Nothing
R/crtFREQ.R
In eefAnalytics: Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs
Defines functions
reinfl
bootCompile
g.total
g.within
crt.crt
crt.perm
crtP
crt
crtFREQ.formula
crtFREQ.default
crtFREQ
Documented in
crtFREQ
#############################################################################
############# CRT main functions ################################################
#' Analysis of Cluster Randomised Education Trials using Multilevel Model under a Frequentist Setting.
#'
#' \code{crtFREQ} performs analysis of cluster randomised education trials using a multilevel model under a frequentist setting.
#'
#' @export
#' @param formula the model to be analysed is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the independent variables.
#' @param random a string variable specifying the "clustering variable" as contained in the data. See example below.
#' @param intervention a string variable specifying the "intervention variable" as appearing in the formula and the data. See example below.
#' @param baseln A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
#' @param nBoot number of bootstraps required to generate bootstrap confidence intervals.
#' @param nPerm number of permutations required to generate a permutated p-value.
#' @param type method of bootstrapping including case re-sampling at student level "case(1)", case re-sampling at school level "case(2)", case re-sampling at both levels "case(1,2)" and residual bootstrapping using "residual". If not provided, default will be case re-sampling at student level.
#' @param ci method for bootstrap confidence interval calculations; options are the Basic (Hall's) confidence interval "basic" or the simple percentile confidence interval "percentile". If not provided default will be percentile.
#' @param seed seed required for bootstrapping and permutation procedure, if not provided default seed will be used.
#' @param data data frame containing the data to be analysed.
#' @return S3 object; a list consisting of
#' \itemize{
#' \item \code{Beta}: Estimates and confidence intervals for variables specified in the model.
#' \item \code{ES}: Conditional Hedges' g effect size and its 95% confidence intervals. If nBoot is not specified, 95% confidence intervals are based on standard errors. If nBoot is specified, they are non-parametric bootstrapped confidence intervals.
#' \item \code{covParm}: A vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
#' \item \code{SchEffects}: A vector of the estimated deviation of each school from the intercept.
#' \item \code{Perm}: A "nPerm x 2w" matrix containing permutated effect sizes using residual variance and total variance. "w" denotes number of intervention. "w=1" for two arm trial and "w=2" for three arm trial excluding the control group. It is produced only when \code{nPerm} is specified.
#' \item \code{Bootstrap}: A "nBoot x 2w" matrix containing the bootstrapped effect sizes using residual variance (Within) and total variance (Total). "w" denotes number of intervention. "w=1" for two arm trial and "w=2" for three arm trial excluding the control group. It is only produced when \code{nBoot} is specified.
#' \item \code{Unconditional}: A list of unconditional effect sizes, covParm, Perm and Bootstrap obtained based on variances from the unconditional model (model with only the intercept as a fixed effect).
#' }
#' @example inst/examples/crtExample.R
crtFREQ<- function(formula,random,intervention,baseln,nPerm,nBoot,type,ci,seed,data)UseMethod("crtFREQ")
#' @export
crtFREQ.default <- function(formula,random,intervention,baseln,nPerm,nBoot,type,ci,seed,data){stop("No correct formula input given.")}
#' @export
crtFREQ.formula <- function(formula,random,intervention,baseln,nPerm,nBoot,type,ci,seed,data){
data <- na.omit(data.frame(data)[ ,unique(c(all.vars(formula),random, intervention))])
data <- data[order(data[,which(colnames(data)==random)]),]
intervention <- intervention
trt <- data[,which(colnames(data)==intervention)]
if( missing(baseln)){trt <- as.factor(trt)}
if(!missing(baseln)){trt <- relevel(as.factor(trt),baseln)}
tmp2 <- which(colnames(data)==random)
cluster2 <- data[,tmp2]
chk <- sum(rowSums(table(cluster2,trt)!=0)>1)
if(chk >0){stop("This is not a CRT design")}
stp <- as.character(row.names(table(cluster2,trt)))
control<-colnames(table(cluster2,trt))[1]
stp2 <- (apply(table(cluster2,trt),1,function(x)colnames(table(cluster2,trt))[x!=0]))
if(!missing(nPerm) & !missing(nBoot)){stop("Either nPerm or nBoot must be specified")}
if(missing(nPerm)){nPerm <-0}
if(missing(nBoot)){nBoot <-0}
if(!missing(type) & nBoot==0 | !missing(ci) & nBoot==0){stop("Please specify number of bootstraps")}
if(missing(type)){type<-"case(1)"}
if(missing(ci)){ci<-"percentile"}
optt <- c("case(1)","residual","case(2)","case(1,2)")
optc <- c("basic","percentile")
if(!type %in% optt | !ci %in% optc ) {stop("Please specify an allowed bootstrap option")}
tmp3 <- which(colnames(data)==intervention)
if( missing(baseln)){data[,tmp3] <- as.factor(data[,tmp3])}
if(!missing(baseln)){data[,tmp3] <- relevel(as.factor(data[,tmp3]),baseln)}
#data[,tmp3] <- as.factor(data[,tmp3])
mf <- model.frame(formula=formula, data=data)
mf <- mf[order(cluster2),]
cluster <- cluster2[order(cluster2)]
trt <- trt[order(cluster2)]
fixedDesignMatrix <- as.matrix(data.frame(model.matrix(attr(mf, "terms"), data=data)))
tmp <- colnames(fixedDesignMatrix )
tmp[1] <- "Intercept"
colnames(fixedDesignMatrix)<- tmp
posttest <- model.response(mf)
btp <- which(tmp %in% paste0(intervention, unique(data[, intervention]) ))
if(length(tmp2)!= 1){stop("Cluster variable misspecified")}
if(length(tmp3)!= 1){stop("Intervention variable misspecified")}
output <- crt(posttest=posttest,fixedDesignMatrix=fixedDesignMatrix,intervention=intervention,cluster=cluster, btp=btp)
if(nPerm>0){
if(nPerm<999){warning("Users should specify a higher number of iterations for valid results (nPerm>=1000)")}
output$Perm<- crt.perm(formula,data,stp,stp2,intervention,cluster,nPerm,random, btp,seed,baseln)
output$Condtional$Perm <- round(data.frame(output$Perm$condtional),2)
output$Unconditional$Perm <- round(data.frame(output$Perm$unconditional),2)
}
if(nBoot >0){
if(type=="residual"){
cov=matrix( nrow = 1, ncol = 2)
sigma2=matrix( nrow = 1, ncol = 2)
