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stockfunctions.R
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stockfunctions.R
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# Setup the environment
apk.r.setup <- function() {
# pckgs <- c("lme4", "psych", "quantreg", "xtable", "reshape2", "arm", "metafor", "MCMCpack", "multilevel", "sqldf", "RMySQL", "lavaan", "Hmisc", "sna", "tseriesChaos", "fNonlinear", "timeSeries", "tm", "pscl", "xlsx", "foreign", "nFactors", "pwr", "amen", "TripleR", "ggplot2", "gplots", "lattice", "crqa", "matrixStats", "data.table", "AER", "sas7bdat", "clusterSEs", "RCurl", "sandwich", "RSA", "lavaan.survey", "survey", "mclust", "tidyLPA", "stargazer", "nnet")
pckgs = c("lme4", "reshame2", "multilevel", "lavaan", "Hmisc", "openxlsx", "lattice", "matrixStats", "data.table", "RSA", "lavaan.survey", "survey", "paws")
install.packages(pckgs, dependencies=TRUE, repos="http://cran.wustl.edu")
}
# FUNCTION FOR OUTPUTTING A CORRELATION MATRIX
correlation.matrix <- function(x, diagval=NA){
require(Hmisc)
require(matrixStats)
x <- as.matrix(x)
#j <- as.matrix(j)
R <- rcorr(x)$r
p <- rcorr(x)$P
M <- formatC(round(colMeans(x, na.rm=T),2), digits=2, format="f")
SD <- formatC(round(colSds(x, na.rm=T),2),digits=2, format="f")
## define notations for significance levels; spacing is important.
mystars <- ifelse(p < .01, "", ifelse(p < .05, "", ifelse(p < .10, "", "")))
## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
## build a new matrix that includes the correlations with their apropriate stars
Rnew <- R
Rnew<-matrix(paste(Rnew, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- diagval
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")
## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = FALSE)] <- ""
## Add the mean and the SD
Rnew0<-cbind(M, SD, Rnew)
Rnew1 <- as.data.frame(Rnew0)
## remove last column and return the matrix (which is now a data frame)
#Rnew <- cbind(Rnew[1:length(Rnew)-1])
return(Rnew1)
}
plot.interaction <- function(dv, v1, v2, controls,dfile){
eq<-paste(dv,v1,sep="~")
eq<-paste(eq,v2,sep="*")
if(length(controls) > 0)
{
for(i in 1:length(controls))
{
eq<-paste(eq,controls[i],sep="+")
}
}
arg<-model.frame(as.formula(eq), dfile)
out<-glm(arg)
v1.x<-mean(dfile[,c(v1)], na.rm=TRUE)
v1.sd<-sd(dfile[,c(v1)], na.rm=TRUE)
v1.lo<-v1.x-v1.sd
v1.hi<-v1.x+v1.sd
v2.x<-mean(dfile[,c(v2)], na.rm=TRUE)
v2.sd<-sd(dfile[,c(v2)], na.rm=TRUE)
v2.lo<-v2.x-v2.sd
v2.hi<-v2.x+v2.sd
lolo<-out$coefficients[1]+out$coefficients[2]*v1.lo+out$coefficients[3]*v2.lo+out$coefficients[length(out$coefficients)]*v1.lo*v2.lo
lohi<-out$coefficients[1]+out$coefficients[2]*v1.lo+out$coefficients[3]*v2.hi+out$coefficients[length(out$coefficients)]*v1.lo*v2.hi
hilo<-out$coefficients[1]+out$coefficients[2]*v1.hi+out$coefficients[3]*v2.lo+out$coefficients[length(out$coefficients)]*v1.hi*v2.lo
hihi<-out$coefficients[1]+out$coefficients[2]*v1.hi+out$coefficients[3]*v2.hi+out$coefficients[length(out$coefficients)]*v1.hi*v2.hi
loline<-c(lolo, lohi)
hiline<-c(hilo, hihi)
#quartz(type="pdf", file=filename)
plot(hiline, type="o",col="red", ylim=c(1,2.5), axes=FALSE, main="", cex.main=.75, xlab="v1", pch=16, ylab="yvar", lty=1, lwd=2)
axis(1, at=1:2, lab=c("Minus 1 SD", "Plus 1 SD"), tck=0, cex.axis=.6)
axis(2, at=c("2.25", "2.75", "3.25"), lab=c("2.25", "2.75", "3.25"), tck=0, cex.axis=.7)
lines(loline, type="o", col="blue", pch=18, lwd=2, lty=2)
legend("bottomright", legend=c("+1 SD v2", "-1 SD v2"), cex=.8,col=c("red", "blue"), pch=c(16,18), lty=c(1,2), bty="n")
#dev.off()
return(out)
}
blau<-function(catvar, groupid, dfile){
require(sqldf)
q0<-paste("SELECT ",groupid,", COUNT(",groupid,") as groupsize FROM dfile WHERE ",catvar," IS NOT NULL GROUP BY ",groupid, sep=" ")
out0<-data.frame(sqldf(q0))
new<-merge(dfile, out0, by=c(groupid))
q1<-paste("SELECT ",groupid,",groupsize, COUNT(", catvar,") AS catsize FROM new GROUP BY ",groupid,", ",catvar, sep=" ")
out1<-data.frame(sqldf(q1))
out1$prop2<-(out1$catsize/out1$groupsize)*(out1$catsize/out1$groupsize)
q2<-paste("SELECT ",groupid,", (1-SUM(prop2)) AS ",catvar,"_blau FROM out1 GROUP BY ",groupid,sep="")
d<-data.frame(sqldf(q2))
return<-d[,c(groupid,paste(catvar,"_blau",sep=""))]
}
regtable<-function(models, rnames)
{
numrows<-length(rnames)
rnames<-data.frame(rnames)
colnames(rnames)<-c("Var")
df<-c("DF",rep("",length(models)*3))
rsq<-c("R2",rep("",length(models)*3))
fstat<-c("F",rep("",length(models)*3))
for(i in 1:length(models))
