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lm.fsreg_2 <- function(target, dataset, iniset = NULL, threshold = 0.05, wei = NULL, stopping = "BIC", tol = 2, ncores = 1 ) {
threshold = log(threshold)
p <- dim(dataset)[2] ## number of variables
pval <- stat <- dof <- numeric( p )
moda <- list()
## percentages
if ( min( target ) > 0 & max( target ) < 1 ) target <- log( target / (1 - target) )
#check for NA values in the dataset and replace them with the variable median or the mode
if ( any(is.na(dataset)) ) {
#dataset = as.matrix(dataset);
warning("The dataset contains missing values (NA) and they were replaced automatically by the variable (column) median (for numeric) or by the most frequent level (mode) if the variable is factor")
if ( is.matrix(dataset) ) {
dataset <- apply( dataset, 2, function(x){ x[which(is.na(x))] = median(x, na.rm = TRUE) ; return(x) } )
} else {
poia <- unique( which( is.na(dataset), arr.ind = TRUE )[, 2] )
for ( i in poia ) {
xi <- dataset[, i]
if ( is.numeric(xi) )
{
xi[ which( is.na(xi) ) ] <- median(xi, na.rm = TRUE)
} else if ( is.factor( xi ) ) xi[ which( is.na(xi) ) ] <- levels(xi)[ which.max( as.vector( table(xi) ) )]
dataset[, i] <- xi
}
}
}
## is there already an initial set of variables to start with?
pa <- NCOL(iniset)
da <- 1:pa
dataset <- cbind(iniset, dataset)
dataset <- as.data.frame(dataset)
n <- length(target) ## sample size
tool <- numeric( length( min(n, p) ) )
info <- matrix( c( 1e300, 0, 0 ), ncol = 3 )
k <- 1 ## counter k is 1, step 1
mi <- lm(target ~. , data = as.data.frame(iniset), weights = wei, y = FALSE, model = FALSE)
runtime <- proc.time()
if (ncores <= 1) {
for (i in 1:p) {
ww <- lm( target ~., data = dataset[, c(da, pa + i)], weights = wei, y = FALSE, model = FALSE )
tab <- anova(mi, ww)
stat[i] <- tab[2, 5]
df1 <- tab[2, 3] ; df2 = tab[2, 1]
pval[i] <- pf( stat[i], df1, df2, lower.tail = FALSE, log.p = TRUE )
}
mat <- cbind(1:p, pval, stat)
} else {
cl <- makePSOCKcluster(ncores)
doParallel::registerDoParallel(cl)
mod <- foreach( i = 1:p, .combine = rbind ) %dopar% {
ww <- lm( target ~., data = dataset[, c(da, pa + i)], weights = wei, y = FALSE, model = FALSE )
tab <- anova( mi, ww )
stat <- tab[2, 5]
df1 <- tab[2, 3] ; df2 = tab[2, 1]
pval <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE )
return( c(pval, stat) )
}
stopCluster(cl)
mat <- cbind(1:p, mod)
}
colnames(mat) <- c( "variables", "log.p-value", "stat" )
rownames(mat) <- 1:p
sel <- which.min(mat[, 2])
sela <- pa + sel
if ( mat[sel, 2] < threshold ) {
info[k, ] <- mat[sel, ]
mat <- mat[-sel, , drop = FALSE]
if ( stopping == "adjrsq" ) {
mi = lm( target ~., data = dataset[, c(da, sel) ], weights = wei, y = FALSE, model = FALSE )
tool[k] <- as.numeric( summary( mi )[[ 9 ]] )
} else if ( stopping == "BIC" ) {
mi = lm( target ~., data = dataset[, c(da, sel) ], y = FALSE, model = FALSE )
tool[k] <- BIC( mi )
}
moda[[ k ]] <- mi
}
######
###### k equal to 2
######
if ( info[k, 2] < threshold & nrow(mat) > 0 ) {
k <- k + 1
pn <- p - k + 1
if ( ncores <= 1 ) {
for (i in 1:pn) {
ww = lm( target ~., data = dataset[, c(da, sel, pa + mat[i, 1]) ], weights = wei, y = FALSE, model = FALSE )
tab = anova( mi, ww )
mat[i, 3] = tab[2, 5]
df1 = tab[2, 3] ; df2 = tab[2, 1]
mat[i, 2] = pf( mat[i, 3], df1, df2, lower.tail = FALSE, log.p = TRUE )