UFit <- lmer(posttest~ 1+(1|cluster))
cov[,2]<- as.numeric(summary(UFit)$varcor)
sigma2[,2]<- as.numeric(summary(UFit)$sigma^2)
Fit <- lmer(posttest~ fixedDesignMatrix-1+(1|cluster))
cov[,1]<- as.numeric(summary(Fit)$varcor)
sigma2[,1]<- as.numeric(summary(Fit)$sigma^2)
ran<-cbind(ranef(Fit)$cluster,ranef(UFit)$cluster) #School effects
ran$cluster <- rownames(ran)
res<-as.data.frame(cbind(resid(Fit),resid(UFit))) #Residuals
colnames(res)<-c("e.rsd","Ue.rsd")
pred<-as.data.frame(cbind(predict(Fit, re.form=NA),predict(UFit, re.form=NA))) #Fixed effects prediction
colnames(pred)<-c("feprd","Ufeprd")
for(i in 1:2){
res[,i]<-reinfl(varcor=sigma2[,i],J=length(res[,i]),res=res[,i])
ran[,i]<-reinfl(varcor=cov[,i],J=length(ran[,i]),res=ran[,i])
}
fixedDesignMatrix<-tryCatch(as.data.frame(merge(cbind(fixedDesignMatrix,cluster,pred,res),ran,all.x=TRUE,by="cluster")))
colnames(fixedDesignMatrix)[(ncol(fixedDesignMatrix)-5):ncol(fixedDesignMatrix)]<-c("feprd","Ufeprd","e.rsd","Ue.rsd","rndief","Urand.ef")
}
if(nBoot<1000){warning("Users should specify a higher number of iterations for valid results (nBoot>=1000)")}
tid <- c(1:nrow(fixedDesignMatrix))
#set.seed(1020252)
if(!missing(seed)){set.seed(seed)}
bootSamples <- NULL
for(ii in 1:length(unique(cluster))){
selID <- tid[cluster==unique(cluster)[ii]]
if(length(selID)>0){
selID2<- sapply(c(1:nBoot),function(x) selID [sample(1:length(selID), length(selID),replace=TRUE)])
bootSamples <- rbind(bootSamples ,selID2)
}
}
bootResults <- apply(bootSamples ,2,function(bt)crt.crt(posttest=posttest,fixedDesignMatrix=fixedDesignMatrix,intervention=intervention,cluster=cluster,type=type,bt=bt, btp=btp))
bootES <- bootCompile(output=output,trt=trt,bootResults=bootResults,intervention=intervention,ci=ci)
output$ES <- bootES
output$Bootstrap <- bootResults
output$Condtional$Bootstrap <- data.frame(t(sapply(1:nBoot, function(i)unlist(bootResults[[i]]$Conditional))))
output$Unconditional$Bootstrap <- data.frame(t(sapply(1:nBoot, function(i)unlist(bootResults[[i]]$Unconditional))))
Bnames <- gsub("Estimate1","Within", gsub("Estimate2", "Total",names(output$Condtional$Bootstrap )))
names(output$Condtional$Bootstrap) <- Bnames
names(output$Unconditional$Bootstrap) <- Bnames
}
output1 <- list()
output1$Beta <- output$Beta
output1$covParm <- output$covParm["Conditional",]
output1$ES <- output$ES$Conditional
output1$SchEffects <- output$SchEffects
if(nPerm > 0){output1$permES <- output$Condtional$Perm}
if(nBoot > 0){output1$Bootstrap <- output$Condtional$Bootstrap}
output1$Unconditional$ES <- output$ES$Unconditional
output1$Unconditional$covParm <- output$covParm["Unconditional",]
if(nPerm > 0){output1$Unconditional$permES <- output$Unconditional$Perm}
if(nBoot > 0){output1$Unconditional$Bootstrap <- output$Unconditional$Bootstrap}
output1$Method <- "MLM"
if(nBoot > 0){output1$Type <- type}
if(nBoot > 0){output1$CI <- ci}
output1$Function <- "crtFREQ"
class(output1) <- "eefAnalytics"
return(output1)
}
#######################################################################################
## random intercept model - internal
crt <- function(posttest,fixedDesignMatrix,intervention,cluster,btp){
freqFit <- lmer(posttest~ fixedDesignMatrix-1+ (1|cluster))
np<- row.names(summary(freqFit)$coef)
cit <- confint(freqFit,np)
betaB <- data.frame(cbind(summary(freqFit)$coefficients[,1],cit))
row.names(betaB)<- colnames(fixedDesignMatrix)
colnames(betaB) <- c("Estimate","95% LB ","95% UB")
betaB <- betaB
var.B<- as.numeric(summary(freqFit)$varcor)
var.W<- summary(freqFit)$sigma^2
var.tt <- var.W+var.B
ICC1 <- var.B/var.tt
sigmaBE1 <- round(c(var.B,var.W,var.B+var.W,(var.B/(var.B+var.W))),2)
names(sigmaBE1)<- c("Schools","Pupils","Total","ICC")
var.B1<- as.numeric(summary(lmer(posttest~ 1+ (1|cluster)))$varcor)
var.W1<- summary(lmer(posttest~ 1+(1|cluster)))$sigma^2
var.tt1 <- var.W1+var.B1
ICC <-(c(Conditional=ICC1,Unconditional=var.B1/var.tt1))
sigmaBE2 <- round(c(var.B1,var.W1,var.B1+var.W1,(var.B1/(var.B1+var.W1))),2)
names(sigmaBE2)<- c("Schools","Pupils","Total","ICC")
sigmaBE <- data.frame(rbind(Conditional=sigmaBE1,
Unconditional=sigmaBE2))
sigmaBE <- sigmaBE
sigma.W<-c(Conditional=var.W,Unconditional=var.W1)
sigma.tt<-c(Conditional=var.tt,Unconditional=var.tt1)
schRand <- data.frame(unique(cluster),ranef(freqFit)$cluster)
names(schRand)<- c("Schools","Estimate")
#btp <- which(substring(row.names(betaB),1,nchar(intervention))==intervention &
# nchar(colnames(fixedDesignMatrix))==(nchar(intervention)+1))
output2 <- list()
for( j in names(sigma.tt)){
var.w <-sigma.W[j]
var.tt<-sigma.tt[j]
icc<-ICC[j]
output.1<-list()
for( i in 1:length(btp )){
beta <- betaB[btp[i],1]
group <- fixedDesignMatrix[,btp[i]]
esWithin <- g.within(var.w=var.w, beta=beta, icc=icc, group=group, schoolID=cluster)
esTotal <- g.total(var.tt=var.tt, beta=beta, icc=icc, group=group, schoolID=cluster)
output1 <- data.frame(rbind(esWithin,esTotal))
colnames(output1) <- c("Estimate","95% LB","95% UB")
rownames(output1) <- c("Within","Total")
output.1[[i]] <- round(output1,2)
}
names(output.1) <- row.names(betaB)[btp]
output2[[j]] <- output.1
}
output <- list(Beta=round(betaB,2),covParm=sigmaBE,ES=output2,SchEffects=round(schRand,2))
return(output)
}
## internal
crtP <- function(posttest,fixedDesignMatrix,intervention,cluster,btp){
freqFit <- lmer(posttest~ fixedDesignMatrix-1+ (1|cluster))
betaB <- data.frame(summary(freqFit)$coefficients[,1])
row.names(betaB)<- colnames(fixedDesignMatrix)
#colnames(betaB) <- c("Estimate","95% LB ","95% UB")
betaB <- betaB
var.B<- as.numeric(summary(freqFit)$varcor)
var.W<- summary(freqFit)$sigma^2
var.tt <- var.W+var.B
ICC1 <- var.B/var.tt
sigmaBE1 <- c(var.B,var.W)
freqFit1 <- lmer(posttest~ 1+ (1|cluster))
var.B1<- as.numeric(summary(freqFit1)$varcor)
var.W1<- summary(freqFit1)$sigma^2
var.tt1 <- var.W1+var.B1
ICC <-(c(Conditional=ICC1,Unconditional=var.B1/var.tt1))
sigmaBE2 <- c(var.B1,var.W1)
sigmaBE <- data.frame(rbind(Conditional=sigmaBE1,Unconditional=sigmaBE2))
sigmaBE <- sigmaBE
sigma.W<-c(Conditional=var.W,Unconditional=var.W1)
sigma.tt<-c(Conditional=var.tt,Unconditional=var.tt1)
#btp <- which(substring(row.names(betaB),1,nchar(intervention))==intervention &
# nchar(colnames(fixedDesignMatrix))==(nchar(intervention)+1))
output2 <- list()
for( j in names(sigma.tt)){