{
# Get the coefficient information - doing as much info as would be needed in a table.
s<-mapply(summary, models[i], USE.NAMES = TRUE)
coef<-s[4,1]$coefficients
m.b <- round(coef[,1],2)
m.se <- round(coef[,2],2)
m.p <- round(coef[,4],3)
coefs<-data.frame(cbind(row.names(coef), m.b, m.se, m.p))
colnames(coefs)<-c("Var",paste("b",i,sep=""), paste("se",i,sep=""), paste("p", i, sep=""))
# Get model summary information.
index <- (i-1)*3 + 2
df[index]<-paste(s[10,1]$fstatistic[2], s[10,1]$fstatistic[3], sep=",")
rsq[index]<-formatC(round(s[8,1]$r.squared[1],2), digits=2, format="f")
fstat[index]<-formatC(round(s[10,1]$fstatistic[1],2), digits=2, format="f")
fstat[index+2]<-round(pf(s[10,1]$fstatistic[1], s[10,1]$fstatistic[2], s[10,1]$fstatistic[3], lower.tail=FALSE), 3)
if(i == 1)
{
tab<-merge(rnames, coefs, by=c("Var"), all.x=TRUE, sort=FALSE)
} else {
tab<-merge(tab, coefs, by=c("Var"), all.x=TRUE, sort=FALSE)
}
}
# Add the summary statistics for each model
data.frame(tab)
# Now make sure that the order is as desired
tab<-merge(rnames, tab, by=c("Var"), all.x=TRUE, sort=FALSE)
sumstats<-data.frame(rbind(df, rsq, fstat))
colnames(sumstats)<-colnames(tab)
tab<-rbind(tab,sumstats)
return(tab)
}
hlmtable<-function(models, rnames)
{
numrows<-length(rnames)
rnames<-data.frame(rnames)
colnames(rnames)<-c("Var")
df<-c("DF",rep("",length(models)))
rsq<-c("R2",rep("",length(models)))
fstat<-c("F",rep("",length(models)))
for(i in 1:length(models))
{
s<-mapply(summary, models[i], USE.NAMES = TRUE)
coef<-s[4,1]$coefficients
column<-rep("",length(coef[,1]))
name<-rep("",length(coef[,1]))
for(z in 1:length(coef[,1]))
{
name[z]<-rownames(coef)[z]
star <- ifelse(coef[z,4] < .01, "**", ifelse(coef[z,4] < .05, "*", ifelse(coef[z,4] < 0.10, "+","")))
val<-formatC(round(coef[z,1],2),digits=2, format="f")
column[z]<-paste(val,star,sep="")
}
coefs<-data.frame(cbind(name, column))
colnames(coefs)<-c("Var",paste("M",i,sep=""))
df[(i+1)]<-paste(s[10,1]$fstatistic[2], s[10,1]$fstatistic[3], sep=",")
rsq[(i+1)]<-formatC(round(s[8,1]$r.squared[1],2), digits=2, format="f")
fval<-formatC(round(s[10,1]$fstatistic[1],2), digits=2, format="f")
pval<-pf(s[10,1]$fstatistic[1], s[10,1]$fstatistic[2], s[10,1]$fstatistic[3], lower.tail=FALSE)
star <- ifelse(pval < .001, "***", ifelse(pval < .01, "** ", ifelse(pval < .05, "* ", " ")))
fstat[(i+1)]<-paste(fval, star, sep="")
if(i == 1)
{
tab<-merge(coefs, rnames, by=c("Var"), all.y=TRUE, sort=FALSE)
} else {
tab<-merge(tab, coefs, by=c("Var"), all.y=TRUE, sort=FALSE)
}
}
# Add the summary statistics for each model
data.frame(tab)
# Now make sure that the order is as desired
tab<-merge(rnames, tab, by=c("Var"), all.x=TRUE, sort=FALSE)
sumstats<-data.frame(rbind(df, rsq, fstat))
# sumstats<-sumstats[,c(1,rev(2:length(colnames(tab))))]
colnames(sumstats)<-colnames(tab)
tab<-rbind(tab,sumstats)
return(tab)
}
specify_decimal <- function(x, k) format(round(x, k), nsmall=k)
lmer.table <- function(models, rnames) {
require(arm)
rnames <- c(rnames, "Random Intercept", "Random Residual", "AIC", "Deviance")
rnames<-data.frame(rnames, stringsAsFactors=F)
colnames(rnames)<-c("Var")
for(i in 1:length(models)) {
vals <- display(models[[i]])
rbind(summary(models[[i]])$varcor,NA)
int <- round(attr(summary(models[[i]])$varcor[[1]], "stddev"),4)^2
res <- round(attr(summary(models[[i]])$varcor, "sc"),4)^2
aic <- round(vals$AIC,4)
dev <- round(vals$deviance,4)
Var <- names(vals$coef)
b <- round(vals$coef,4)
se <- round(vals$se, 4)
t <- round(b/se, 4)
new.col <- data.frame(Var,b,se, t,stringsAsFactors=F)
new.col <- rbind(new.col, c("Random Intercept", int, NA, NA))
new.col <- rbind(new.col, c("Random Residual", res, NA, NA))
new.col <- rbind(new.col, c("AIC", aic, NA, NA))
new.col <- rbind(new.col, c("Deviance", dev, NA, NA))
