psych::alpha(dimE,keys=(1:9)[(keyE==-1)])
library(psych)
qtype <- c("E","A","C","N","O", "E","A","C","N","O",
"E","A","C","N","O", "E","A","C","N","O",
"E","A","C","N","O", "E","A","C","N","O",
"E","A","C","N","O", "E","A","C","N","O",
"O","A","C","O")
valence <- c(1,-1,1,1,1,  -1,1,-1,-1,1,
1,-1,1,1,1,  1,1,-1,1,1,
-1,1,-1,-1,1,  1,1,-1,-1,-1,
1,-1,1,-1,-1,  1,1,-1,-1,-1,
1,-1,1,-1)
##let's use select with the column numbers:
dat.raw <- read.csv("bigfive-full.csv")[,4:47]
#dat.raw[is.na(dat.raw)] <- 3
dat.raw[1:5,]
dat2 <- dat.raw
library(dplyr)
dimE <- dplyr::select(dat2,(1:44)[qtype=="E"])
dimA <- dplyr::select(dat2,(1:44)[qtype=="A"])
dimC <- dplyr::select(dat2,(1:44)[qtype=="C"])
dimN <- dplyr::select(dat2,(1:44)[qtype=="N"])
dimO <- dplyr::select(dat2,(1:44)[qtype=="O"])
keyE <- valence[qtype=="E"]
keyA <- valence[qtype=="A"]
keyC <- valence[qtype=="C"]
keyN <- valence[qtype=="N"]
keyO <- valence[qtype=="O"]
psych::alpha(dimE,keys=(1:9)[(keyE==-1)])
dimE
psych::alpha(dimE,keys=(1:8)[(keyE==-1)])
keyE
?alpha
psych::alpha(dimE)#
keys=(1:8)[(keyE==-1)
]
keys
keyE
dimE
psych::alpha(dimE,keys=keyE)
psych::alpha(dimA,keys=(1:9)[(keyA==-1)])
glb(dimE,key=keyE)
psych::alpha(dimE,keys=keyE)
glb(dimE,key=keyE)
# This no longer works:
#psych::alpha(dimA,keys=(1:9)[(keyA==-1)])
psych::alpha(dimA,keys=keyA)
glb(dimA,key=keyA)
psych::alpha(dimN,keys=keyN)
glb(dimN,key=keyN)
psych::alpha(dimO,keys=keyO)
glb(dimO,key=keyO)
psych::alpha(dimE,keys=keyE,title="Extraversion")
glb(dimE,key=keyE)
# This no longer works:
#psych::alpha(dimA,keys=(1:9)[(keyA==-1)])
psych::alpha(dimA,keys=keyA)
glb(dimA,key=keyA)
psych::alpha(dimN,keys=keyN)
glb(dimN,key=keyN)
psych::alpha(dimO,keys=keyO)
glb(dimO,key=keyO)
psych::alpha(dimE,keys=keyE,title="Extraversion")
glb(dimE,key=keyE)
# This no longer works:
#psych::alpha(dimA,keys=(1:9)[(keyA==-1)])
psych::alpha(dimA,keys=keyA,title="Agreeableness")
glb(dimA,key=keyA)
psych::alpha(dimN,keys=keyN,title="Neuroticism")
glb(dimN,key=keyN)
psych::alpha(dimO,keys=keyO,title="Openness to experience")
glb(dimO,key=keyO)
psych::alpha(dimC)
##with keys specified a priori
psych::alpha(dimC,keys=keyC)
psych::alpha(dimC,keys=c("Q8","Q18","Q23","Q43"))
library(ggplot2)
##without keys
psych::alpha(dimC)
##with keys specified a priori
psych::alpha(dimC,keys=keyC)
psych::alpha(dimC,keys=c("Q8","Q18","Q23","Q43"))
