB) <- varNames
dataFB$Network<-str_replace_all(dataFB$Network, c(" " = "" ))
dataFB <- dataFB %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
dataFB <- dataFB[-1,] # delete first row of true values
dataFB$Measure <- rep("FB",10)
dataFB$Sample <- 1:10
dataFBLong <- gather(dataFB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataFT <- read.csv(file="Hijack_3percent_Flowthrough.csv",skip=5,nrows=11,header=FALSE)
colnames(dataFT) <- varNames
dataFT$Network<-str_replace_all(dataFT$Network, c(" " = "" ))
dataFT <- dataFT %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
dataFT <- dataFT[-1,] # delete first row of true values
dataFT$Measure <- rep("FT",10)
dataFT$Sample <- 1:10
dataFTLong <- gather(dataFT,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataSB <- read.csv(file="Hijack_3percent_StableBetween.csv",skip=5,nrows=11,header=FALSE)
colnames(dataSB) <- varNames
dataSB$Network<-str_replace_all(dataSB$Network, c(" " = "" ))
dataSB <- dataSB %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
dataSB <- dataSB[-1,] # delete first row of true values
dataSB$Measure <- rep("SB",10)
dataSB$Sample <- 1:10
dataSBLong <- gather(dataSB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
hijackPert3 <- bind_rows(dataFTLong, dataFBLong, dataCLLong, dataBWLong, dataSBLong)
hijackPert3$Measure <- ordered(hijackPert3$Measure, levels=c("FT","FB","CL","BW","SB"))
table(hijackPert3$Sample)
table(hijackPert3$Centrality)
table(hijackPert3$Measure)
str(hijackPert3)
summary(hijackPert3$Centrality)
head(hijackPert3)
head(dataBW)
?if
?
??if
?
help(if)
help(if)
help(if)
help(ifelse)
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)-abs(x[1])))
dataBW <- read.csv(file="Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)-abs(x[1])))
head(dataBW)
dataBW <- read.csv(file="Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
head(dataBW)
x <- dataBW$Cent2
x - x[1]
numer <- x - x[1]
denom <- abs(x) + abs(x[1])
2*numer/denom
dataBW <- read.csv(file="Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
head(dataBW)
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs = if(is.nan(x)) x = 0)
head(dataBW)
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs = ifelse(is.nan(x), 0, x))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs = function(x) ifelse(is.nan(x), 0, x))
head(dataBW)
2*numer/denom
dataBW$Cent2
errorCalc <- function(x){
rpe <- (2*(x-x[1]))/(abs(x)+abs(x[1]))
ifelse(is.nan(rpe),0,x)
}
dataCL <- read.csv(file="Hijack_3percent_Closeness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataCL) <- varNames
dataCL$Network<-str_replace_all(dataCL$Network, c(" " = "" ))
head(dataCL[,2:8])
dataCL <- dataCL %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
head(dataCL[,2:8])
errorCalc <- function(x){
rpe <- (2*(x-x[1]))/(abs(x)+abs(x[1]))
ifelse(is.nan(rpe),0,rpe)
}
dataCL <- dataCL %>% mutate_at(vars(matches("Cent")), .funs =function(x) errorCalc(x))
head(dataCL[,2:8])
dataBW <- read.csv(file="Hijack_3percent_betweenness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataBW) <- varNames
dataBW$Network<-str_replace_all(dataBW$Network, c(" " = "" ))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
dataBW <- dataBW %>% mutate_at(vars(matches("Cent")), .funs = function(x) ifelse(is.nan(x), 0, x))
dataBW <- dataBW[-1,] # delete first row of true values
dataBW$Measure <- rep("BW",10)
dataBW$Sample <- 1:10
dataBWLong <- gather(dataBW,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataCL <- read.csv(file="Hijack_3percent_Closeness.csv",skip=5,nrows=11,header=FALSE)
colnames(dataCL) <- varNames
dataCL <- dataCL %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
dataCL <- dataCL %>% mutate_at(vars(matches("Cent")), .funs = function(x) ifelse(is.nan(x), 0, x))
dataCL <- dataCL[-1,] # delete first row of true values
dataCL$Measure <- rep("CL",10)
dataCL$Sample <- 1:10
dataCLLong <- gather(dataCL,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataFB <- read.csv(file="Hijack_3percent_FlowBetween.csv",skip=5,nrows=11,header=FALSE)
colnames(dataFB) <- varNames
dataFB$Network<-str_replace_all(dataFB$Network, c(" " = "" ))
dataFB <- dataFB %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
dataFB <- dataFB %>% mutate_at(vars(matches("Cent")), .funs = function(x) ifelse(is.nan(x), 0, x))
dataFB <- dataFB[-1,] # delete first row of true values
dataFB$Measure <- rep("FB",10)
dataFB$Sample <- 1:10
dataFBLong <- gather(dataFB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataFT <- read.csv(file="Hijack_3percent_Flowthrough.csv",skip=5,nrows=11,header=FALSE)
