Nothing
glmm.ordinal.bsreg <- function(target, dataset, id, threshold = 0.05, wei = NULL) {
threshold <- log(threshold)
dm <- dim(dataset)
if ( is.null(dm) ) {
n <- length(target)
p <- 1
} else {
n <- dm[1] ## sample size
p <- dm[2] ## number of variables
}
if ( p > n ) {
res <- paste("The number of variables is higher than the sample size. No backward procedure was attempted")
} else {
tic <- proc.time()
###################
ini <- ordinal::clmm( target ~ dataset + (1 | id), weights = wei )
likini <- logLik(ini)
stat <- numeric(p)
if ( p == 1 ) {
mod <- ordinal::clmm( target ~ 1 + (1 | id), weights = wei )
stat <- 2 * ( likini - logLik(mod) )
} else {
for (j in 1:p) {
mod <- ordinal::clmm( target ~ dataset[, -j, drop = FALSE] + (1 | id), weights = wei )
stat[j] <- 2 * ( likini - logLik(mod) )
}
}
mat <- cbind(1:p, pchisq( stat, 1, lower.tail = FALSE, log.p = TRUE), stat )
colnames(mat) <- c("variable", "log.p-values", "statistic" )
rownames(mat) <- 1:p
sel <- which.max( mat[, 2] )
info <- matrix( c(0, -10, -10), ncol = 3 )
if ( mat[sel, 2] < threshold ) {
runtime <- proc.time() - tic
res <- list(runtime = runtime, info = matrix(0, 0, 3), mat = mat, final = ini )
} else {
info[1, ] <- mat[sel, ]
mat <- mat[-sel, , drop = FALSE]
dat <- dataset[, -sel, drop = FALSE]
i <- 1
if ( info[1, 2] > threshold & dim(mat)[1] > 0) {
while ( info[i, 2] > threshold & dim(dat)[2] > 0 ) {
ini <- ordinal::clmm( target ~ dat + (1 | id), weights = wei )
likini <- logLik(ini)
i <- i + 1
k <- p - i + 1
if ( k == 1 ) {
mod <- ordinal::clmm(target ~ 1 + (1 | id), REML = FALSE, weights = wei)
stat <- 2 * ( likini - logLik(mod) )
pval <- pchisq( stat, 1, lower.tail = FALSE, log.p = TRUE)
if (pval > threshold ) {
final <- "No variables were selected"
info <- rbind(info, c(mat[, 1], pval, stat) )
dat <- dataset[, -info[, 1], drop = FALSE ]
mat <- matrix(nrow = 0, ncol = 3)
} else {
info <- rbind(info, c(0, -10, -10))
final <- ini
}
} else {
stat <- numeric(k)
for (j in 1:k) {
mod <- ordinal::clmm( target ~ dat[, -j, drop = FALSE] + (1 | id), weights = wei )
stat[j] <- 2 * ( likini - logLik(mod) )
}
mat[, 2:3] <- cbind( pchisq( stat, 1, lower.tail = FALSE, log.p = TRUE), stat )
sel <- which.max( mat[, 2] )
if ( mat[sel, 2] < threshold ) {
final <- ini
info <- rbind(info, c(0, -10, -10) )
} else {
info <- rbind(info, mat[sel, ] )
mat <- mat[-sel, , drop = FALSE]
dat <- dataset[, -info[, 1], drop = FALSE ]
}
}
} ## end while ( info[i, 2] > threshold & dim(dat)[2] > 0 )
runtime <- proc.time() - tic
info <- info[ info[, 1] > 0, , drop = FALSE]
colnames(mat) <- c("Variables", "log.p-values", "statistic")
res <- list(runtime = runtime, info = info, mat = mat, final = final )
} else {
runtime <- proc.time() - tic
res <- list(runtime = runtime, info = info, mat = NULL, final = mod )
} ## end if ( info[1, 2] > threshold & dim(mat)[1] > 0)
} ## end if ( mat[sel, 2] < threshold )
} ## end if ( p > n )
res
}
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