colnames(new.col)[2:4] <- c(paste("b",i,sep=""), paste("se",i,sep=""), paste("t",i,sep=""))
if(i == 1 ) {
tab <- merge(rnames, new.col, by=c("Var"), all.x=TRUE, sort=FALSE)
} else {
tab <- merge(tab, new.col, by=c("Var"), all.x=TRUE, sort=FALSE)
}
}
# Order everything
tab<-merge(rnames, tab, by=c("Var"), all.x=TRUE, sort=FALSE)
return(tab)
}
# Output a nice table of results for bayesian models
bayestable <- function(vars) {
p<-matrix(rep(NA,(6*ncol(vars))), nrow=ncol(vars), ncol=6)
for(i in 1:ncol(vars)){
p[i,1] <- mean(vars[,i], na.rm=TRUE)
p[i,2] <- sd(vars[,i], na.rm=TRUE)
p[i,4] <- quantile(vars[,i], .025)
p[i,5] <- quantile(vars[,i], .975)
if(p[i,1] > 0)
{
num <- length(which(vars[,i] < 0))
}
else
{
num <- length(which(vars[,i] > 0))
}
p[i,6] <- num/length(vars[,i])
}
p[,3] <- p[,1] / p[,2]
rownames(p) <- colnames(vars)
colnames(p) <- c("X","SD","t",".025",".975","p")
return(p)
}
# A function for calculating simple slopes given an object output from lme, z is a vector of three points at which you want slopes
# A function for calculating simple slopes given coefficients, standard errors, and the positions in the array of the relevant variables
ss.lin.ols <- function(coefs, ses, x.n, z.n, xz.n,z, zvals) {
b.0 <- coefs[1]
b.1 <- coefs[x.n]
b.2 <- coefs[z.n]
b.3 <- coefs[xz.n]
# z.hi <- mean(z, na.rm=TRUE) + sd(z, na.rm=TRUE)
# z.av <- mean(z, na.rm=TRUE)
# z.lo <- mean(z, na.rm=TRUE) - sd(z, na.rm=TRUE)
z.hi <- zvals[3]
z.av <- zvals[2]
z.lo <- zvals[1]
int.hi <- (b.0 + b.2*z.hi)
int.av <- (b.0 + b.2*z.av)
int.lo <- (b.0 + b.2*z.lo)
ss.hi <- (b.1+b.3*z.hi)
ss.av <- (b.1+b.3*z.av)
ss.lo <- (b.1+b.3*z.lo)
se.hi <- sqrt(ses[x.n,x.n] + 2*z.hi*ses[x.n,xz.n] + (z.hi^2)*ses[xz.n,xz.n])
se.av <- sqrt(ses[x.n,x.n] + 2*z.av*ses[x.n,xz.n] + (z.av^2)*ses[xz.n,xz.n])
se.lo <- sqrt(ses[x.n,x.n] + 2*z.lo*ses[x.n,xz.n] + (z.lo^2)*ses[xz.n,xz.n])
se.int.hi <- sqrt(ses[1,1] + 2*z.hi*ses[1,z.n] + (z.hi^2)*ses[z.n, z.n])
se.int.av <- sqrt(ses[1,1] + 2*z.av*ses[1,z.n] + (z.av^2)*ses[z.n, z.n])
se.int.lo <- sqrt(ses[1,1] + 2*z.lo*ses[1,z.n] + (z.lo^2)*ses[z.n, z.n])
tval.hi <- ss.hi/se.hi
tval.av <- ss.av/se.av
tval.lo <- ss.lo/se.lo
tval.int.hi <- int.hi/se.int.hi
tval.int.av <- int.av/se.int.av
tval.int.lo <- int.lo/se.int.lo
dfs <- length(z) - (length(coefs)-1) - 1
pval.hi <- 2*pt(-abs(tval.hi),df=dfs)
pval.av <- 2*pt(-abs(tval.av),df=dfs)
pval.lo <- 2*pt(-abs(tval.lo),df=dfs)
pval.int.hi <- 2*pt(-abs(tval.int.hi),df=dfs)
pval.int.av <- 2*pt(-abs(tval.int.av),df=dfs)
pval.int.lo <- 2*pt(-abs(tval.int.lo),df=dfs)
ss <- rbind(ss.hi, ss.av, ss.lo)
se <- rbind(se.hi, se.av, se.lo)
tval <- rbind(tval.hi, tval.av, tval.lo)
dfs <- rbind(dfs, dfs, dfs)
pval <- rbind(pval.hi, pval.av, pval.lo)
int <- rbind(int.hi, int.av, int.lo)
se.int <- rbind(se.int.hi, se.int.av, se.int.lo)
tval.int <- rbind(tval.int.hi, tval.int.av, tval.int.lo)
pval.int <- rbind(pval.int.hi, pval.int.av, pval.int.lo)
ret <- cbind(ss, se, tval, dfs, pval, int, se.int, tval.int, pval.int)
row.names(ret) <- c("+1SD Z","Mean Z","-1SD Z")
colnames(ret) <- c("SS","SE","t","df","p", "INT", "INT.SE", "INT.t", "INT.p")
return(ret)
}
ss.lin.rcm <- function(obj, x.n, z.n, xz.n, z) {
coefs <- fixef(obj)
ses <- vcov(obj)
dfs <- length(obj@frame[,1])-length(coefs)
b.1 <- coefs[x.n]
b.2 <- coefs[z.n]
b.3 <- coefs[xz.n]
z.hi <- mean(z, na.rm=TRUE) + sd(z, na.rm=TRUE)
z.av <- mean(z, na.rm=TRUE)
z.lo <- mean(z, na.rm=TRUE) - sd(z, na.rm=TRUE)
ss.hi <- (b.1+b.3*z.hi)
ss.av <- (b.1+b.3*z.av)
ss.lo <- (b.1+b.3*z.lo)
se.hi <- sqrt(ses[x.n,x.n] + 2*z.hi*ses[x.n,xz.n] + (z.hi^2)*ses[xz.n,xz.n])
se.av <- sqrt(ses[x.n,x.n] + 2*z.av*ses[x.n,xz.n] + (z.av^2)*ses[xz.n,xz.n])
se.lo <- sqrt(ses[x.n,x.n] + 2*z.lo*ses[x.n,xz.n] + (z.lo^2)*ses[xz.n,xz.n])
tval.hi <- ss.hi/se.hi
tval.av <- ss.av/se.av
tval.lo <- ss.lo/se.lo
dfs <- dfs
pval.hi <- 2*pt(-abs(tval.hi),df=dfs)
