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####################################################################################################################################
### Filename: f3_sub3.R
### Description: Functions for calculating the test statistic for only three subplot factor
###
###
###
####################################################################################################################################
#' Test for two subplot factors
#'
#' @param X dataframe containing the data in the long table format
#' @param alpha alpha level used for the test
#' @param group column name of the data frame X specifying the groups
#' @param factor1 column name of the data frame X of the first factor variable
#' @param factor2 column name of the data frame X of the second factor variable
#' @param factor3 column name of the data frame X of the third factor variable
#' @param subject column name of the data frame X identifying the subjects
#' @return Returns a data frame consisting of the degrees of freedom, the test value, the critical value and the p-value
#' @keywords internal
hrm.test.3.three <- function(X, alpha , factor1, factor2, factor3, subject, data, formula, testing = rep(1,7), nonparametric, np.correction ){
ranked <- NULL
varQGlobal <- NULL
correction <- NULL
# create list for storing results; NULL used, because it is ignored by rbind
temp <- list(NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL)
for(i in 1:7){
if(testing[i]) {
temp[[i]] <- hrm.0w.3s(X, alpha , factor1, factor2, factor3, subject, data, i, "", nonparametric, ranked, varQGlobal, np.correction)
}
}
output <- list()
output$result <- rbind(temp[[1]], temp[[2]], temp[[3]], temp[[4]], temp[[5]], temp[[6]], temp[[7]])
output$formula <- formula
output$alpha <- alpha
output$subject <- subject
output$factors <- list(c("none"), c(factor1, factor2, factor3))
output$data <- X
output$var <- varQGlobal
output$nonparametric <- nonparametric
output$np.correction <- correction
rownames(output$result) <- 1:dim(output$result)[1]
class(output) <- "HRM"
return (output)
}
#' Test for interaction of factor A and B
#'
#' @param X dataframe containing the data in the long table format
#' @param alpha alpha level used for the test
#' @param group column name of the data frame X specifying the groups
#' @param factor1 column name of the data frame X of the first factor variable
#' @param subject column name of the data frame X identifying the subjects
#' @param data column name of the response variable
#' @param H string specifying the hypothesis
#' @param text a string, which will be printed in the output
#' @return Returns a data frame consisting of the degrees of freedom, the test value, the critical value and the p-value
#' @keywords internal
hrm.0w.3s <- function(X, alpha , factor1, factor2, factor3, subject, data, H = 1, text ="", nonparametric, ranked, varQGlobal, np.correction ){
stopifnot(is.data.frame(X),is.character(subject), is.character(factor1), is.character(factor2), alpha<=1, alpha>=0, is.logical(nonparametric))
f <- 0
f0 <- 0
crit <- 0
test <- 0
factor1 <- as.character(factor1)
factor2 <- as.character(factor2)
factor3 <- as.character(factor3)
subject <- as.character(subject)
X <- as.data.table(X)
setnames(X, c(data, factor1, subject, factor2, factor3), c("data", "factor1", "subject", "factor2", "factor3"))
a <- 1
d <- nlevels(X[,factor1])
c <- nlevels(X[,factor2])
c2 <- nlevels(X[,factor3])
n <- dim(X)[1]
if(nonparametric & is.null(ranked)) {
X[,data:= 1/(sum(n)*d*c*c2)*(rank(X[,data], ties.method = "average") - 1/2)]
}
for(i in 1:a){
X <- X[ order(subject, factor1, factor2, factor3), ]
X <- X[,data]
X <- matrix(X,ncol=d*c*c2,byrow=TRUE)
n[i] <- dim(X)[1]
}
if(is.null(ranked)){
eval.parent(substitute(ranked<-X))
} else {
X <- ranked
}
# creating X_bar (list with a entries)
X_bar <- colMeans(X) # as.matrix(vec(sapply(X, colMeans, na.rm=TRUE)))
kdim <- 1
# main effects
if(H==1){
K <- kronecker(kronecker(P(d), 1/c*J(c)), 1/c2*J(c2))