## with keys discovered using first PCA dimension.
psych::alpha(dimC, check.keys=T)
glb(dimC,key=keyC)
cc <- cor(dimC,use="pairwise.complete")
e <- eigen(cc)
tmp <- data.frame(i=1:9,eigenvalue=e$values)
ggplot(tmp,aes(x=i,y=eigenvalue))+geom_line()+geom_point(aes(x=i,y=eigenvalue)) +
geom_hline( yintercept=1) + ylim(0,4) +
scale_x_continuous(breaks=1:9)
tmp <- data.frame(i=1:9,vector=e$vectors[,1])
ggplot(tmp,aes(x=i,y=vector))+geom_bar(stat="identity")
keyC <- c(1,-1,1,-1,-1,1,1,1,-1)
psych::alpha(dimC,keys=(1:9)[keyC==-1])
keyC <- c(1,-1,1,-1,-1,1,1,1,-1)
psych::alpha(dimC,keys=keyC)
glb(dimC,key=keyC)
colMeans(dimC,na.rm=T)
colSums(is.na(dimC))
cc
round(abs(cc)>.35,2)
heatmap(cc)
heatmap(cc)
round(abs(cc)>.35,2)
small <- dplyr::select(dimC,Q8,Q18,Q23,Q33,Q43)
psych::alpha(small)
psych::alpha(small,check.keys=T)
psych::alpha(small,keys=keyC[c(2,4,5,7,9)])
round(cc,3)
heatmap(cc)
round(abs(cc)>.35,2)
round(cc,3)
heatmap(cc^2)
round(abs(cc)>.35,2)
heatmap(abs(cc))
cor(dplyr::select(dimC,Q43,Q8,Q23,Q18),
dplyr::select(dimC,Q28,Q38,Q33,Q3,Q13))
cor(dplyr::select(dimC,Q43,Q8,Q23,Q18),
dplyr::select(dimC,Q28,Q38,Q33,Q3,Q13),use="pairwise.complete")
small <- dplyr::select(dimC,Q8,Q18,Q23,Q33,Q43)
psych::alpha(small)
psych::alpha(small,check.keys=T)
psych::alpha(small,keys=keyC[c(2,4,5,7,9)])
print(tmp0)
print(tmp)
dimC
tmp$names <- colnames(dimC)
print(tmp)
tr <- read.csv("TaskRatings.csv")
data <- tr
#r1 to r2
cat("1 versus 2:\n")
round(diag(cor(data[,3:10],data[,11:18])),3)
mean(round(diag(cor(data[,3:10],data[,11:18])),3))
#r1 to r3
cat("1 versus 3:\n")
round(diag(cor(data[,3:10],data[,19:26])),3)
mean(round(diag(cor(data[,3:10],data[,19:26])),3))
#r2 to r3
cat("2 versus 3:\n")
round(diag(cor(data[,11:18],data[,19:26])),3)
mean(round(diag(cor(data[,11:18],data[,19:26])),3))
tr
library(GGally)
data <- tr
i <- 2;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 3;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 4;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 5;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 6;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 7;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 8;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 9;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
library(ICC)
data <- tr
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n------------------------\n")
print( round(cor(data[,c(i+1,i+9,i+17)]),3))
ggpairs(data[,c(i+1,i+9,i+17)])
merged <- c(data[,i+1],data[,i+9],data[,i+17])
subject <- as.factor(rep(1:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(task,merged,data=frame.1))
}
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
frame.1 <- data.frame(merged,subject)
print(ICCest(subject,merged,data=frame.1))
}
frame.1
task
irr:icc(merged)
library(irr)
icc(merged)
icc(t(merged))
merged
frame.1[1:5,]
library(ICC)
data <- tr
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n------------------------\n")