pval.av <- 2*pt(-abs(tval.av),df=dfs)
pval.lo <- 2*pt(-abs(tval.lo),df=dfs)
ss <- rbind(ss.hi, ss.av, ss.lo)
se <- rbind(se.hi, se.av, se.lo)
tval <- rbind(tval.hi, tval.av, tval.lo)
dfs <- rbind(dfs, dfs, dfs)
pval <- rbind(pval.hi, pval.av, pval.lo)
ret <- cbind(ss, se, tval, dfs, pval)
row.names(ret) <- c("+1SD Z","Mean Z","-1SD Z")
colnames(ret) <- c("SS","SE","t","df","p")
return(ret)
}
ss.lin.rcm.bin <- function(obj, x.n, z.n, xz.n) {
coefs <- fixef(obj)
ses <- vcov(obj)
dfs <- length(obj@frame[,1])-length(coefs)
b.0 <- coefs[1]
b.1 <- coefs[x.n]
b.2 <- coefs[z.n]
b.3 <- coefs[xz.n]
z.hi <- 1
z.lo <- 0
int.hi <- (b.0 + b.2*z.hi)
int.lo <- (b.0 + b.2*z.lo)
ss.hi <- (b.1+b.3*z.hi)
ss.lo <- (b.1+b.3*z.lo)
se.hi <- sqrt(ses[x.n,x.n] + 2*z.hi*ses[x.n,xz.n] + (z.hi^2)*ses[xz.n,xz.n])
se.lo <- sqrt(ses[x.n,x.n] + 2*z.lo*ses[x.n,xz.n] + (z.lo^2)*ses[xz.n,xz.n])
se.int.hi <- sqrt(ses[1,1] + 2*z.hi*ses[1,z.n] + (z.hi^2)*ses[z.n, z.n])
se.int.lo <- sqrt(ses[1,1] + 2*z.lo*ses[1,z.n] + (z.lo^2)*ses[z.n, z.n])
tval.hi <- ss.hi/se.hi
tval.lo <- ss.lo/se.lo
tval.int.hi <- int.hi/se.int.hi
tval.int.lo <- int.lo/se.int.lo
dfs <- dfs
pval.hi <- 2*pt(-abs(tval.hi),df=dfs)
pval.lo <- 2*pt(-abs(tval.lo),df=dfs)
pval.int.hi <- 2*pt(-abs(tval.int.hi),df=dfs)
pval.int.lo <- 2*pt(-abs(tval.int.lo),df=dfs)
ss <- rbind(ss.hi, ss.lo)
se <- rbind(se.hi, se.lo)
tval <- rbind(tval.hi, tval.lo)
dfs <- rbind(dfs, dfs)
pval <- rbind(pval.hi,pval.lo)
int <- rbind(int.hi, int.lo)
se.int <- rbind(se.int.hi, se.int.lo)
tval.int <- rbind(tval.int.hi, tval.int.lo)
pval.int <- rbind(pval.int.hi, pval.int.lo)
ret <- cbind(ss, se, tval, dfs, pval, int, se.int, tval.int, pval.int)
row.names(ret) <- c("Z=1","Z=0")
colnames(ret) <- c("SS","SE","t","df","p", "INT", "INT.SE", "INT.t", "INT.p")
return(ret)
}
# Function to return typical scale statistics for group-level constructs
scale.scores <- function(vars, dat) {
count <- 1
for(i in vars) {
if(length(i[[3]]) > 1) {
val = data.frame(rowMeans(dat[,c(i[[3]])], na.rm=T))
colnames(val) = i[[2]]
if(count == 1) {
return.dat <- val
} else {
return.dat <- cbind(return.dat, val)
}
count = count + 1
}
}
dat = cbind(dat, return.dat)
return(dat)
}
scale.stats <- function (vars, groupvar, dat) {
require(psych)
require(multilevel)
# initialize a matrix to hold the results of this
icc1 <- rep(NA, length(vars))
icc1p <- rep(NA, length(vars))
icc2 <- rep(NA, length(vars))
alpha <- rep(NA, length(vars))
alpha_group <- rep(NA, length(vars))
rwgjmed <- rep(NA, length(vars))
rwgjx <- rep(NA, length(vars))
rwgjsd <- rep(NA, length(vars))
rwgjmin <- rep(NA, length(vars))
rwgjmax <- rep(NA, length(vars))
# indiv_n <- rep(NA, length(vars))
# group_n <- rep(NA, length(vars))
# group_size <- rep(NA, length(vars))
name <- rep(NA, length(vars))
count <- 1
for(i in vars) {
name[count] <- i[[1]]
dv <- dat[, c(i[[2]])]
gv <- dat[, c(groupvar)]
items <- dat[,c(i[[3]])]
out <- aov(dv ~ as.factor(gv), data=dat)
icc1[count] <-round(ICC1(out),3)
icc1p[count] <- round(summary(out)[[1]][["Pr(>F)"]], 4)[1]
icc2[count] <-round(ICC2(out),3)
## Get the individual-level n
# indiv_n[count] <- sum(!is.na(dv))
## Get the group-level n
# group_n[count] <- length(unique(gv))
if(length(i[[3]]) <= 1) {
alpha[count] <- NA
alpha_group[count] <- NA
out<-rwg(items,gv, i[[4]])
rwgjmed[count]<-round(median(out$rwg, na.rm=TRUE),3)
rwgjx[count]<-round(mean(out$rwg, na.rm=TRUE),3)
rwgjsd[count]<-round(sd(out$rwg, na.rm=TRUE),3)
rwgjmin[count]<-round(min(out$rwg, na.rm=TRUE),3)
rwgjmax[count]<-round(max(out$rwg, na.rm=TRUE),3)
} else {
# aggregate the items for the group-level alpha
agg <- aggregate(dat[,c(i[[3]])], by=list(dat[,groupvar]), mean, na.rm=TRUE)
colnames(agg) <- c(groupvar,c(i[[3]]))
out <- alpha(agg[,c(i[[3]])], check.keys=FALSE)
alpha_group[count] <- round(out$total['raw_alpha'],3)
