Nothing
quasibinom.fsreg_2 <- function(target, dataset, iniset = NULL, wei = NULL, threshold = 0.05, tol = 2, ncores = 1) {
dm <- dim(dataset)
if ( is.null(dm) ) {
n <- length(target)
p <- 1
} else {
n <- dm[1] ## sample size
p <- dm[2] ## number of variables
}
devi <- dof <- numeric( p )
moda <- list()
k <- 1 ## counter
tool <- numeric( min(n, p) )
threshold <- log(threshold)
pa <- NCOL(iniset)
da <- 1:pa
dataset <- cbind(iniset, dataset)
dataset <- as.data.frame(dataset)
runtime <- proc.time()
devi <- dof <- phi <- numeric(p)
mi <- glm(target ~., data = as.data.frame( iniset ), weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
do <- length( mi$coefficients )
ini <- mi$deviance
if (ncores <= 1) {
for (i in 1:p) {
mi <- glm( target ~ . , data = dataset[, c(da, pa + i)], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
devi[i] <- mi$deviance
dof[i] <- length( mi$coefficients )
phi[i] <- summary(mi)[[ 14 ]]
}
stat <- (ini - devi)/(dof - do) / phi
pval <- pf( stat, dof - do, n - dof, lower.tail = FALSE, log.p = TRUE )
} else {
cl <- makePSOCKcluster(ncores)
registerDoParallel(cl)
mod <- foreach( i = 1:p, .combine = rbind) %dopar% {
ww <- glm( target ~., data = dataset[, c(da, pa + i)], weights = wei, family = quasibinomial(link = logit) )
return( c( ww$deviance, length( ww$coefficients ), summary(ww)[[ 14 ]] ) )
}
stopCluster(cl)
stat <- (mod[, 1] - ini)/(mod[, 2] - do) /mod[, 3]
pval <- pf( stat, mod[, 2] - do, n - mod[, 2], lower.tail = FALSE, log.p = TRUE )
}
mat <- cbind(1:p, pval, stat)
colnames(mat)[1] <- "variables"
rownames(mat) <- 1:p
sel <- which.min(pval)
info <- matrix( c( 1e300, 0, 0 ), ncol = 3 )
sela <- pa + sel
if ( mat[sel, 2] < threshold ) {
info[k, ] <- mat[sel, ]
mat <- mat[-sel, , drop = FALSE]
mi <- glm( target ~., data = dataset[, c(da, sela) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
tool[k] <- BIC( mi )
moda[[ k ]] <- mi
}
############
### k equals 2
############
if ( info[k, 2] < threshold & nrow(mat) > 0 ) {
k <- k + 1
pn <- p - k + 1
ini <- 2 * as.numeric( logLik(moda[[ 1 ]]) )
do <- length( coef( moda[[ 1 ]] ) )
devi <- dof <- phi <- numeric( pn )
if ( ncores <= 1 ) {
for ( i in 1:pn ) {
ww <- glm( target ~., data = dataset[, c(da, sela, pa + mat[i, 1]) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
devi[i] <- ww$deviance
dof[i] <- length( ww$coefficients )
phi[i] <- summary(ww)[[ 14 ]]
}
stat <- (ini - devi)/(dof - do) / phi
pval <- pf( stat, dof - do, n - dof, lower.tail = FALSE, log.p = TRUE )
} else {
cl <- makePSOCKcluster(ncores)
registerDoParallel(cl)
mod <- foreach( i = 1:pn, .combine = rbind) %dopar% {
ww <- glm( target ~., data = dataset[, c(da, sela, pa + mat[i, 1]) ], weights = wei, family = quasibinomial(link = logit) )
return( c( ww$deviance, length( ww$coefficients ), summary(ww)[[ 14 ]] ) )
}
stopCluster(cl)
stat <- (ini - mod[, 1])/(mod[, 2] - do) / mod[, 3]
pval <- pf( stat, mod[, 2] - do, n - mod[, 2], lower.tail = FALSE, log.p = TRUE )
}
mat[, 2:3] <- cbind(pval, stat)
ina <- which.min(mat[, 2])
sel <- pa + mat[ina, 1]
if ( mat[ina, 2] < threshold ) {
ma <- glm( target ~., data = dataset[, c(da, sela, sel) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
tool[k] <- BIC( ma )
if ( tool[ k - 1 ] - tool[ k ] <= tol ) {
info <- info
} else {
info <- rbind(info, c( mat[ina, ] ) )
sela <- c(sela, sel)
mat <- mat[-ina , , drop = FALSE]
moda[[ k ]] <- ma
}
} else info <- info
}
############
### k greater than 2
############
if ( nrow(info) > 1 & nrow(mat) > 0 ) {
while ( info[k, 2] < threshold & k < n - 15 & tool[ k - 1 ] - tool[ k ] > tol & nrow(mat) > 0 ) {
ini <- 2 * as.numeric( logLik(moda[[ k ]]) )
do <- length( coef( moda[[ k ]] ) )
k <- k + 1
pn <- p - k + 1
devi <- dof <- phi <- numeric( pn )
if (ncores <= 1) {
for ( i in 1:pn ) {
ma <- glm( target ~., data = dataset[, c(da, sela, pa + mat[i, 1] ) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
devi[i] <- ma$deviance
dof[i] <- length( ma$coefficients )
phi[i] <- summary(ma)[[ 14 ]]
}
stat <- (ini - devi)/(dof - do) /phi
pval <- pf( stat, dof - do, n - dof, lower.tail = FALSE, log.p = TRUE )
} else {
cl <- makePSOCKcluster(ncores)
registerDoParallel(cl)
mod <- foreach( i = 1:pn, .combine = rbind) %dopar% {
ww <- glm( target ~., data = dataset[, c(da, sela, pa + mat[i, 1]) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
return( c( ww$deviance, length( ww$coefficients ), summary(ww)[[ 14 ]] ) )
}
stopCluster(cl)
stat <- (ini - mod[, 1])/(mod[, 2] - do)/mod[, 3]
pval <- pf( stat, mod[, 2] - do, n - mod[, 2], lower.tail = FALSE, log.p = TRUE )
}
mat[, 2:3] <- cbind(pval, stat)
ina <- which.min(mat[, 2])
sel <- pa + mat[ina, 1]
if ( mat[ina, 2] < threshold ) {
ma <- glm( target ~., data = dataset[, c(da, sela, sel) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
tool[k] <- BIC( ma )
if ( tool[ k - 1 ] - tool[ k ] < tol ) {
info <- rbind(info, c( 1e300, 0, 0 ) )
} else {
info <- rbind( info, mat[ina, ] )
sela <- c(sela, sel)
mat <- mat[-ina, , drop = FALSE]
moda[[ k ]] <- ma
}
} else info <- rbind(info, c( 1e300, 0, 0 ) )
}
}
runtime <- proc.time() - runtime
d <- length(moda)
final <- glm( target ~., data = dataset[, c(da, sela) ], weights = wei, family = quasibinomial(link = logit), y = FALSE, model = FALSE )
info <- info[1:d, , drop = FALSE]
info <- cbind( info, tool[ 1:d ] )
colnames(info) <- c( "variables", "log.p-value", "stat", "BIC" )
rownames(info) <- info[, 1]
list( runtime = runtime, mat = t(mat), info = info, ci_test = "testIndQBinom", final = final )
}
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