print( round(cor(data[,c(i+1,i+9,i+17)]),3))
ggpairs(data[,c(i+1,i+9,i+17)])
merged <- c(data[,i+1],data[,i+9],data[,i+17])
subject <- as.factor(rep(1:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(task,merged,data=frame.1))
}
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
frame.1 <- data.frame(merged,subject)
print(ICCest(subject,merged,data=frame.1))
}
frame.1[1:5,]
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
frame.1 <- data.frame(merged,subject)
print(ICCest(subject,merged,data=frame.1))
}
?ICCest
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
frame.1 <- data.frame(merged,subject)
print(ICCest(subject,merged,data=frame.1))
}
subject
library(ICC)
data <- tr
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n------------------------\n")
print( round(cor(data[,c(i+1,i+9,i+17)]),3))
ggpairs(data[,c(i+1,i+9,i+17)])
merged <- c(data[,i+1],data[,i+9],data[,i+17])
subject <- as.factor(rep(1:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(subject,merged,data=frame.1))
}
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
frame.1 <- data.frame(merged,subject)
print(ICCest(subject,merged,data=frame.1))
}
frame.1
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(task,merged,data=frame.1))
}
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+1],data[,i+9],data[,i+17])
subject <- as.factor(rep(1:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(task,merged,data=frame.1))
}
library(psych)
cohen.kappa(data[,c(3,11)])
for(i in 3:10)
{
cat("===========================================\n")
cat("Kappa for",names(data)[i],"\n")
print(cohen.kappa(data[,c(i,i+8)]))
}
cc1 <- cor(t(tr[,3:26]))
e <- eigen(cc1)
cc1 <- cor(t(tr[,3:26]))
e <- eigen(cc1)
plot(e$values)
tr$Task[order(-e1$vectors[,1])][1:10]
tr$Task[order(e1$vectors[,1])][1:10]
tr$Task[order(-e1$vectors[,1])][1:10]
tr$Task[order(e1$vectors[,1])][1:10]
tr$Task[order(-e1$vectors[,2])][1:10]
tr$Task[order(e1$vectors[,2])][1:10]
tr$Task[order(-e1$vectors[,3])][1:10]
tr$Task[order(e1$vectors[,3])][1:10]
plot(e$vectors[,1],e$vectors[,2])
text(e$vectors[,1],e$vectors[,2],tr$Task)
plot(e$vectors[,1],e$vectors[,2])
text(e$vectors[,1],e$vectors[,2],tr$Task)
cc2 <- cor(tr[,3:26])
e2 <- eigen(cc2)
cc2 <- cor(tr[,3:26])
e2 <- eigen(cc2)
plot(e2$values)
tr[1:5,]
rownames(tr)
colnames(tr)
colnames(tr)[3:26]
plot(e2$vectors[,1],e2$vectors[,2])
text(e2$vectors[,1],e2$vectors[,2], colnames(tr)[3:26])
data(ChickWeight)
ICCest(Chick, weight, data = ChickWeight, CI.type = "S")
library(psych)
qtype <- c("E","A","C","N","O", "E","A","C","N","O",
"E","A","C","N","O", "E","A","C","N","O",
"E","A","C","N","O", "E","A","C","N","O",
"E","A","C","N","O", "E","A","C","N","O",
"O","A","C","O")
valence <- c(1,-1,1,1,1,  -1,1,-1,-1,1,
1,-1,1,1,1,  1,1,-1,1,1,
-1,1,-1,-1,1,  1,1,-1,-1,-1,
1,-1,1,-1,-1,  1,1,-1,-1,-1,
1,-1,1,-1)
##let's use select with the column numbers:
dat.raw <- read.csv("bigfive-full.csv")[,4:47]
#dat.raw[is.na(dat.raw)] <- 3
dat.raw[1:5,]