var.w <-sigma.W[j]
var.tt<-sigma.tt[j]
icc<-ICC[j]
output.1<-list()
for( i in 1:length(btp)){
beta <- betaB[btp[i],1]
group <- fixedDesignMatrix[,btp[i]]
esWithin <- g.within(var.w=var.w, beta=beta, icc=icc, group=group, schoolID=cluster)
esTotal <- g.total(var.tt=var.tt, beta=beta, icc=icc, group=group, schoolID=cluster)
output1 <- data.frame(rbind(esWithin,esTotal))
colnames(output1) <- c("Estimate","95% LB","95% UB")
rownames(output1) <- c("Within","Total")
output.1[[i]] <- round(output1,2)
}
names(output.1) <- row.names(betaB)[btp]
output2[[j]] <- output.1
}
output <- list(ES=output2)
return(output)
}
## - internal
crt.perm <- function(formula,data,stp,stp2,intervention,cluster,nPerm,random,btp,seed,baseln){
data2 <- data[,-which(colnames(data)==intervention)]
g <- matrix(NA,nPerm,2*(length(unique(stp2))-1))
g.unc <- matrix(NA,nPerm,2*(length(unique(stp2))-1))
for(i in 1:nPerm){
#set.seed(12890*i+1)
if(!missing(seed)){set.seed(seed*i+1)}
tp3 <- data.frame(stp,sample(stp2))
names(tp3) <- c(paste(random),paste(intervention))
data.tp4 <- merge(data2,tp3,by=random)
data.tp4 <- data.tp4[order(data.tp4[,which(colnames(data.tp4)==random)]),]
cluster = data.tp4[,which(colnames(data.tp4)==random)]
tmp34 <- which(colnames(data.tp4)==intervention)
#data.tp4[,tmp34] <- as.factor(data.tp4[,tmp34])
if( missing(baseln)){data.tp4[,tmp34] <- as.factor(data.tp4[,tmp34])}
if(!missing(baseln)){data.tp4[,tmp34] <- relevel(as.factor(data.tp4[,tmp34]),baseln)}
mf <- model.frame(formula=formula, data=data.tp4)
fixedDesignMatrix <- as.matrix(data.frame(model.matrix(attr(mf, "terms"), data=data.tp4)))
tmp <- colnames(fixedDesignMatrix )
tmp[1] <- "Intercept"
colnames(fixedDesignMatrix)<- tmp
posttest <- model.response(mf)
intervention <- intervention
p2CRTFREQ <-crtP(posttest=posttest,fixedDesignMatrix=fixedDesignMatrix,intervention=intervention,cluster=cluster, btp=btp)
chkppp <- data.frame(cond=unlist(p2CRTFREQ$ES$Conditional),uncond=unlist(p2CRTFREQ$ES$Unconditional))
chkppp2 <- c(seq(1,6*(length(unique(tp3[,2]))-1),6),seq(2,6*(length(unique(tp3[,2]))-1),6))
chkppp3 <- chkppp2[order(chkppp2)]
g[i,] <- chkppp[chkppp3,"cond"]
g.unc[i,] <- chkppp[chkppp3,"uncond"]
}
ntpp <- rep(names(p2CRTFREQ$ES$Conditional),2)
ntpp <- ntpp[order(ntpp )]
wt <- rep(c("Within","Total"),length(names(p2CRTFREQ$ES$Conditional)))
colnames(g) <- paste(ntpp ,wt,sep="")
colnames(g.unc) <- paste(ntpp ,wt,sep="")
g1 <- list(condtional=g,unconditional=g.unc)
return(g1)
}
## - internal
crt.crt<- function(posttest,fixedDesignMatrix,intervention,cluster,type,bt,btp){
if(type=="case(1)") {
posttest2 <- posttest[bt]
fixedDesignMatrix2 <- fixedDesignMatrix[bt,]
cluster2 <- cluster[bt]
}
if(type=="case(2)") {
chk<-0
q<-0
binded<-as.data.frame(cbind(posttest,fixedDesignMatrix,cluster))
while(chk %in% c(0,1)){ #average=1|0 implies no variation in one or more dummies.
q<-q+1
if(q>100) {stop("Re-sampling of cases has failed.")}
sdata <- split(binded, binded[,"cluster"])
schsamp <- sdata[sample(length(sdata), replace = TRUE)]
rbinded <- do.call(rbind, schsamp)
fixedDesignMatrix2<- as.matrix(rbinded[,which(names(rbinded) %in% colnames(fixedDesignMatrix))])
rownames(fixedDesignMatrix2) <- 1:nrow(fixedDesignMatrix2)
posttest2<-as.matrix(rbinded[,1])
cluster2<-as.matrix(rbinded[,"cluster"]) #maybe change the way the cluster is identified?
chk<-colMeans(as.matrix(fixedDesignMatrix2[,btp]))
}
}
if(type=="case(1,2)") {
binded<-as.data.frame(cbind(posttest,fixedDesignMatrix,cluster))
chk<-0
q<-0
binded<-as.data.frame(cbind(posttest,fixedDesignMatrix,cluster))
while(chk %in% c(0,1)){
q<-q+1
if(q>100) {stop("Re-sampling of cases has failed.")}
sdata <- split(binded, binded[,"cluster"])
schsamp <- sdata[sample(length(sdata), replace = TRUE)]
samp <- lapply(schsamp, function(x) x[sample(nrow(x), replace = TRUE), ])
rbinded <- do.call(rbind, samp)
posttest2<-as.matrix(rbinded[,1])
fixedDesignMatrix2<-as.matrix(rbinded[,which(names(rbinded) %in% colnames(fixedDesignMatrix))])
rownames(fixedDesignMatrix2) <- 1:nrow(fixedDesignMatrix2)
cluster2<-as.matrix(rbinded[,"cluster"])
chk<-colMeans(as.matrix(fixedDesignMatrix2[,btp]))
}
}
if(type=="residual") {
res<-as.data.frame(fixedDesignMatrix[ ,c("e.rsd","Ue.rsd")])
ran<-as.data.frame(fixedDesignMatrix[ ,c("cluster","rand.ef","Urand.ef")])
pred<-as.data.frame(fixedDesignMatrix[ ,c("feprd","Ufeprd")])
ressamp <- as.data.frame(res[sample(length(res[,1]), replace = TRUE),]) #or res[bt]
sdata <- split(ran,ran[,"cluster"])
sdata<-lapply(sdata, function(x) x[1,]) #collapse random effects by group
ransamp<-cbind(do.call(rbind, sdata))
rownames(ransamp) <- 1:nrow(ransamp)
ransamp[,c("rand.ef","Urand.ef")]<-ransamp[sample(length(ransamp[,1]), replace = TRUE),c("rand.ef","Urand.ef")]
binded<-cbind(fixedDesignMatrix[,"cluster"],pred,ressamp)
colnames(binded)[1]<-"cluster"
binded<-merge(binded,ransamp,all.x=TRUE,by="cluster")
posttest2<-rowSums(binded[,c("feprd","e.rsd","rand.ef")])
Uposttest2<-rowSums(binded[,c("Ufeprd","Ue.rsd","Urand.ef")])
fixedDesignMatrix <- as.matrix(fixedDesignMatrix[ ,-which(names(fixedDesignMatrix) %in% c("cluster","feprd","e.rsd","rand.ef","Ufeprd","Ue.rsd","Urand.ef"))])
rownames(fixedDesignMatrix) <- 1:nrow(fixedDesignMatrix)
fixedDesignMatrix2<-fixedDesignMatrix
cluster2 <- cluster
}
freqFit <- try(lmer(posttest2~ fixedDesignMatrix2-1+(1|cluster2)),silent=TRUE)
output2 <- NULL
if(!is(freqFit, "try-error")){
betaB <- data.frame(summary(freqFit)$coefficients[,1])
row.names(betaB)<- colnames(fixedDesignMatrix)
betaB <- betaB
var.B2<- as.matrix(summary(freqFit)$varcor)
var.B3 <- c(matrix(attr(var.B2[[1]],"stddev")))
var.B <- var.B3^2
var.W<- summary(freqFit)$sigma^2
var.tt <- var.W+var.B
ICC1 <- var.B/var.tt
sigmaBE1 <- c(var.B,var.W)
names(sigmaBE1)<- c("Schools","Pupils")
if(type=="residual") {posttest2<-Uposttest2}
var.B21<- as.matrix(summary(lmer(posttest2~ 1+ (1|cluster2)))$varcor)
var.B31 <- c(matrix(attr(var.B21[[1]],"stddev")))
var.B1 <- var.B31^2
var.W1<- summary(lmer(posttest2~ 1+(1|cluster2)))$sigma^2
var.tt1 <- var.W1+var.B1
ICC <-(c(Conditional=ICC1, Unconditional=var.B1/var.tt1))
sigmaBE2 <- c(var.B1,var.W1)
names(sigmaBE2)<- c("Schools","Pupils")
sigmaBE <- data.frame(rbind(Conditional=sigmaBE1,
Unconditional=sigmaBE2))
sigmaBE <- sigmaBE
sigma.W<-c(Conditional=var.W,Unconditional=var.W1)