# Produce the individual-level alpha
out <- alpha(items, na.rm=TRUE, check.keys=FALSE)
alpha[count] <- round(out$total['raw_alpha'],3)
out<-rwg.j(items,gv, i[[4]])
rwgjmed[count]<-round(median(out$rwg.j, na.rm=TRUE),3)
rwgjx[count]<-round(mean(out$rwg.j, na.rm=TRUE),3)
rwgjsd[count]<-round(sd(out$rwg.j, na.rm=TRUE),3)
rwgjmin[count]<-round(min(out$rwg.j, na.rm=TRUE),3)
rwgjmax[count]<-round(max(out$rwg.j, na.rm=TRUE),3)
}
count <- count + 1
}
v <- data.frame(name, alpha=unlist(alpha), alpha_group=unlist(alpha_group), icc1, icc1p, icc2, rwgjx, rwgjsd, rwgjmed, rwgjmin, rwgjmax, stringsAsFactors=F)
# Clean up the formatting of this return table
return(v)
}
# This function takes the construct listing and generates two tables - one containing fit indices, the second containing standardized loadings
scale.cfa <- function(vars,dat) {
require(lavaan)
count <- 1
for(construct in vars) {
itemlist <- paste0(construct$items, collapse=" + ")
model <- paste('f1 =~ ', itemlist, sep="")
mod <- cfa(model, data=dat)
mod.fit <- fitMeasures(mod)
mod.est <- standardizedSolution(mod)
fit.ind <- c(round(mod.fit[c(3,4,5,9,10,19,20,23,29)],3))
ind.names <- mod.est[1:length(construct$items),3]
std.loads <- round(mod.est[1:length(construct$items),4],3)
var.name <- c(construct$name)
lds <- cbind(rep(var.name), length(ind.names), ind.names, std.loads)
if(count == 1) {
fit.out <- c(var.name, fit.ind)
load.out <- lds
} else {
fit.out <- rbind(fit.out, c(var.name, fit.ind))
load.out <- rbind(load.out, lds)
}
count <- count + 1
}
row.names(fit.out) <- 1:length(fit.out[,1])
fit.out <- data.frame(fit.out)
load.out <- data.frame(load.out)
return(list(fit.out, load.out))
}
# Aggregation Function
ak_aggregate <- function(dat, constructs, groupvars, stdev=FALSE, items=FALSE) {
require(multilevel)
require(sqldf)
aggvars <- rep(NA, length(constructs))
for(i in 1:length(constructs)) {
aggvars[i] <- constructs[[i]]$var
}
if(items) {
for(i in 1:length(constructs)) {
aggvars <- c(aggvars, constructs[[i]]$items)
}
}
group_list <- vector("list", length(groupvars))
for(i in 1:length(groupvars)) {
group_list[[i]] <- dat[,groupvars[i]]
}
q <- paste("SELECT ",paste(groupvars, collapse=", "), ", COUNT(", groupvars[length(groupvars)],") AS groupsize FROM dat GROUP BY ", paste(groupvars,collapse=","))
n <- sqldf(q)
agg.x <- aggregate(dat[,aggvars], group_list, mean, na.rm=TRUE)
colnames(agg.x) <- c(groupvars, paste(aggvars, "_x", sep=""))
if(stdev) {
agg.sd <- aggregate(dat[,aggvars], group_list, sd, na.rm=TRUE)
colnames(agg.sd) <- c(groupvars, paste(aggvars, "_sd", sep=""))
agg.dat <- merge(agg.x, agg.sd, by=groupvars)
} else {
agg.dat <- agg.x
}
agg.dat <- merge(agg.dat, n, by=groupvars)
return(agg.dat)
}
# Function to ge ta correlation lookup table
cor.table <- function(d) {
varlist <- rep(NA, length(colnames(d)))
count <- 1
for(i in 1:length(colnames(d))) {
if(is.numeric(d[,i]) || is.integer(d[,i])) {
varlist[count] <- colnames(d)[i]
count <- count + 1
}
}
cvars <- varlist[!is.na(varlist)]
size <- length(cvars)*length(cvars)
xvar <- rep(NA, size)
yvar <- rep(NA, size)
r <- rep(NA, size)
p <- rep(NA, size)
nobs <- rep(NA, size)
count <- 1
for(x in 1:length(cvars)) {
xname <- cvars[x]
for(y in 1:length(cvars)) {
yname <- cvars[y]
xvar[count] <- xname
yvar[count] <- yname
nobs[count] = nrow(d[!is.na(d[,xname]) & !is.na(d[,yname]), ])
## See if there are enough observations ##
if(nobs[count] >= 2) {
cout <- cor.test(d[,xname], d[,yname], na.rm=TRUE)
r[count] <- cout$estimate
p[count] <- cout$p.value
} else {
r[count] <- NA
p[count] <- NA
}
count <- count + 1
}
}
ctab <- data.frame(cbind(xvar, yvar, r, p, nobs), stringsAsFactors=FALSE)
ctab[,3] <- round(as.numeric(ctab[,3]),3)
ctab[,4] <- round(as.numeric(ctab[,4]),3)
ctab[,5] <- as.numeric(ctab[,5])
return(ctab[!is.na(ctab$p) & ctab$xvar != ctab$yvar, ])