dat2 <- dat.raw
library(dplyr)
dimE <- dplyr::select(dat2,(1:44)[qtype=="E"])
dimA <- dplyr::select(dat2,(1:44)[qtype=="A"])
dimC <- dplyr::select(dat2,(1:44)[qtype=="C"])
dimN <- dplyr::select(dat2,(1:44)[qtype=="N"])
dimO <- dplyr::select(dat2,(1:44)[qtype=="O"])
keyE <- valence[qtype=="E"]
keyA <- valence[qtype=="A"]
keyC <- valence[qtype=="C"]
keyN <- valence[qtype=="N"]
keyO <- valence[qtype=="O"]
psych::alpha(dimE,keys=keyE,title="Extraversion")
glb(dimE,key=keyE)
# This does not work:
#psych::alpha(dimA,keys=(1:9)[(keyA==-1)])
psych::alpha(dimA,keys=keyA,title="Agreeableness")
glb(dimA,key=keyA)
psych::alpha(dimN,keys=keyN,title="Neuroticism")
glb(dimN,key=keyN)
psych::alpha(dimO,keys=keyO,title="Openness to experience")
glb(dimO,key=keyO)
psych::alpha(dimE,keys=keyE,title="Extraversion")
glb(dimE,key=keyE)
# This does not work:
#psych::alpha(dimA,keys=(1:9)[(keyA==-1)])
psych::alpha(dimA,keys=keyA,title="Agreeableness")
glb(dimA,key=keyA)
psych::alpha(dimN,keys=keyN,title="Neuroticism")
glb(dimN,key=keyN)
psych::alpha(dimO,keys=keyO,title="Openness to experience")
glb(dimO,key=keyO)
psych::alpha(dimA,keys=keyA,title="Agreeableness")
glb(dimA,key=keyA)
library(ggplot2)
##without keys
psych::alpha(dimC)
##with keys specified a priori
psych::alpha(dimC,keys=keyC)
psych::alpha(dimC,keys=c("Q8","Q18","Q23","Q43"))
## with keys discovered using first PCA dimension.
psych::alpha(dimC, check.keys=T)
glb(dimC,key=keyC)
cc <- cor(dimC,use="pairwise.complete")
e <- eigen(cc)
tmp <- data.frame(i=1:9,eigenvalue=e$values)
ggplot(tmp,aes(x=i,y=eigenvalue))+geom_line()+geom_point(aes(x=i,y=eigenvalue)) +
geom_hline( yintercept=1) + ylim(0,4) +
scale_x_continuous(breaks=1:9)
tmp <- data.frame(i=1:9,vector=e$vectors[,1])
tmp$names <- colnames(dimC)
print(tmp)
ggplot(tmp,aes(x=i,y=vector))+geom_bar(stat="identity")
psych::alpha(dimC)
psych::alpha(dimC, check.keys=T)
psych::alpha(dimC,keys=c("Q8","Q18","Q23","Q43"))
glb(dimC,key=keyC)
cc <- cor(dimC,use="pairwise.complete")
e <- eigen(cc)
tmp <- data.frame(i=1:9,eigenvalue=e$values)
ggplot(tmp,aes(x=i,y=eigenvalue))+geom_line()+geom_point(aes(x=i,y=eigenvalue)) +
geom_hline( yintercept=1) + ylim(0,4) +
scale_x_continuous(breaks=1:9)
tmp <- data.frame(i=1:9,vector=e$vectors[,1])
tmp$names <- colnames(dimC)
print(tmp)
ggplot(tmp,aes(x=i,y=vector))+geom_bar(stat="identity")
keyC <- c(1,-1,1,-1,-1,1,1,1,-1)
psych::alpha(dimC,keys=keyC)
glb(dimC,key=keyC)
colMeans(dimC,na.rm=T)
colSums(is.na(dimC))
psych::alpha(dimC,keys=c("Q8","Q18","Q23","Q43"))
round(cc,3)
heatmap(abs(cc))
round(abs(cc)>.35,2)
small <- dplyr::select(dimC,Q8,Q18,Q23,Q33,Q43)
psych::alpha(small)
psych::alpha(small,check.keys=T)
psych::alpha(small,keys=keyC[c(2,4,5,7,9)])
small
cor(small)
v.small <- eigen(cor(small,use="pairwise.complete"))$vectors[,1]
v.small
v.small <- eigen(cor(t(small),use="pairwise.complete"))$vectors
tr <- read.csv("TaskRatings.csv")
data <- tr
#r1 to r2
cat("1 versus 2:\n")
round(diag(cor(data[,3:10],data[,11:18])),3)