colnames(dataFT) <- varNames
dataFT$Network<-str_replace_all(dataFT$Network, c(" " = "" ))
dataFT <- dataFT %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
dataFT <- dataFT %>% mutate_at(vars(matches("Cent")), .funs = function(x) ifelse(is.nan(x), 0, x))
dataFT <- dataFT[-1,] # delete first row of true values
dataFT$Measure <- rep("FT",10)
dataFT$Sample <- 1:10
dataFTLong <- gather(dataFT,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
dataSB <- read.csv(file="Hijack_3percent_StableBetween.csv",skip=5,nrows=11,header=FALSE)
colnames(dataSB) <- varNames
dataSB$Network<-str_replace_all(dataSB$Network, c(" " = "" ))
dataSB <- dataSB %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
dataSB <- dataSB %>% mutate_at(vars(matches("Cent")), .funs = function(x) ifelse(is.nan(x), 0, x))
dataSB <- dataSB[-1,] # delete first row of true values
dataSB$Measure <- rep("SB",10)
dataSB$Sample <- 1:10
dataSBLong <- gather(dataSB,key="CentLevel",value="Centrality",starts_with("Cent"), factor_key=TRUE) %>% select(Sample,Network,Measure,Centrality,CentLevel)
hijackPert3 <- bind_rows(dataFTLong, dataFBLong, dataCLLong, dataBWLong, dataSBLong)
hijackPert3$Measure <- ordered(hijackPert3$Measure, levels=c("FT","FB","CL","BW","SB"))
summary(hijackPert3$Centrality)
hijackPert3All <- bind_rows(hijackPert3Lev1,hijackPert3Lev2,hijackPert3Lev3,hijackPert3Lev4,hijackPert3Lev5, .id="CentLevel")
hijackPert3Lev1 <- subset(hijackPert3, CentLevel == "Cent1", select = c("Sample","Network","Measure","Centrality"))
npmLev1 <- NPMTest(Centrality~Measure,data=hijackPert3Lev1)
hijackPert3Lev2 <- subset(hijackPert3, CentLevel == "Cent2", select = c("Sample","Network","Measure","Centrality"))
npmLev2 <- NPMTest(Centrality~Measure,data=hijackPert3Lev2)
hijackPert3Lev3 <- subset(hijackPert3, CentLevel == "Cent3", select = c("Sample","Network","Measure","Centrality"))
npmLev3 <- NPMTest(Centrality~Measure,data=hijackPert3Lev3)
hijackPert3Lev4 <- subset(hijackPert3, CentLevel == "Cent4", select = c("Sample","Network","Measure","Centrality"))
npmLev4 <- NPMTest(Centrality~Measure,data=hijackPert3Lev4)
hijackPert3Lev5 <- subset(hijackPert3, CentLevel == "Cent5", select = c("Sample","Network","Measure","Centrality"))
npmLev5 <- NPMTest(Centrality~Measure,data=hijackPert3Lev5)
summary(npmLev1)
summary(npmLev2)
summary(npmLev3)
summary(npmLev4)
summary(npmLev5)
npmLev1
hijackPert3All <- bind_rows(hijackPert3Lev1,hijackPert3Lev2,hijackPert3Lev3,hijackPert3Lev4,hijackPert3Lev5, .id="CentLevel")
pdf(file="hijackPert3Lines.pdf")
ggplot(hijackPert3All, aes(x=Sample,y=Centrality,colour=Measure)) + geom_line() + facet_grid(CentLevel~.)
dev.off()
dataBW$Cent5
dataCL$Cent5
dataFB$Cent5
dataFT$Cent5
dataSB$Cent5
length(is.infinite(dataSBLong$Centrality))
length(is.infinite(dataBWLong$Centrality))
length(is.infinite(dataCLLong$Centrality))
summary(dataCLLong$Centrality)
summary(dataSBLong$Centrality)
summary(dataFBLong$Centrality)
summary(dataFTLong$Centrality)
summary(dataBWLong$Centrality)
which(is.infinite(dataSBLong$Centrality))
test <- apply(dataSB,2,function(x) is.infinite(x))
summary(test)
dataSB <- read.csv(file="Hijack_3percent_StableBetween.csv",skip=5,nrows=11,header=FALSE)
colnames(dataSB) <- varNames
dataSB$Network<-str_replace_all(dataSB$Network, c(" " = "" ))
head(dataSB)
dataSB <- dataSB %>% mutate_at(vars(matches("Cent")), .funs =function(x) (2*(x-x[1]))/(abs(x)+abs(x[1])))
head(dataSB)
dataSB <- read.csv(file="Hijack_3percent_StableBetween.csv",skip=5,nrows=11,header=FALSE)
colnames(dataSB) <- varNames
test <- dataSB %>% mutate_at(vars(matches("Cent")), .funs = function(x) is.infinite(x))
length(test[test == TRUE])
sumDataPert3 <- hijackPert3All %>% group_by(CentLevel,Measure) %>% summarise(Mean=mean(Centrality),SD=sd(Centrality),Median=median(Centrality),MAD=mad(Centrality))
pdf(file="hijackPert3Summary.pdf")
ggplot(sumDataPert3,aes(x=CentLevel,y=Mean,colour=Measure)) + geom_point()
dev.off()
xtable(sumDataPert3)
getwd()
ggplot(hijackPert6All, aes(x=Sample,y=Centrality,colour=Measure)) +
theme_bw() + geom_line() + scale_colour_manual(values=mypal) +
facet_grid(CentLevel~.)
help("geom_line")
help("geom_line")
knitr::opts_chunk$set(echo = TRUE)
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
install.packages("PMCMRplus")
library(PMCMRplus) # For Nashimoto-Wright NPM-test for ordered means of non-normal
install.packages("PMCMRplus")
library(PMCMRplus) # For Nashimoto-Wright NPM-test for ordered means of non-normal
getwd()