}
# function for r-squared for lmer
# R-squared information
r2.corr.lmer <- function(lmer.object) {
summary(lm(attr(lmer.object, "y") ~ fitted (lmer.object) ))$r.squared
}
# R-squared information
Rsq <- function(reml.mod) {
## Based on
## N. J. D. Nagelkerke. A note on a general definition
## of the coefficient of determination. Biometrika, 78:691–692, 1991.
ml.mod <- update(reml.mod, REML=FALSE)
l.B <- logLik(ml.mod)
l.0 <- logLik( lm(ml.mod@y ~ 1) )
Rsq <- 1 - exp( - ( 2/length(ml.mod@y) ) * (l.B - l.0) )
Rsq[1]
}
##################################################################
# This function conducts the Dawson & Richter probing of a three-way interaction #
# Input: An output object either generated via lm or lmer, indication of the positioning in the variable order of the different components #
# Output: A table of slope differences and test statistics
##################################################################
three.way <- function(obj, x,w,z,xw,xz,wz,xwz,w.vals, z.vals) {
# Figure out if this is a multilevel model or a single level model
# to extract the parameters estimates and the standard errors
if( class(obj) == "lm" ) {
b <- coef(obj)
se <- vcov(obj)
df <- obj$df.residual
} else {
b <- fixef(obj)
se <- vcov(obj)
df <- length(obj@frame[,1])-length(b)
}
# Compute the simple slopes
b1 = b[x] + b[xz]*z.vals[2] + b[xw]*w.vals[2] + b[xwz]*z.vals[2]*w.vals[2]
b2 = b[x] + b[xz]*z.vals[2] + b[xw]*w.vals[1] + b[xwz]*z.vals[2]*w.vals[1]
b3 = b[x] + b[xz]*z.vals[1] + b[xw]*w.vals[2] + b[xwz]*z.vals[1]*w.vals[2]
b4 = b[x] + b[xz]*z.vals[1] + b[xw]*w.vals[1] + b[xwz]*z.vals[1]*w.vals[1]
# Compute the standard errors for the simple slopes
s1 = sqrt( se[x,x] + se[xz,xz]*z.vals[2]^2 + se[xw,xw]*w.vals[2]^2 + se[xwz,xwz]*(w.vals[2]^2)*(z.vals[2]^2) + se[x,xz]*z.vals[2]*2 + se[x,xw]*w.vals[2]*2 + se[x,xwz]*z.vals[2]*w.vals[2]*2 + se[xz,xw]*z.vals[2]*w.vals[2]*2 + se[xz,xwz]*2*w.vals[2]*z.vals[2]^2 + se[xw,xwz]*z.vals[2]*2*w.vals[2]^2 )
s2 = sqrt( se[x,x] + se[xz,xz]*z.vals[2]^2 + se[xw,xw]*w.vals[1]^2 + se[xwz,xwz]*(w.vals[1]^2)*(z.vals[2]^2) + se[x,xz]*z.vals[2]*2 + se[x,xw]*w.vals[1]*2 + se[x,xwz]*z.vals[2]*w.vals[1]*2 + se[xz,xw]*z.vals[2]*w.vals[1]*2 + se[xz,xwz]*2*w.vals[1]*z.vals[2]^2 + se[xw,xwz]*z.vals[2]*2*w.vals[1]^2 )
s3 = sqrt( se[x,x] + se[xz,xz]*z.vals[1]^2 + se[xw,xw]*w.vals[2]^2 + se[xwz,xwz]*(w.vals[2]^2)*(z.vals[1]^2) + se[x,xz]*z.vals[1]*2 + se[x,xw]*w.vals[2]*2 + se[x,xwz]*z.vals[1]*w.vals[2]*2 + se[xz,xw]*z.vals[1]*w.vals[2]*2 + se[xz,xwz]*2*w.vals[2]*z.vals[1]^2 + se[xw,xwz]*z.vals[1]*2*w.vals[2]^2 )
s4 = sqrt( se[x,x] + se[xz,xz]*z.vals[1]^2 + se[xw,xw]*w.vals[1]^2 + se[xwz,xwz]*(w.vals[1]^2)*(z.vals[1]^2) + se[x,xz]*z.vals[1]*2 + se[x,xw]*w.vals[1]*2 + se[x,xwz]*z.vals[1]*w.vals[1]*2 + se[xz,xw]*z.vals[1]*w.vals[1]*2 + se[xz,xwz]*2*w.vals[1]*z.vals[1]^2 + se[xw,xwz]*z.vals[1]*2*w.vals[1]^2 )
# Compute the t values for these simple slopes
t1 = b1/s1
t2 = b2/s2
t3 = b3/s3
t4 = b4/s4
# Get the p values for these simple slopes
p1 = 2*pt(-abs(t1),df=df)
p2 = 2*pt(-abs(t2),df=df)
p3 = 2*pt(-abs(t3),df=df)
p4 = 2*pt(-abs(t4),df=df)
# Compute the simple slope differences
b12 = b[xw]*(w.vals[2]-w.vals[1]) + b[xwz]*z.vals[2]*(w.vals[2]-w.vals[1])
b13 = b[xz]*(z.vals[2]-z.vals[1]) + b[xwz]*w.vals[2]*(z.vals[2]-z.vals[1])
b14 = b[xz]*(z.vals[2]-z.vals[1]) + b[xw]*(w.vals[2]-w.vals[1]) + b[xwz]*(z.vals[2]*w.vals[2]-z.vals[1]*w.vals[1])
b23 = b[xz]*(z.vals[2]-z.vals[1]) + b[xw]*(w.vals[1]-w.vals[2]) + b[xwz]*(z.vals[2]*w.vals[1]-z.vals[1]*w.vals[2])
b24 = b[xz]*(z.vals[2]-z.vals[1]) + b[xwz]*w.vals[1]*(z.vals[2]-z.vals[1])
b34 = b[xw]*(w.vals[2]-w.vals[1]) + b[xwz]*z.vals[1]*(w.vals[2]-w.vals[1])
# Get the standard errors for the differences
s12 = (w.vals[2]-w.vals[1])*sqrt( se[xw,xw] + se[xwz,xwz]*z.vals[2]^2 + 2*z.vals[2]*se[xw,xwz] )
s13 = (z.vals[2]-z.vals[1])*sqrt( se[xz,xz] + se[xwz,xwz]*w.vals[2]^2 + 2*w.vals[2]*se[xz, xwz] )