text <- paste(as.character(factor1) )
kdim <- d
}
if(H==2){
K <- kronecker(kronecker(1/d*J(d), P(c)), 1/c2*J(c2))
text <- paste(as.character(factor2) )
kdim <- c
}
if(H==3){
K <- kronecker(kronecker(1/d*J(d), 1/c*J(c)), P(c2))
text <- paste(as.character(factor3) )
kdim <- c2
}
# inferaction effects of 2 factors
if(H==4){
K <- kronecker(kronecker(P(d), P(c)), 1/c2*J(c2))
text <- paste(as.character(factor1), ":", as.character(factor2) )
kdim <- d*c
}
if(H==5){
K <- kronecker(kronecker(P(d), 1/c*J(c)), P(c2))
text <- paste(as.character(factor1), ":", as.character(factor3) )
kdim <- d*c2
}
if(H==6){
K <- kronecker(kronecker(1/d*J(d), P(c)), P(c2))
text <- paste( as.character(factor2), ":", as.character(factor3) )
kdim <- c*c2
}
# interaction effect of three factors
if(H==7){
K <- kronecker(kronecker(P(d), P(c)), P(c2))
text <- paste(as.character(factor1), ":", as.character(factor2), ":", as.character(factor3) )
kdim <- d*c*c2
}
S <- 1
# creating dual empirical covariance matrices
K_B <- kronecker(S, K)
V <- list(DualEmpirical2(Data = X, B=K)) #lapply(X, DualEmpirical2, B=K)
##########################
### U statistics
#########################
Q <- data.frame(Q1 = rep(0,a), Q2 = rep(0,a))
if(nonparametric){
for(i in 1:a){
Q[i,] <- calcU_onegroup(X,n,K)
}
}
eval.parent(substitute(correction <- np.correction))
if(is.na(np.correction)) {
eval.parent(substitute(correction <- (d*c*c2 >= max(n))))
np.correction <- (kdim >= max(n))
}
if(np.correction & nonparametric) {
if(H %in% 1:7) {
for(gg in 1:a) {
tmp <- X%*%K
nr <- dim(tmp)[1]
p <- dim(tmp)[2]
if(nr%%2 == 1){
nr <- nr - 1
}
mm <- colMeans(tmp)
g <- rep(0,nr)
g2 <- vector("list", length = nr)
t2 <- matrix(rep(0,p^2), ncol = p)
for(i in 1:nr) {
g[i] <- t(tmp[i,] - mm) %*% (tmp[i,] - mm)
g2[[i]] <- (tmp[i,] - mm) %*% t(tmp[i,] - mm)
t2 <- t2 + g2[[i]]
}
reps <- min(150, choose(nr,nr/2))
covs <- rep(0,reps)
g1 <- rep(0, nr/2)
g12 <- rep(0, nr/2)
for(i in 1:reps) {
grp <- sample(c(rep(1,nr/2), rep(2,nr/2)))
g1 <- g[grp == 1]
g12 <- g[grp == 2]
covs[i] <- cov(g1,g12)
}
t4 <- rep(0, nr*(nr - 1)/2)
k <- 1
for(i in 1:nr) {
j <- i + 1
while(j <= nr) {
t4[k] <- matrix.trace(g2[[i]]%*%g2[[j]])
k <- k + 1
j <- j + 1
}
}
corr <- mean(covs)
corr2 <- mean(t4) - matrix.trace((1/nr*t2)*(1/nr*t2))
tmpQ1 <- Q[gg,1] - corr*(n[gg]^2*1/(n[gg]^2 - n[gg]))^2
tmpQ2 <- Q[gg,2] - corr2*(n[gg]^2*1/(n[gg]^2 - n[gg]))^2
if(tmpQ1 > 0) {
Q[gg,1] <- tmpQ1
}
if(tmpQ2 > 0) {
Q[gg,2] <- tmpQ2
}
}
}
}
#################################################################################################
# f
f_1 <- 0
f_2 <- 0
for(i in 1:a){
f_1 <- f_1 + (1*1/n[i])^2*.E1(n,i,V[[i]], nonparametric, Q)
j <- i+1
while(j<=a){
f_1 <- f_1 + 2*(1*1/n[i])*(S[j,j]*1/n[j])*.E3(V[[i]],V[[j]])
j <- j+1
}
}
for(i in 1:a){
f_2 <- f_2 + (1*1/n[i])^2*.E2(n,i,V[[i]], nonparametric, Q)
j <- i+1
while(j<=a){
f_2 <- f_2 + 2*S[i,j]*S[j,i]*1/(n[i]*n[j])*.E4(1/(n[i]-1)*P(n[i])%*%X,1/(n[j]-1)*K%*%t(X)%*%P(n[j])%*%X%*%K%*%t(X)%*%P(n[i]))
j <- j+1
}
}
f <- f_1/f_2
##################################################################################################
#################################################################################################
# f0
f0_1 <- f_1
f0_2 <- 0
for(i in 1:a){
f0_2 <- f0_2 + (1*1/n[i])^2*1/(n[i]-1)*.E2(n,i,V[[i]], nonparametric, Q)
}
f0 <- f0_1/f0_2
##################################################################################################
# critical value
crit <- qf(1-alpha,f,f0)
# Test
direct <- 1/n[1]*var(X)
eval.parent(substitute(varQGlobal <- direct))
test <- (t(X_bar)%*%K_B%*%X_bar)/(t(rep(1,dim(K_B)[1]))%*%(K_B*direct)%*%(rep(1,dim(K_B)[1])))
p.value <- 1-pf(test,f,f0)
output <- data.frame(hypothesis=text,df1=f,df2=f0, crit=crit, test=test, p.value=p.value, sign.code=.hrm.sigcode(p.value))
if(nonparametric) {
output$np.correction <- np.correction
}
return (output)
}
# End ------------------------------------------------------------
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