}
} else {
cl <- makePSOCKcluster(ncores)
doParallel::registerDoParallel(cl)
mod <- foreach( i = 1:pn, .combine = rbind) %dopar% {
ww <- lm( target ~., data = dataset[, c(da, sel, pa + mat[i, 1]) ], weights = wei, y = FALSE, model = FALSE )
tab <- anova( mi, ww )
stat <- tab[2, 4]
df1 <- tab[2, 3] ; df2 = tab[2, 1]
pval <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE )
return( c(pval, stat) )
}
stopCluster(cl)
mat <- cbind(mat[, 1], mod)
}
}
ina <- which.min(mat[, 2])
sel <- pa + mat[ina, 1]
if ( stopping == "adjrsq" ) {
if ( mat[ina, 2] < threshold ) {
ma <- lm( target ~., data = dataset[, c(da, sela, sel) ], weights = wei, y = FALSE, model = FALSE )
tool[k] <- as.numeric( summary( ma )[[ 9 ]] )
if ( tool[ k ] - tool[ k - 1 ] <= tol ) {
info <- info
} else {
info <- rbind(info, mat[ina, ] )
sela <- c(sela, sel)
mat <- mat[-ina, , drop = FALSE]
moda[[ k ]] <- ma
}
} else info <- info
} else if ( stopping == "BIC" ) {
if ( mat[ina, 2] < threshold ) {
ma <- lm( target ~., data = dataset[, c(da, sela, sel) ], weights = wei, y = FALSE, model = FALSE )
tool[k] <- BIC( ma )
if ( tool[ k - 1] - tool[ k ] <= tol ) {
info <- info
} else {
info <- rbind(info, mat[ina, ] )
sela <- info[, 1]
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 & abs( tool[ k ] - tool[ k - 1 ] ) > tol & nrow(mat) > 0 ) {
k <- k + 1
pn <- p - k + 1
if ( ncores <= 1 ) {
for ( i in 1:pn ) {
ww <- lm( target ~., data = dataset[, c(da, sela, pa + mat[i, 1]) ], weights = wei, y = FALSE, model = FALSE )
tab <- anova( ww )
mat[i, 3] <- tab[pa + k, 4]
df1 <- tab[pa + k, 1] ; df2 = tab[pa + k + 1, 1]
mat[i, 2] <- pf( mat[i, 3], df1, df2, lower.tail = FALSE, log.p = TRUE )
}
} else {
cl <- makePSOCKcluster(ncores)
doParallel::registerDoParallel(cl)
mod <- foreach( i = 1:pn, .combine = rbind) %dopar% {
ww <- lm( target ~., data = dataset[, c(da, sela, pa + mat[i, 1]) ], weights = wei, y = FALSE, model = FALSE )
tab <- anova(ma, ww)
stat <- tab[2, 5]
df1 <- tab[2, 3] ; df2 = tab[2, 1]
pval <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE )
return( c( pval, stat ) )
}
stopCluster(cl)
mat <- cbind( mat[, 1], mod )
}
ina <- which.min(mat[, 2])
sel <- pa + mat[ina, 1]
if ( stopping == "BIC" ) {
if ( mat[ina, 2] < threshold ) {
ma <- lm( target ~., data = dataset[, c(da, sela, sel)], 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 <- c(sela, sel)
mat <- mat[-ina, , drop = FALSE]
moda[[ k ]] <- ma
}
} else info <- rbind(info, c( 1e300, 0, 0 ) )
} else if ( stopping == "adjrsq" ) {
if ( mat[ina, 2] < threshold ) {
ma <- lm( target ~., data = dataset[, c(da, sela, sel)], weights = wei, y = FALSE, model = FALSE )
tool[k] <- as.numeric( summary(ma)[[ 9 ]] )
if ( tool[ k ] - tool[ k - 1 ] <= 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
d <- length(moda)
if (d == 0) {
final <- lm( target ~., data = as.data.frame( iniset ), weights = wei, y = FALSE, model = FALSE )
} else {
final <- lm( target ~., data = dataset[, c(da, sela) ], weights = wei, y = FALSE, model = FALSE )
info <- info[1:d, , drop = FALSE ]
info <- cbind( info, tool[ 1:d ] )
colnames(info) <- c( "variables", "log.p-value", "stat", stopping )
rownames(info) <- info[, 1]
}
list( runtime = runtime, mat = t(mat), info = info, ci_test = "testIndReg", final = final )
}
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