s14 = sqrt( se[xz,xz]*(z.vals[2]-z.vals[1])^2 + se[xw,xw]*(w.vals[2]-w.vals[1])^2 + se[xwz,xwz]*(z.vals[2]*w.vals[2]-z.vals[1]*w.vals[1])^2 + 2*( se[xz,xw]*(z.vals[2]-z.vals[1])*(w.vals[2]-w.vals[1]) + se[xz,xwz]*(z.vals[2]-z.vals[1])*(z.vals[2]*w.vals[2]-z.vals[1]*w.vals[1]) + se[xw,xwz]*(w.vals[2]-w.vals[1])*(z.vals[2]*w.vals[2]-z.vals[1]*w.vals[1]) ) )
s23 = sqrt( se[xz,xz]*(z.vals[2]-z.vals[1])^2 + se[xw,xw]*(w.vals[1]-w.vals[2])^2 + se[xwz,xwz]*(z.vals[2]*w.vals[1]-z.vals[1]*w.vals[2])^2 + 2*( se[xz,xw]*(z.vals[2]-z.vals[1])*(w.vals[1]-w.vals[2]) + se[xz,xwz]*(z.vals[2]-z.vals[1])*(z.vals[2]*w.vals[1]-z.vals[1]*w.vals[2]) + se[xw,xwz]*(w.vals[1]-w.vals[2])*(z.vals[2]*w.vals[1]-z.vals[1]*w.vals[2]) ) )
s24 = (z.vals[2]-z.vals[1])*sqrt( se[xz,xz] + se[xwz,xwz]*w.vals[1]^2 + 2*w.vals[1]*se[xz, xwz] )
s34 = (w.vals[2]-w.vals[1])*sqrt( se[xw,xw] + se[xwz,xwz]*z.vals[1]^2 + 2*z.vals[1]*se[xw,xwz] )
# Get the test statistics
t12 = b12/s12
t13 = b13/s13
t14 = b14/s14
t23 = b23/s23
t24 = b24/s24
t34 = b34/s34
# Get the p values
p12 = 2*pt(-abs(t12),df=df)
p13 = 2*pt(-abs(t13),df=df)
p14 = 2*pt(-abs(t14),df=df)
p23 = 2*pt(-abs(t23),df=df)
p24 = 2*pt(-abs(t24),df=df)
p34 = 2*pt(-abs(t34),df=df)
# Create the output tables
#t1 = info for the four simple slopes
zlab = c("High", "High", "Low", "Low")
wlab = c("High", "Low", "High", "Low")
slab = c(1:4)
bval = c(b1,b2,b3,b4)
seval = c(s1,s2,s3,s4)
tval = c(t1,t2,t3,t4)
dfval= c(df,df,df,df)
pval = c(p1,p2,p3,p4)
table1 = data.frame(zlab,wlab, slab,bval,seval, tval,dfval,pval, stringsAsFactors=F)
colnames(table1)<- c("Z", "W", "slope_label", "b", "se", "t", "df", "p")
#t2 = info for slope difference tests
slope1 = c(1,1,1,2,2,3)
slope2 = c(2,3,4,3,4,4)
slab = c("a", "b", "c", "d", "e", "f")
bval = c(b12,b13,b14,b23,b24,b34)
seval = c(s12,s13,s14,s23,s24,s34)
tval = c(t12,t13,t14,t23,t24,t34)
dfval= c(df,df,df,df,df,df)
pval = c(p12,p13,p14,p23,p24,p34)
table2 = data.frame(slope1,slope2, slab,bval,seval, tval,dfval,pval, stringsAsFactors=F)
colnames(table2)<- c("slope_1", "slope_2", "slope_label", "b", "se", "t", "df", "p")
# Return the values
return(list(table1,table2))
}
indirect.effects <- function(m1, m2, coef1, coef2, nsims, d) {
# Add an id variable to the file
d$boot_id <- 1:length(d[,1])
i <- 1
ie <- rep(NA,nsims)
for(i in 1:nsims) {
# pull the bootstrap sample
unt <- sample(1:length(d[,1]), length(d[,1]), replace=TRUE)
unt.num <- d$boot_id
pulls <- data.frame(cbind(unt.num, unt))
bset <- merge(pulls, d, by.x=c("unt"), by.y=c("boot_id"), sort=TRUE)
# Run the regressions
o1 <- lm(m1, data=bset)
o2 <- lm(m2, data=bset)
# Pull the coefficients and calculate the indirect effect
ie[i] <- o1$coefficients[coef1]*o2$coefficients[coef2]
}
return(ie)
}
indirect.effects.lmer <- function(m1, m2, coef1, coef2, nsims, d) {
# Add an id variable to the file
d$boot_id <- 1:length(d[,1])
i <- 1
ie <- rep(NA,nsims)
for(i in 1:nsims) {
# pull the bootstrap sample
unt <- sample(1:length(d[,1]), length(d[,1]), replace=TRUE)
unt.num <- d$boot_id
pulls <- data.frame(cbind(unt.num, unt))
bset <- merge(pulls, d, by.x=c("unt"), by.y=c("boot_id"), sort=TRUE)
# Run the regressions
o1 <- lmer(m1, data=bset)
o2 <- lmer(m2, data=bset)
# Pull the coefficients and calculate the indirect effect
ie[i] <- o1$coefficients[coef1]*o2$coefficients[coef2]
}
return(ie)
}
boot.table <- function(eff) {
est <- mean(eff,na.rm=TRUE)
se <- sd(eff,na.rm=TRUE)
lcl <- quantile(eff, 0.025, na.rm=TRUE)
ucl <- quantile(eff, 0.975, na.rm=TRUE)
cltab <- data.frame(cbind(est, se, lcl, ucl))
cltab <- round(cltab, 4)
return(data.frame(cltab))
}
indirect.three.way <- function(m1, m2, x,w,z,xw,xz,wz,xwz, m, w.vals, z.vals, nsims, d) {
# Add an id variable to the file
d$boot_id <- 1:length(d[,1])
ie1 <- rep(NA,nsims)
ie2 <- rep(NA,nsims)
ie3 <- rep(NA,nsims)
ie4 <- rep(NA,nsims)
for(i in 1:nsims) {
# pull the bootstrap sample
unt <- sample(1:length(d[,1]), length(d[,1]), replace=TRUE)
unt.num <- d$boot_id
pulls <- data.frame(cbind(unt.num, unt))
bset <- merge(pulls, d, by.x=c("unt"), by.y=c("boot_id"), sort=TRUE)
# Run the regressions
o1 <- lm(m1, data=bset)
o2 <- lm(m2, data=bset)