sigma.tt<-c(Conditional=var.tt,Unconditional=var.tt1)
#btp <- which(substring(row.names(betaB),1,nchar(intervention))==intervention&
# nchar(colnames(fixedDesignMatrix))==(nchar(intervention)+1))
output2 <- list()
for( j in names(sigma.tt)){
var.w <-sigma.W[j]
var.tt<-sigma.tt[j]
output.1<-list()
for( i in 1:length(btp )){
beta <- betaB[btp[i],1]
group <- fixedDesignMatrix[,btp[i]]
esWithin <- beta/sqrt(var.w)
esTotal <- beta/sqrt(var.tt)
output1 <- data.frame(rbind(esWithin,esTotal))
names(output1) <- c("Estimate")
rownames(output1) <- c("Within","Total")
output.1[[i]] <- round(output1,2)
}
names(output.1) <- row.names(betaB)[btp]
output2[[j]] <- output.1
}
}
return(output2)
}
## - internal
g.within <- function(var.w, beta, icc, group, schoolID){
t <- group; id <- schoolID
d.w <- (beta/sqrt(var.w))
n.it <- table(id[t==1]); n.ic <- table(id[t==0])
m.t <- length(unique(id[t==1])); m.c <- length(unique(id[t==0]))
M <- (m.t + m.c)
N.t <- sum(table(id[t==1])); N.c <- sum(table(id[t==0]))
N <- (N.t + N.c)
n.sim.1 <- ((N.c * sum(n.it^2))/(as.numeric(N.t)*as.numeric(N)))
n.sim.2 <- ((N.t * sum(n.ic^2))/(as.numeric(N.c)*as.numeric(N)))
n.sim <- (n.sim.1 + n.sim.2)
vterm1 <- ((N.t+N.c)/(as.numeric(N.t)*as.numeric(N.c)))
vterm2 <- (((1+(n.sim-1)*icc))/(1-icc))
vterm3 <- ((d.w^2)/(2*(N-M)))
se <- sqrt(vterm1*vterm2+vterm3)
LB <- (d.w-1.96*se); UB <- (d.w+1.96*se)
output <- data.frame(d.w, LB, UB)
names(output) <- c("g", "LB", "UB")
return(output)
}
## - internal
g.total <- function(var.tt, beta, icc, group, schoolID){
t <- group; id <- schoolID
n.it <- table(id[t==1]); n.ic <- table(id[t==0])
m.t <- length(unique(id[t==1])); m.c <- length(unique(id[t==0]))
M <- (m.t + m.c)
N.t <- sum(table(id[t==1])); N.c <- sum(table(id[t==0]))
N <- (N.t + N.c)
n.ut <- ((N.t^2-sum(n.it^2))/(as.numeric(N.t)*as.numeric(m.t-1)))
n.uc <- ((N.c^2-sum(n.ic^2))/(as.numeric(N.c)*as.numeric(m.c-1)))
dt.1 <- (beta/sqrt(var.tt))
dt.2 <- sqrt(1-icc*(((N-n.ut*m.t-n.uc*m.c)+n.ut+n.uc-2)/(N-2)))
d.t <- (dt.1*dt.2)
n.sim.1 <- ((as.numeric(N.c) * sum(n.it^2))/(as.numeric(N.t)*as.numeric(N)))
n.sim.2 <- ((as.numeric(N.t) * sum(n.ic^2))/(as.numeric(N.c)*as.numeric(N)))
n.sim <- (n.sim.1 + n.sim.2)
B <- (n.ut*(m.t-1)+n.uc*(m.c-1))
A.t <- ((as.numeric(N.t)^2*sum(n.it^2)+(sum(n.it^2))^2-2*as.numeric(N.t)*sum(n.it^3))/as.numeric(N.t)^2)
A.c <- ((as.numeric(N.c)^2*sum(n.ic^2)+(sum(n.ic^2))^2-2*as.numeric(N.c)*sum(n.ic^3))/as.numeric(N.c)^2)
A <- (A.t + A.c)
vterm1 <- (((N.t+N.c)/(as.numeric(N.t)*as.numeric(N.c)))*(1+(n.sim-1)*icc))
vterm2 <- (((N-2)*(1-icc)^2+A*icc^2+2*B*icc*(1-icc))*d.t^2)
vterm3 <- (2*(N-2)*((N-2)-icc*(N-2-B)))
se <- sqrt(vterm1+vterm2/vterm3)
LB <- (d.t-1.96*se); UB <- (d.t+1.96*se)
output <- data.frame(d.t, LB, UB)
names(output)<- c("g", "LB", "UB")
return(output)
}
## compile bootstrap results - internal
bootCompile <- function(output,trt,bootResults,intervention,ci){
Con_withinBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
Con_totalBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
Un_withinBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
Un_totalBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
for(k in 1:length(bootResults)){
tmp <- bootResults[[k]]$Conditional
tmpR <- NULL
for(j in 1:length(tmp)){
tmpR <- c(tmpR,tmp[[j]][,1])
}
Con_withinBoot[k,] <- tmpR[seq(1,2*length(tmp),2)]
Con_totalBoot[k,] <- tmpR[seq(2,2*length(tmp),2)]
}
for(k in 1:length(bootResults)){
tmp1 <- bootResults[[k]]$Unconditional
tmpR1 <- NULL
for(j in 1:length(tmp1)){
tmpR1 <- c(tmpR1,tmp1[[j]][,1])
}
Un_withinBoot[k,] <- tmpR1[seq(1,2*length(tmp1),2)]
Un_totalBoot[k,] <- tmpR1[seq(2,2*length(tmp1),2)]
}
withinCI_Conditional <- apply(Con_withinBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
TotalCI_Conditional <- apply(Con_totalBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
withinCI_Unconditional <- apply(Un_withinBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
TotalCI_Unconditional <- apply(Un_totalBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
withinCI<-list(withinCI_Conditional,withinCI_Unconditional)
TotalCI<-list(TotalCI_Conditional,TotalCI_Unconditional)
ES<-list(output$ES$Conditional,output$ES$Unconditional)
if(ci=="basic") {
for(j in 1:2) {
g=1
within<-list()
total<-list()
for(i in 1:(length(unique(trt))-1)) {
within[[1]]<-2*(as.numeric(as.data.frame(ES[[j]])[1,g]))-as.numeric(withinCI[[j]]["2.5%",i])
within[[2]]<-2*(as.numeric(as.data.frame(ES[[j]])[1,g]))-as.numeric(withinCI[[j]]["97.5%",i])
withinCI[[j]]["97.5%",i]<-within[[1]]
withinCI[[j]]["2.5%",i]<-within[[2]]
total[[1]] <-2*(as.numeric(as.data.frame(ES[[j]])[2,g]))-as.numeric(TotalCI[[j]]["2.5%",i])
total[[2]] <-2*(as.numeric(as.data.frame(ES[[j]])[2,g]))-as.numeric(TotalCI[[j]]["97.5%",i])
TotalCI[[j]]["97.5%",i]<-total[[1]]
TotalCI[[j]]["2.5%",i]<-total[[2]]
g=g+3
}
}
}
names(ES)<-c("Conditional","Unconditional")
names(withinCI)<-c("Conditional","Unconditional")
names(TotalCI)<-c("Conditional","Unconditional")
#btp <- which(substring(row.names(output$Beta),1,nchar(intervention))==intervention)
tmpES <- list()
for( j in names(ES)){
withinCI1<-withinCI[[j]]
TotalCI1<-TotalCI[[j]]
ES1<-ES[[j]]
temp<-list()
for(kk in 1:length(ES1)){
tmp1 <- round(rbind(withinCI1[,kk], TotalCI1[,kk]),2)
tmp2 <- cbind(ES1[[kk]][,1],tmp1)
colnames(tmp2)<- c("Estimate","95% LB","95% UB")
row.names(tmp2) <- c("Within","Total")
temp[[kk]] <- tmp2
}
names(temp) <- names(ES1)
tmpES[[j]] <- temp
#names(tmpES) <- names(output$ES)
}
return(tmpES)
}
## centers and reflates residuals - internal
reinfl<-function(varcor,J,res){
res<-res-mean(res)
Lr<-chol(varcor)
Ls<-chol(crossprod(res)/J)
A<-t(Lr%*%solve(Ls))
out<-res%*%A
return(out)
}
Try the eefAnalytics package in your browser
Any scripts or data that you put into this service are public.
eefAnalytics documentation built on July 3, 2022, 1:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions reinfl bootCompile g.total g.within crt.crt crt.perm crtP crt crtFREQ.formula crtFREQ.default crtFREQ
Documented in crtFREQ
#############################################################################
############# CRT main functions ################################################
#' Analysis of Cluster Randomised Education Trials using Multilevel Model under a Frequentist Setting.
