Nothing
gammafsreg <- function(target, dataset, ini = NULL, threshold = 0.05, wei = NULL, tol = 2, ncores = 1) {
if ( !is.null(ini) ) {
result <- gammafsreg_2(target, dataset, iniset = ini, wei = wei, threshold = threshold, tol = tol, ncores = ncores)
} else { ## else do the classical forward regression
threshold <- log(threshold) ## log of the significance level
p <- dim(dataset)[2] ## number of variables
devi <- dof <- phi <- numeric( p )
moda <- list()
k <- 1 ## counter
n <- length(target) ## sample size
con <- log(n)
tool <- numeric( min(n, p) )
dataset <- as.data.frame(dataset)
runtime <- proc.time()
ini <- glm( target ~ 1, family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )$deviance
if (ncores <= 1) {
for (i in 1:p) {
mi <- glm( target ~ dataset[, i], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
devi[i] <- mi$deviance
dof[i] <- length( mi$coefficients )
phi <- summary(mi)[[ 14 ]]
}
stat <- (ini - devi)/(dof - 1) /phi
pval <- pf( stat, dof - 1, n - dof , lower.tail = FALSE, log.p = TRUE )
} else {
cl <- makePSOCKcluster(ncores)
registerDoParallel(cl)
mod <- foreach( i = 1:p, .combine = rbind) %dopar% {
ww <- glm( target ~ dataset[, i], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
return( c( ww$deviance, length( ww$coefficients ), summary(ww)[[ 14 ]] ) )
}
stopCluster(cl)
}
mat <- cbind(1:p, pval, stat)
colnames(mat) <- c( "variables", "log.p-value", "stat" )
rownames(mat) <- 1:p
sel <- which.min(pval)
info <- matrix( numeric(3), ncol = 3 )
sela <- sel
if ( mat[sel, 2] < threshold ) {
info[1, ] <- mat[sel, ]
mat <- mat[-sel, , drop = FALSE]
mi <- glm( target ~ dataset[, sel], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
tool[1] <- BIC( mi )
moda[[ 1 ]] <- mi
} else {
info <- info
sela <- NULL
}
##########
##### k equals 2
##########
if ( info[k, 2] < threshold & nrow(mat) > 0 ) {
k <- 2
pn <- p - k + 1
ini <- mi$deviance
do <- length( mi$coefficients )
devi <- dof <- phi <- numeric( pn )
if ( ncores <= 1 ) {
devi <- dof <- numeric(pn)
for ( i in 1:pn ) {
ww <- glm( target ~., data = dataset[, c(sela, mat[i, 1]) ], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
devi[i] <- ww$deviance
dof[i] <- length( ww$coefficients )
phi[i] <- summary(ww)[[ 14 ]]
}
stat <- (ini - devi) /(dof - do) / phi
pval <- pf( stat, dof - do, n - dof, lower.tail = FALSE, log.p = TRUE )
} else {
cl <- makePSOCKcluster(ncores)
registerDoParallel(cl)
mod <- foreach( i = 1:pn, .combine = rbind) %dopar% {
ww <- glm( target ~., data = dataset[, c(sela, mat[i, 1]) ], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
return( c( ww$deviance, length( ww$coefficients ), summary(ww)[[ 14 ]] ) )
}
stopCluster(cl)
stat <- (ini - mod[, 1]) / (mod[, 2] - do)/ mod[, 3]
pval <- pf( stat, mod[, 2] - do, n - mod[, 2], lower.tail = FALSE, log.p = TRUE )
}
mat[, 2:3] <- cbind(pval, stat)
ina <- which.min(mat[, 2])
sel <- mat[ina, 1]
if ( mat[ina, 2] < threshold ) {
ma <- glm( target ~ dataset[, sela] + dataset[, sel], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
tool[k] <- BIC( ma )
if ( tool[ k - 1 ] - tool[ k ] <= tol ) {
info <- rbind(info, c( 1e300, 0, 0 ) )
} else {
info <- rbind(info, c( mat[ina, ] ) )
sela <- info[, 1]
mat <- mat[-ina , , drop = FALSE]
moda[[ k ]] <- ma
}
} else info <- rbind(info, c( 1e300, 0, 0 ) )
}
#######
#### k greater than 2
#######
if ( nrow(info) > 1 & nrow(mat) > 0 ) {
while ( ( info[k, 2] < threshold ) & ( k < n ) & ( tool[ k - 1 ] - tool[ k ] > tol ) & ( nrow(mat) > 0 ) ) {
ini <- moda[[ k ]]$deviance
do <- length( coef( moda[[ k ]] ) )
k <- k + 1
pn <- p - k + 1
devi <- dof <- phi <- numeric( pn )
if (ncores <= 1) {
devi = dof = numeric(pn)
for ( i in 1:pn ) {
ma <- glm( target ~., data = dataset[, c(sela, mat[i, 1] ) ], family = Gamma(link = log), weights = wei, 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(sela, mat[i, 1]) ], family = Gamma(link = log), weights = wei, 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 <- mat[ina, 1]
if ( mat[ina, 2] < threshold ) {
ma <- glm( target ~., data = as.data.frame( dataset[, c(sela, sel) ] ), family = Gamma(link = log), weights = wei, 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 <- info[, 1]
mat <- mat[-ina, , drop = FALSE]
moda[[ k ]] <- ma
}
} else info <- rbind(info, c( 1e300, 0, 0 ) )
}
}
runtime <- proc.time() - runtime
final <- NULL
d <- p - dim(mat)[1]
if ( d >= 1 ) {
final <- glm( target ~., data = dataset[, sela, drop = FALSE], family = Gamma(link = log), weights = wei, y = FALSE, model = FALSE )
info <- info[1:d, , drop = FALSE]
info <- cbind( info, tool[ 1:d ] )
colnames(info) <- c( "variables", "log.p-values", "stat", "BIC" )
rownames(info) <- info[, 1]
mat <- cbind( mat, exp(mat[, 2]) )
colnames(mat)[4] <- "p-value"
}
result = list( runtime = runtime, mat = mat, info = info, ci_test = "testIndGamma", final = final )
}
result
}
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