# Figure out if this is a multilevel model or a single level model
# to extract the parameters estimates and the standard errors
if( class(o1) == "lm" ) {
b <- coef(o1)
b.m <- coef(o2)
df <- o1$df.residual
} else {
b <- fixef(o1)
df <- length(o1@frame[,1])-length(b)
}
# Compute the simple slopes
b1 = b[x] + b[xz]*z.vals[2] + b[xw]*w.vals[2] + b[xwz]*z.vals[2]*w.vals[2]
b2 = b[x] + b[xz]*z.vals[2] + b[xw]*w.vals[1] + b[xwz]*z.vals[2]*w.vals[1]
b3 = b[x] + b[xz]*z.vals[1] + b[xw]*w.vals[2] + b[xwz]*z.vals[1]*w.vals[2]
b4 = b[x] + b[xz]*z.vals[1] + b[xw]*w.vals[1] + b[xwz]*z.vals[1]*w.vals[1]
# Pull the coefficients and calculate the indirect effect
ie1[i] <- b1*b.m[m]
ie2[i] <- b2*b.m[m]
ie3[i] <- b3*b.m[m]
ie4[i] <- b4*b.m[m]
}
ie <- cbind(ie1, ie2, ie3, ie4)
est <- apply(ie, 2, mean, na.rm=TRUE)
se <- apply(ie, 2, sd, na.rm=TRUE)
lcl <- apply(ie, 2, function(x) quantile(x, 0.025, na.rm=TRUE))
ucl <- apply(ie, 2, function(x) quantile(x, 0.975, na.rm=TRUE))
# Format a nicer table of results
tab <- data.frame(round(t(rbind(est, se, lcl, ucl)),3) )
tab$w <- c("High", "Low", "High", "Low")
tab$z <- c("High", "High", "Low", "Low")
return(tab[,c(6,5,1:4)])
}
## This little blip is also for the mod.med stuff below - - this is old code that I need to update to save face for the embarrassing way that I coded it.
run.model.stage1 <- function(d, form1, form2, val) {
# first model
m1 <- lm(form1, data=d[!is.na(d$y), ])
a1 <- m1$coefficients['x']
a3 <- m1$coefficients['xw']
# second model
m2 <- lm(form2, data=d)
b1 <- m2$coefficients['m']
ind <- (a1 + a3*val)*b1
return(ind)
}
## This function is for use the below - this just bootstraps the modmed
modmed.boot.stage1 <- function(d, form1, form2, n.samp, val) {
b.res <- rep(NA, n.samp)
for(i in 1:n.samp) {
# Get dataset, with replacement
d.samp <- d[sample(nrow(d), length(d$y), replace=TRUE), ]
b.res[i] <- run.model.stage1(d.samp, form1, form2, val)
}
b <- mean(b.res, na.rm=TRUE)
se <- sd(b.res, na.rm=TRUE)
z <- b/se
p <- 2*pnorm(-abs(z))
ci.ub <- quantile(b.res, 0.975)
ci.lb <- quantile(b.res, 0.025)
ln <- round(cbind(b,se,z,p,ci.lb, ci.ub),3)
rownames(ln) <- round(val,2)
return(ln)
}
## This function computes conditional indirect effects
modmed.stage1 <- function(data, x,w,m,y, controls, n.samp, wvals) {
form1 <- formula(paste("m ~ ", paste(controls, collapse=" + "), " + ", "x + w + xw", sep=""))
form2 <- formula(paste("y ~ ", paste(controls, collapse=" + "), " + ", "x + w + xw + m", sep=""))
# rename the variables in the dataset that matter into what they should be
colnames(data)[colnames(data) == x] <- "x"
colnames(data)[colnames(data) == w] <- "w"
colnames(data)[colnames(data) == m] <- "m"
colnames(data)[colnames(data) == y] <- "y"
# Create the interaction variable for easy usage
data$xw <- data$x*data$w
# subset the data
data <- data[,c(controls, "x", "w", "xw", "m", "y")]
count <- 1
for(val in wvals) {
ln <- modmed.boot.stage1(data, form1, form2, n.samp, val)
if(count == 1) {
tab <- ln
} else {
tab <- rbind(tab, ln)
}
count <- count + 1
}
return(tab)
}
### Doing a dirty re-write of the functions to accoutn for second stage moderation in the psych ownership => ownership conflict effect through marking*helpseeking
run.model.stage2 <- function(d, form1, form2, val) {
# first model
m1 <- lm(form1, data=d[!is.na(d$y), ])
a1 <- m1$coefficients['x']
# a3 <- m1$coefficients['xw']
# second model
m2 <- lm(form2, data=d)
b1 <- m2$coefficients['m']
b3 <- m2$coefficients['mw']
ind <- (b1 + b3*val)*a1
return(ind)
}
## This function is for use the below - this just bootstraps the modmed
modmed.boot.stage2 <- function(d, form1, form2, n.samp, val) {
b.res <- rep(NA, n.samp)
for(i in 1:n.samp) {
# Get dataset, with replacement
d.samp <- d[sample(nrow(d), length(d$y), replace=TRUE), ]
b.res[i] <- run.model.stage2(d.samp, form1, form2, val)
}
b <- mean(b.res, na.rm=TRUE)
se <- sd(b.res, na.rm=TRUE)
z <- b/se
p <- 2*pnorm(-abs(z))
ci.ub <- quantile(b.res, 0.975)
ci.lb <- quantile(b.res, 0.025)
ln <- round(cbind(b,se,z,p,ci.lb, ci.ub),3)
rownames(ln) <- round(val,2)
return(ln)
}