#'
#' \code{crtFREQ} performs analysis of cluster randomised education trials using a multilevel model under a frequentist setting.
#'
#' @export
#' @param formula the model to be analysed is of the form y ~ x1+x2+.... Where y is the outcome variable and Xs are the independent variables.
#' @param random a string variable specifying the "clustering variable" as contained in the data. See example below.
#' @param intervention a string variable specifying the "intervention variable" as appearing in the formula and the data. See example below.
#' @param baseln A string variable allowing the user to specify the reference category for intervention variable. When not specified, the first level will be used as a reference.
#' @param nBoot number of bootstraps required to generate bootstrap confidence intervals.
#' @param nPerm number of permutations required to generate a permutated p-value.
#' @param type method of bootstrapping including case re-sampling at student level "case(1)", case re-sampling at school level "case(2)", case re-sampling at both levels "case(1,2)" and residual bootstrapping using "residual". If not provided, default will be case re-sampling at student level.
#' @param ci method for bootstrap confidence interval calculations; options are the Basic (Hall's) confidence interval "basic" or the simple percentile confidence interval "percentile". If not provided default will be percentile.
#' @param seed seed required for bootstrapping and permutation procedure, if not provided default seed will be used.
#' @param data data frame containing the data to be analysed.
#' @return S3 object; a list consisting of
#' \itemize{
#' \item \code{Beta}: Estimates and confidence intervals for variables specified in the model.
#' \item \code{ES}: Conditional Hedges' g effect size and its 95% confidence intervals. If nBoot is not specified, 95% confidence intervals are based on standard errors. If nBoot is specified, they are non-parametric bootstrapped confidence intervals.
#' \item \code{covParm}: A vector of variance decomposition into between cluster variance (Schools) and within cluster variance (Pupils). It also contains intra-cluster correlation (ICC).
#' \item \code{SchEffects}: A vector of the estimated deviation of each school from the intercept.
#' \item \code{Perm}: A "nPerm x 2w" matrix containing permutated effect sizes using residual variance and total variance. "w" denotes number of intervention. "w=1" for two arm trial and "w=2" for three arm trial excluding the control group. It is produced only when \code{nPerm} is specified.
#' \item \code{Bootstrap}: A "nBoot x 2w" matrix containing the bootstrapped effect sizes using residual variance (Within) and total variance (Total). "w" denotes number of intervention. "w=1" for two arm trial and "w=2" for three arm trial excluding the control group. It is only produced when \code{nBoot} is specified.
#' \item \code{Unconditional}: A list of unconditional effect sizes, covParm, Perm and Bootstrap obtained based on variances from the unconditional model (model with only the intercept as a fixed effect).
#' }
#' @example inst/examples/crtExample.R
crtFREQ<- function(formula,random,intervention,baseln,nPerm,nBoot,type,ci,seed,data)UseMethod("crtFREQ")
#' @export
crtFREQ.default <- function(formula,random,intervention,baseln,nPerm,nBoot,type,ci,seed,data){stop("No correct formula input given.")}
#' @export
crtFREQ.formula <- function(formula,random,intervention,baseln,nPerm,nBoot,type,ci,seed,data){
data <- na.omit(data.frame(data)[ ,unique(c(all.vars(formula),random, intervention))])
data <- data[order(data[,which(colnames(data)==random)]),]
intervention <- intervention
trt <- data[,which(colnames(data)==intervention)]
if( missing(baseln)){trt <- as.factor(trt)}
if(!missing(baseln)){trt <- relevel(as.factor(trt),baseln)}
tmp2 <- which(colnames(data)==random)
cluster2 <- data[,tmp2]
chk <- sum(rowSums(table(cluster2,trt)!=0)>1)
if(chk >0){stop("This is not a CRT design")}
stp <- as.character(row.names(table(cluster2,trt)))
control<-colnames(table(cluster2,trt))[1]
stp2 <- (apply(table(cluster2,trt),1,function(x)colnames(table(cluster2,trt))[x!=0]))
if(!missing(nPerm) & !missing(nBoot)){stop("Either nPerm or nBoot must be specified")}
if(missing(nPerm)){nPerm <-0}
if(missing(nBoot)){nBoot <-0}
if(!missing(type) & nBoot==0 | !missing(ci) & nBoot==0){stop("Please specify number of bootstraps")}
if(missing(type)){type<-"case(1)"}
if(missing(ci)){ci<-"percentile"}
optt <- c("case(1)","residual","case(2)","case(1,2)")
optc <- c("basic","percentile")
if(!type %in% optt | !ci %in% optc ) {stop("Please specify an allowed bootstrap option")}
tmp3 <- which(colnames(data)==intervention)
if( missing(baseln)){data[,tmp3] <- as.factor(data[,tmp3])}
if(!missing(baseln)){data[,tmp3] <- relevel(as.factor(data[,tmp3]),baseln)}
#data[,tmp3] <- as.factor(data[,tmp3])
mf <- model.frame(formula=formula, data=data)
mf <- mf[order(cluster2),]
cluster <- cluster2[order(cluster2)]
trt <- trt[order(cluster2)]
fixedDesignMatrix <- as.matrix(data.frame(model.matrix(attr(mf, "terms"), data=data)))
tmp <- colnames(fixedDesignMatrix )
tmp[1] <- "Intercept"
colnames(fixedDesignMatrix)<- tmp
posttest <- model.response(mf)
btp <- which(tmp %in% paste0(intervention, unique(data[, intervention]) ))
if(length(tmp2)!= 1){stop("Cluster variable misspecified")}
if(length(tmp3)!= 1){stop("Intervention variable misspecified")}
output <- crt(posttest=posttest,fixedDesignMatrix=fixedDesignMatrix,intervention=intervention,cluster=cluster, btp=btp)
if(nPerm>0){
if(nPerm<999){warning("Users should specify a higher number of iterations for valid results (nPerm>=1000)")}
output$Perm<- crt.perm(formula,data,stp,stp2,intervention,cluster,nPerm,random, btp,seed,baseln)
output$Condtional$Perm <- round(data.frame(output$Perm$condtional),2)
output$Unconditional$Perm <- round(data.frame(output$Perm$unconditional),2)
}
if(nBoot >0){
if(type=="residual"){
cov=matrix( nrow = 1, ncol = 2)
sigma2=matrix( nrow = 1, ncol = 2)
UFit <- lmer(posttest~ 1+(1|cluster))
cov[,2]<- as.numeric(summary(UFit)$varcor)