mean(round(diag(cor(data[,3:10],data[,11:18])),3))
#r1 to r3
cat("1 versus 3:\n")
round(diag(cor(data[,3:10],data[,19:26])),3)
mean(round(diag(cor(data[,3:10],data[,19:26])),3))
#r2 to r3
cat("2 versus 3:\n")
round(diag(cor(data[,11:18],data[,19:26])),3)
mean(round(diag(cor(data[,11:18],data[,19:26])),3))
tr[,3:11]
library(GGally)
data <- tr
i <- 2;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 3;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 4;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 5;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 6;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 7;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 8;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
i <- 9;tmp <- as.data.frame(data[,c(i+1,i+9,i+17)]) + (runif(3*nrow(data))-.5)*.2
ggpairs(tmp)
library(ICC)
data <- tr
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n------------------------\n")
print( round(cor(data[,c(i+1,i+9,i+17)]),3))
ggpairs(data[,c(i+1,i+9,i+17)])
merged <- c(data[,i+1],data[,i+9],data[,i+17])
subject <- as.factor(rep(1:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(subject,merged,data=frame.1))
}
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+9],data[,i+17])
subject <- as.factor(rep(2:3,each=nrow(data)))
frame.1 <- data.frame(merged,subject)
print(ICCest(subject,merged,data=frame.1))
}
for(i in 2:8)
{
cat("-------------------------\n",colnames(data)[i+1],"\n")
merged <- c(data[,i+1],data[,i+9],data[,i+17])
subject <- as.factor(rep(1:3,each=nrow(data)))
task <- as.factor(rep(1:nrow(data),3))
frame.1 <- data.frame(merged,subject,task)
print(ICCest(task,merged,data=frame.1))
}
library(psych)
cohen.kappa(data[,c(3,11)])
for(i in 3:10)
{
cat("===========================================\n")
cat("Kappa for",names(data)[i],"\n")
print(cohen.kappa(data[,c(i,i+8)]))
}
cc1 <- cor(t(tr[,3:26]))
e <- eigen(cc1)
plot(e$values)
tr$Task[order(-e1$vectors[,1])][1:10]
tr$Task[order(e1$vectors[,1])][1:10]
tr$Task[order(-e1$vectors[,2])][1:10]
tr$Task[order(e1$vectors[,2])][1:10]
tr$Task[order(-e1$vectors[,3])][1:10]
tr$Task[order(e1$vectors[,3])][1:10]
e1$vectors[,1]
plot(e1$vectors[,1])
tr$Task[28]
tr$Task[order(-e1$vectors[,1])][1:10]
tr$Task[order(e1$vectors[,1])][1:10]
plot(e1$vectors[,2])
tr$Task[order(-e1$vectors[,2])][1:10]
tr$Task[order(e1$vectors[,2])][1:10]
tr$Task[order(-e1$vectors[,3])][1:10]
tr$Task[order(e1$vectors[,3])][1:10]
plot(e$vectors[,1],e$vectors[,2])
text(e$vectors[,1],e$vectors[,2],tr$Task)
plot(e2$vectors[,1],e2$vectors[,2])
text(e2$vectors[,1],e2$vectors[,2], colnames(tr)[3:26])
cc2 <- cor(tr[,3:26])
e2 <- eigen(cc2)
plot(e2$values)
plot(e2$vectors[,1],e2$vectors[,2])
text(e2$vectors[,1],e2$vectors[,2], colnames(tr)[3:26])
plot(e2$vectors[,1],e2$vectors[,2],cex=.1)
text(e2$vectors[,1],e2$vectors[,2], colnames(tr)[3:26])
plot(e2$vectors[,1],e2$vectors[,2],cex=.1)
text(e2$vectors[,1],e2$vectors[,2], colnames(tr)[3:26])