sigma2[,2]<- as.numeric(summary(UFit)$sigma^2)
Fit <- lmer(posttest~ fixedDesignMatrix-1+(1|cluster))
cov[,1]<- as.numeric(summary(Fit)$varcor)
sigma2[,1]<- as.numeric(summary(Fit)$sigma^2)
ran<-cbind(ranef(Fit)$cluster,ranef(UFit)$cluster) #School effects
ran$cluster <- rownames(ran)
res<-as.data.frame(cbind(resid(Fit),resid(UFit))) #Residuals
colnames(res)<-c("e.rsd","Ue.rsd")
pred<-as.data.frame(cbind(predict(Fit, re.form=NA),predict(UFit, re.form=NA))) #Fixed effects prediction
colnames(pred)<-c("feprd","Ufeprd")
for(i in 1:2){
res[,i]<-reinfl(varcor=sigma2[,i],J=length(res[,i]),res=res[,i])
ran[,i]<-reinfl(varcor=cov[,i],J=length(ran[,i]),res=ran[,i])
}
fixedDesignMatrix<-tryCatch(as.data.frame(merge(cbind(fixedDesignMatrix,cluster,pred,res),ran,all.x=TRUE,by="cluster")))
colnames(fixedDesignMatrix)[(ncol(fixedDesignMatrix)-5):ncol(fixedDesignMatrix)]<-c("feprd","Ufeprd","e.rsd","Ue.rsd","rndief","Urand.ef")
}
if(nBoot<1000){warning("Users should specify a higher number of iterations for valid results (nBoot>=1000)")}
tid <- c(1:nrow(fixedDesignMatrix))
#set.seed(1020252)
if(!missing(seed)){set.seed(seed)}
bootSamples <- NULL
for(ii in 1:length(unique(cluster))){
selID <- tid[cluster==unique(cluster)[ii]]
if(length(selID)>0){
selID2<- sapply(c(1:nBoot),function(x) selID [sample(1:length(selID), length(selID),replace=TRUE)])
bootSamples <- rbind(bootSamples ,selID2)
}
}
bootResults <- apply(bootSamples ,2,function(bt)crt.crt(posttest=posttest,fixedDesignMatrix=fixedDesignMatrix,intervention=intervention,cluster=cluster,type=type,bt=bt, btp=btp))
bootES <- bootCompile(output=output,trt=trt,bootResults=bootResults,intervention=intervention,ci=ci)
output$ES <- bootES
output$Bootstrap <- bootResults
output$Condtional$Bootstrap <- data.frame(t(sapply(1:nBoot, function(i)unlist(bootResults[[i]]$Conditional))))
output$Unconditional$Bootstrap <- data.frame(t(sapply(1:nBoot, function(i)unlist(bootResults[[i]]$Unconditional))))
Bnames <- gsub("Estimate1","Within", gsub("Estimate2", "Total",names(output$Condtional$Bootstrap )))
names(output$Condtional$Bootstrap) <- Bnames
names(output$Unconditional$Bootstrap) <- Bnames
}
output1 <- list()
output1$Beta <- output$Beta
output1$covParm <- output$covParm["Conditional",]
output1$ES <- output$ES$Conditional
output1$SchEffects <- output$SchEffects
if(nPerm > 0){output1$permES <- output$Condtional$Perm}
if(nBoot > 0){output1$Bootstrap <- output$Condtional$Bootstrap}
output1$Unconditional$ES <- output$ES$Unconditional
output1$Unconditional$covParm <- output$covParm["Unconditional",]
if(nPerm > 0){output1$Unconditional$permES <- output$Unconditional$Perm}
if(nBoot > 0){output1$Unconditional$Bootstrap <- output$Unconditional$Bootstrap}
output1$Method <- "MLM"
if(nBoot > 0){output1$Type <- type}
if(nBoot > 0){output1$CI <- ci}
output1$Function <- "crtFREQ"
class(output1) <- "eefAnalytics"
return(output1)
}
#######################################################################################
## random intercept model - internal
crt <- function(posttest,fixedDesignMatrix,intervention,cluster,btp){
freqFit <- lmer(posttest~ fixedDesignMatrix-1+ (1|cluster))
np<- row.names(summary(freqFit)$coef)
cit <- confint(freqFit,np)
betaB <- data.frame(cbind(summary(freqFit)$coefficients[,1],cit))
row.names(betaB)<- colnames(fixedDesignMatrix)
colnames(betaB) <- c("Estimate","95% LB ","95% UB")
betaB <- betaB
var.B<- as.numeric(summary(freqFit)$varcor)
var.W<- summary(freqFit)$sigma^2
var.tt <- var.W+var.B
ICC1 <- var.B/var.tt
sigmaBE1 <- round(c(var.B,var.W,var.B+var.W,(var.B/(var.B+var.W))),2)
names(sigmaBE1)<- c("Schools","Pupils","Total","ICC")
var.B1<- as.numeric(summary(lmer(posttest~ 1+ (1|cluster)))$varcor)
var.W1<- summary(lmer(posttest~ 1+(1|cluster)))$sigma^2
var.tt1 <- var.W1+var.B1
ICC <-(c(Conditional=ICC1,Unconditional=var.B1/var.tt1))
sigmaBE2 <- round(c(var.B1,var.W1,var.B1+var.W1,(var.B1/(var.B1+var.W1))),2)
names(sigmaBE2)<- c("Schools","Pupils","Total","ICC")
sigmaBE <- data.frame(rbind(Conditional=sigmaBE1,
Unconditional=sigmaBE2))
sigmaBE <- sigmaBE
sigma.W<-c(Conditional=var.W,Unconditional=var.W1)
sigma.tt<-c(Conditional=var.tt,Unconditional=var.tt1)
schRand <- data.frame(unique(cluster),ranef(freqFit)$cluster)
names(schRand)<- c("Schools","Estimate")
#btp <- which(substring(row.names(betaB),1,nchar(intervention))==intervention &
# nchar(colnames(fixedDesignMatrix))==(nchar(intervention)+1))
output2 <- list()
for( j in names(sigma.tt)){
var.w <-sigma.W[j]
var.tt<-sigma.tt[j]
icc<-ICC[j]
output.1<-list()
for( i in 1:length(btp )){
beta <- betaB[btp[i],1]
group <- fixedDesignMatrix[,btp[i]]
esWithin <- g.within(var.w=var.w, beta=beta, icc=icc, group=group, schoolID=cluster)
esTotal <- g.total(var.tt=var.tt, beta=beta, icc=icc, group=group, schoolID=cluster)
output1 <- data.frame(rbind(esWithin,esTotal))
colnames(output1) <- c("Estimate","95% LB","95% UB")
rownames(output1) <- c("Within","Total")
output.1[[i]] <- round(output1,2)
}
names(output.1) <- row.names(betaB)[btp]
output2[[j]] <- output.1
}
output <- list(Beta=round(betaB,2),covParm=sigmaBE,ES=output2,SchEffects=round(schRand,2))
return(output)
}
## internal
crtP <- function(posttest,fixedDesignMatrix,intervention,cluster,btp){
freqFit <- lmer(posttest~ fixedDesignMatrix-1+ (1|cluster))
betaB <- data.frame(summary(freqFit)$coefficients[,1])
row.names(betaB)<- colnames(fixedDesignMatrix)
#colnames(betaB) <- c("Estimate","95% LB ","95% UB")
betaB <- betaB
var.B<- as.numeric(summary(freqFit)$varcor)
var.W<- summary(freqFit)$sigma^2
var.tt <- var.W+var.B
ICC1 <- var.B/var.tt
sigmaBE1 <- c(var.B,var.W)
freqFit1 <- lmer(posttest~ 1+ (1|cluster))
var.B1<- as.numeric(summary(freqFit1)$varcor)
var.W1<- summary(freqFit1)$sigma^2
var.tt1 <- var.W1+var.B1
ICC <-(c(Conditional=ICC1,Unconditional=var.B1/var.tt1))
sigmaBE2 <- c(var.B1,var.W1)
sigmaBE <- data.frame(rbind(Conditional=sigmaBE1,Unconditional=sigmaBE2))
sigmaBE <- sigmaBE
sigma.W<-c(Conditional=var.W,Unconditional=var.W1)
sigma.tt<-c(Conditional=var.tt,Unconditional=var.tt1)
#btp <- which(substring(row.names(betaB),1,nchar(intervention))==intervention &
# nchar(colnames(fixedDesignMatrix))==(nchar(intervention)+1))
output2 <- list()
for( j in names(sigma.tt)){
var.w <-sigma.W[j]
var.tt<-sigma.tt[j]
icc<-ICC[j]
output.1<-list()
for( i in 1:length(btp)){
beta <- betaB[btp[i],1]
group <- fixedDesignMatrix[,btp[i]]
esWithin <- g.within(var.w=var.w, beta=beta, icc=icc, group=group, schoolID=cluster)
esTotal <- g.total(var.tt=var.tt, beta=beta, icc=icc, group=group, schoolID=cluster)
output1 <- data.frame(rbind(esWithin,esTotal))
colnames(output1) <- c("Estimate","95% LB","95% UB")
rownames(output1) <- c("Within","Total")
output.1[[i]] <- round(output1,2)
}
names(output.1) <- row.names(betaB)[btp]
output2[[j]] <- output.1
}
output <- list(ES=output2)
return(output)
}
## - internal
crt.perm <- function(formula,data,stp,stp2,intervention,cluster,nPerm,random,btp,seed,baseln){
data2 <- data[,-which(colnames(data)==intervention)]
g <- matrix(NA,nPerm,2*(length(unique(stp2))-1))
g.unc <- matrix(NA,nPerm,2*(length(unique(stp2))-1))
for(i in 1:nPerm){
#set.seed(12890*i+1)
if(!missing(seed)){set.seed(seed*i+1)}
tp3 <- data.frame(stp,sample(stp2))
names(tp3) <- c(paste(random),paste(intervention))
data.tp4 <- merge(data2,tp3,by=random)
data.tp4 <- data.tp4[order(data.tp4[,which(colnames(data.tp4)==random)]),]
cluster = data.tp4[,which(colnames(data.tp4)==random)]
tmp34 <- which(colnames(data.tp4)==intervention)
#data.tp4[,tmp34] <- as.factor(data.tp4[,tmp34])
if( missing(baseln)){data.tp4[,tmp34] <- as.factor(data.tp4[,tmp34])}
if(!missing(baseln)){data.tp4[,tmp34] <- relevel(as.factor(data.tp4[,tmp34]),baseln)}
mf <- model.frame(formula=formula, data=data.tp4)
fixedDesignMatrix <- as.matrix(data.frame(model.matrix(attr(mf, "terms"), data=data.tp4)))
tmp <- colnames(fixedDesignMatrix )
tmp[1] <- "Intercept"
colnames(fixedDesignMatrix)<- tmp
posttest <- model.response(mf)
intervention <- intervention
p2CRTFREQ <-crtP(posttest=posttest,fixedDesignMatrix=fixedDesignMatrix,intervention=intervention,cluster=cluster, btp=btp)
chkppp <- data.frame(cond=unlist(p2CRTFREQ$ES$Conditional),uncond=unlist(p2CRTFREQ$ES$Unconditional))
chkppp2 <- c(seq(1,6*(length(unique(tp3[,2]))-1),6),seq(2,6*(length(unique(tp3[,2]))-1),6))
chkppp3 <- chkppp2[order(chkppp2)]
g[i,] <- chkppp[chkppp3,"cond"]
g.unc[i,] <- chkppp[chkppp3,"uncond"]
}
ntpp <- rep(names(p2CRTFREQ$ES$Conditional),2)
ntpp <- ntpp[order(ntpp )]
wt <- rep(c("Within","Total"),length(names(p2CRTFREQ$ES$Conditional)))
colnames(g) <- paste(ntpp ,wt,sep="")
colnames(g.unc) <- paste(ntpp ,wt,sep="")
g1 <- list(condtional=g,unconditional=g.unc)
return(g1)
}
## - internal
crt.crt<- function(posttest,fixedDesignMatrix,intervention,cluster,type,bt,btp){
if(type=="case(1)") {
posttest2 <- posttest[bt]
fixedDesignMatrix2 <- fixedDesignMatrix[bt,]
cluster2 <- cluster[bt]
}
if(type=="case(2)") {
chk<-0
q<-0
binded<-as.data.frame(cbind(posttest,fixedDesignMatrix,cluster))
while(chk %in% c(0,1)){ #average=1|0 implies no variation in one or more dummies.
q<-q+1
if(q>100) {stop("Re-sampling of cases has failed.")}
sdata <- split(binded, binded[,"cluster"])
schsamp <- sdata[sample(length(sdata), replace = TRUE)]
rbinded <- do.call(rbind, schsamp)
fixedDesignMatrix2<- as.matrix(rbinded[,which(names(rbinded) %in% colnames(fixedDesignMatrix))])
rownames(fixedDesignMatrix2) <- 1:nrow(fixedDesignMatrix2)
posttest2<-as.matrix(rbinded[,1])
cluster2<-as.matrix(rbinded[,"cluster"]) #maybe change the way the cluster is identified?
chk<-colMeans(as.matrix(fixedDesignMatrix2[,btp]))
}
}
if(type=="case(1,2)") {
binded<-as.data.frame(cbind(posttest,fixedDesignMatrix,cluster))
chk<-0
q<-0
binded<-as.data.frame(cbind(posttest,fixedDesignMatrix,cluster))
while(chk %in% c(0,1)){
q<-q+1
if(q>100) {stop("Re-sampling of cases has failed.")}
sdata <- split(binded, binded[,"cluster"])
schsamp <- sdata[sample(length(sdata), replace = TRUE)]
samp <- lapply(schsamp, function(x) x[sample(nrow(x), replace = TRUE), ])
rbinded <- do.call(rbind, samp)
posttest2<-as.matrix(rbinded[,1])
fixedDesignMatrix2<-as.matrix(rbinded[,which(names(rbinded) %in% colnames(fixedDesignMatrix))])
rownames(fixedDesignMatrix2) <- 1:nrow(fixedDesignMatrix2)
cluster2<-as.matrix(rbinded[,"cluster"])
chk<-colMeans(as.matrix(fixedDesignMatrix2[,btp]))
}
}
if(type=="residual") {
res<-as.data.frame(fixedDesignMatrix[ ,c("e.rsd","Ue.rsd")])
ran<-as.data.frame(fixedDesignMatrix[ ,c("cluster","rand.ef","Urand.ef")])
pred<-as.data.frame(fixedDesignMatrix[ ,c("feprd","Ufeprd")])
ressamp <- as.data.frame(res[sample(length(res[,1]), replace = TRUE),]) #or res[bt]
sdata <- split(ran,ran[,"cluster"])
sdata<-lapply(sdata, function(x) x[1,]) #collapse random effects by group
ransamp<-cbind(do.call(rbind, sdata))
rownames(ransamp) <- 1:nrow(ransamp)
ransamp[,c("rand.ef","Urand.ef")]<-ransamp[sample(length(ransamp[,1]), replace = TRUE),c("rand.ef","Urand.ef")]
binded<-cbind(fixedDesignMatrix[,"cluster"],pred,ressamp)
colnames(binded)[1]<-"cluster"
binded<-merge(binded,ransamp,all.x=TRUE,by="cluster")
posttest2<-rowSums(binded[,c("feprd","e.rsd","rand.ef")])
Uposttest2<-rowSums(binded[,c("Ufeprd","Ue.rsd","Urand.ef")])
fixedDesignMatrix <- as.matrix(fixedDesignMatrix[ ,-which(names(fixedDesignMatrix) %in% c("cluster","feprd","e.rsd","rand.ef","Ufeprd","Ue.rsd","Urand.ef"))])
rownames(fixedDesignMatrix) <- 1:nrow(fixedDesignMatrix)
fixedDesignMatrix2<-fixedDesignMatrix
cluster2 <- cluster
}
freqFit <- try(lmer(posttest2~ fixedDesignMatrix2-1+(1|cluster2)),silent=TRUE)
output2 <- NULL
if(!is(freqFit, "try-error")){
betaB <- data.frame(summary(freqFit)$coefficients[,1])
row.names(betaB)<- colnames(fixedDesignMatrix)
betaB <- betaB
var.B2<- as.matrix(summary(freqFit)$varcor)
var.B3 <- c(matrix(attr(var.B2[[1]],"stddev")))
var.B <- var.B3^2
var.W<- summary(freqFit)$sigma^2
var.tt <- var.W+var.B
ICC1 <- var.B/var.tt
sigmaBE1 <- c(var.B,var.W)
names(sigmaBE1)<- c("Schools","Pupils")
if(type=="residual") {posttest2<-Uposttest2}
var.B21<- as.matrix(summary(lmer(posttest2~ 1+ (1|cluster2)))$varcor)
var.B31 <- c(matrix(attr(var.B21[[1]],"stddev")))
var.B1 <- var.B31^2
var.W1<- summary(lmer(posttest2~ 1+(1|cluster2)))$sigma^2
var.tt1 <- var.W1+var.B1
ICC <-(c(Conditional=ICC1, Unconditional=var.B1/var.tt1))
sigmaBE2 <- c(var.B1,var.W1)
names(sigmaBE2)<- c("Schools","Pupils")
sigmaBE <- data.frame(rbind(Conditional=sigmaBE1,
Unconditional=sigmaBE2))
sigmaBE <- sigmaBE
sigma.W<-c(Conditional=var.W,Unconditional=var.W1)
sigma.tt<-c(Conditional=var.tt,Unconditional=var.tt1)
#btp <- which(substring(row.names(betaB),1,nchar(intervention))==intervention&
# nchar(colnames(fixedDesignMatrix))==(nchar(intervention)+1))
output2 <- list()
for( j in names(sigma.tt)){
var.w <-sigma.W[j]
var.tt<-sigma.tt[j]
output.1<-list()
for( i in 1:length(btp )){
beta <- betaB[btp[i],1]
group <- fixedDesignMatrix[,btp[i]]
esWithin <- beta/sqrt(var.w)
esTotal <- beta/sqrt(var.tt)
output1 <- data.frame(rbind(esWithin,esTotal))
names(output1) <- c("Estimate")
rownames(output1) <- c("Within","Total")
output.1[[i]] <- round(output1,2)
}
names(output.1) <- row.names(betaB)[btp]
output2[[j]] <- output.1
}
}
return(output2)
}
## - internal
g.within <- function(var.w, beta, icc, group, schoolID){
t <- group; id <- schoolID
d.w <- (beta/sqrt(var.w))
n.it <- table(id[t==1]); n.ic <- table(id[t==0])
m.t <- length(unique(id[t==1])); m.c <- length(unique(id[t==0]))
M <- (m.t + m.c)
N.t <- sum(table(id[t==1])); N.c <- sum(table(id[t==0]))
N <- (N.t + N.c)
n.sim.1 <- ((N.c * sum(n.it^2))/(as.numeric(N.t)*as.numeric(N)))
n.sim.2 <- ((N.t * sum(n.ic^2))/(as.numeric(N.c)*as.numeric(N)))
n.sim <- (n.sim.1 + n.sim.2)
vterm1 <- ((N.t+N.c)/(as.numeric(N.t)*as.numeric(N.c)))
vterm2 <- (((1+(n.sim-1)*icc))/(1-icc))
vterm3 <- ((d.w^2)/(2*(N-M)))
se <- sqrt(vterm1*vterm2+vterm3)
LB <- (d.w-1.96*se); UB <- (d.w+1.96*se)
output <- data.frame(d.w, LB, UB)
names(output) <- c("g", "LB", "UB")
return(output)
}
## - internal
g.total <- function(var.tt, beta, icc, group, schoolID){
t <- group; id <- schoolID
n.it <- table(id[t==1]); n.ic <- table(id[t==0])
m.t <- length(unique(id[t==1])); m.c <- length(unique(id[t==0]))
M <- (m.t + m.c)
N.t <- sum(table(id[t==1])); N.c <- sum(table(id[t==0]))
N <- (N.t + N.c)
n.ut <- ((N.t^2-sum(n.it^2))/(as.numeric(N.t)*as.numeric(m.t-1)))
n.uc <- ((N.c^2-sum(n.ic^2))/(as.numeric(N.c)*as.numeric(m.c-1)))
dt.1 <- (beta/sqrt(var.tt))
dt.2 <- sqrt(1-icc*(((N-n.ut*m.t-n.uc*m.c)+n.ut+n.uc-2)/(N-2)))
d.t <- (dt.1*dt.2)
n.sim.1 <- ((as.numeric(N.c) * sum(n.it^2))/(as.numeric(N.t)*as.numeric(N)))
n.sim.2 <- ((as.numeric(N.t) * sum(n.ic^2))/(as.numeric(N.c)*as.numeric(N)))
n.sim <- (n.sim.1 + n.sim.2)
B <- (n.ut*(m.t-1)+n.uc*(m.c-1))
A.t <- ((as.numeric(N.t)^2*sum(n.it^2)+(sum(n.it^2))^2-2*as.numeric(N.t)*sum(n.it^3))/as.numeric(N.t)^2)
A.c <- ((as.numeric(N.c)^2*sum(n.ic^2)+(sum(n.ic^2))^2-2*as.numeric(N.c)*sum(n.ic^3))/as.numeric(N.c)^2)
A <- (A.t + A.c)
vterm1 <- (((N.t+N.c)/(as.numeric(N.t)*as.numeric(N.c)))*(1+(n.sim-1)*icc))
vterm2 <- (((N-2)*(1-icc)^2+A*icc^2+2*B*icc*(1-icc))*d.t^2)
vterm3 <- (2*(N-2)*((N-2)-icc*(N-2-B)))
se <- sqrt(vterm1+vterm2/vterm3)
LB <- (d.t-1.96*se); UB <- (d.t+1.96*se)
output <- data.frame(d.t, LB, UB)
names(output)<- c("g", "LB", "UB")
return(output)
}
## compile bootstrap results - internal
bootCompile <- function(output,trt,bootResults,intervention,ci){
Con_withinBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
Con_totalBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
Un_withinBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
Un_totalBoot <- matrix(NA,nrow=length(bootResults),ncol=(length(unique(trt ))-1))
for(k in 1:length(bootResults)){
tmp <- bootResults[[k]]$Conditional
tmpR <- NULL
for(j in 1:length(tmp)){
tmpR <- c(tmpR,tmp[[j]][,1])
}
Con_withinBoot[k,] <- tmpR[seq(1,2*length(tmp),2)]
Con_totalBoot[k,] <- tmpR[seq(2,2*length(tmp),2)]
}
for(k in 1:length(bootResults)){
tmp1 <- bootResults[[k]]$Unconditional
tmpR1 <- NULL
for(j in 1:length(tmp1)){
tmpR1 <- c(tmpR1,tmp1[[j]][,1])
}
Un_withinBoot[k,] <- tmpR1[seq(1,2*length(tmp1),2)]
Un_totalBoot[k,] <- tmpR1[seq(2,2*length(tmp1),2)]
}
withinCI_Conditional <- apply(Con_withinBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
TotalCI_Conditional <- apply(Con_totalBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
withinCI_Unconditional <- apply(Un_withinBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
TotalCI_Unconditional <- apply(Un_totalBoot,2,function(x)quantile(x,prob=c(0.025,0.975)))
withinCI<-list(withinCI_Conditional,withinCI_Unconditional)
TotalCI<-list(TotalCI_Conditional,TotalCI_Unconditional)
ES<-list(output$ES$Conditional,output$ES$Unconditional)
if(ci=="basic") {
for(j in 1:2) {
g=1
within<-list()
total<-list()
for(i in 1:(length(unique(trt))-1)) {
within[[1]]<-2*(as.numeric(as.data.frame(ES[[j]])[1,g]))-as.numeric(withinCI[[j]]["2.5%",i])
within[[2]]<-2*(as.numeric(as.data.frame(ES[[j]])[1,g]))-as.numeric(withinCI[[j]]["97.5%",i])
withinCI[[j]]["97.5%",i]<-within[[1]]
withinCI[[j]]["2.5%",i]<-within[[2]]
total[[1]] <-2*(as.numeric(as.data.frame(ES[[j]])[2,g]))-as.numeric(TotalCI[[j]]["2.5%",i])
total[[2]] <-2*(as.numeric(as.data.frame(ES[[j]])[2,g]))-as.numeric(TotalCI[[j]]["97.5%",i])
TotalCI[[j]]["97.5%",i]<-total[[1]]
TotalCI[[j]]["2.5%",i]<-total[[2]]
g=g+3
}
}
}
names(ES)<-c("Conditional","Unconditional")
names(withinCI)<-c("Conditional","Unconditional")
names(TotalCI)<-c("Conditional","Unconditional")
#btp <- which(substring(row.names(output$Beta),1,nchar(intervention))==intervention)
tmpES <- list()
for( j in names(ES)){
withinCI1<-withinCI[[j]]
TotalCI1<-TotalCI[[j]]
ES1<-ES[[j]]
temp<-list()
for(kk in 1:length(ES1)){
tmp1 <- round(rbind(withinCI1[,kk], TotalCI1[,kk]),2)
tmp2 <- cbind(ES1[[kk]][,1],tmp1)
colnames(tmp2)<- c("Estimate","95% LB","95% UB")
row.names(tmp2) <- c("Within","Total")
temp[[kk]] <- tmp2
}
names(temp) <- names(ES1)
tmpES[[j]] <- temp
#names(tmpES) <- names(output$ES)
}
return(tmpES)
}
## centers and reflates residuals - internal
reinfl<-function(varcor,J,res){
res<-res-mean(res)
Lr<-chol(varcor)
Ls<-chol(crossprod(res)/J)
A<-t(Lr%*%solve(Ls))
out<-res%*%A
return(out)
}
Try the eefAnalytics package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.