Nothing
# target: the target value
# sesObject: the outcome of the ses
# nisgnat: Number of signatures and generated models. It could be numeric from 1 to total number of signatures or "all" for all the
## signatures. Default is 1.
mmpc.glmm.model = function(target, dataset, reps = NULL, group, slopes = FALSE, wei = NULL, mmpcglmm.Object, test = NULL) {
if ( sum( is.na(mmpcglmm.Object@selectedVars) ) > 0 ) {
mod <- paste("No associations were found, hence no model is produced.")
signature <- NULL
res <- list(mod = mod, signature = signature)
} else {
if ( any(is.na(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")
dataset <- apply( dataset, 2, function(x){ x[which(is.na(x))] = median(x, na.rm = TRUE) ; return(x) } )
}
if ( is.null(test) ) {
ci_test <- mmpcglmm.Object@test
slopes <- mmpcglmm.Object@slope
} else ci_test <- test
signature <- mmpcglmm.Object@selectedVars
if ( ci_test == "testIndGLMMLogistic" ) {
if ( is.null(reps) ) {
mod <- lme4::glmer( target ~ dataset[, signature] + (1|group), weights = wei, family = binomial )
} else {
reps <- reps
if (slopes ) {
mod <- lme4::glmer( target ~ reps + dataset[, signature] + (reps|group), weights = wei, family = binomial )
} else mod <- lme4::glmer( target ~ reps + dataset[, signature] + (1|group), weights = wei, family = binomial )
}
} else if ( ci_test == "testIndGLMMPois" ) {
if ( is.null(reps) ) {
mod <- lme4::glmer( target ~ dataset[, signature] + (1|group), weights = wei, family = poisson )
} else {
reps <- reps
if (slopes ) {
mod <- lme4::glmer( target ~ reps + dataset[, signature] + (reps|group), weights = wei, family = poisson )
} else mod <- lme4::glmer( target ~ reps + dataset[, signature] + (1|group), weights = wei, family = poisson )
}
} else if ( ci_test == "testIndGLMMGamma" ) {
if ( is.null(reps) ) {
mod <- lme4::glmer( target ~ dataset[, signature] + (1|group), weights = wei, family = Gamma(log) )
} else {
reps <- reps
if (slopes ) {
mod <- lme4::glmer( target ~ reps + dataset[, signature] + (reps|group), weights = wei, family = Gamma(log) )
} else mod <- lme4::glmer( target ~ reps + dataset[, signature] + (1|group), weights = wei, family = Gamma(log) )
}
} else if ( ci_test == "testIndGLMMNormLog" ) {
if ( is.null(reps) ) {
mod <- lme4::glmer( target ~ dataset[, signature] + (1|group), weights = wei, family = gaussian(log) )
} else {
reps <- reps
if (slopes ) {
mod <- lme4::glmer( target ~ reps + dataset[, signature] + (reps|group), weights = wei, family = gaussian(log) )
} else mod <- lme4::glmer( target ~ reps + dataset[, signature] + (1|group), weights = wei, family = gaussian(log) )
}
} else if ( ci_test == "testIndGLMMOrdinal" ) {
mod <- ordinal::clmm( target ~ dataset[, signature] + (1|group), weights = wei )
} else if ( ci_test == "testIndGLMMCR" ) {
mod <- coxme::coxme( target ~ dataset[, signature] + (1|group), weights = wei )
} else if ( ci_test == "testIndGLMMReg" | ci_test == "testIndLMM" ) {
if ( is.null(reps) ) {
mod <- lme4::lmer( target ~ dataset[, signature] + (1|group), weights = wei, REML = FALSE )
} else {
reps <- reps
if ( slopes ) {
mod <- lme4::lmer( target ~ reps + dataset[, signature] + (reps|group), weights = wei, REML = FALSE )
} else {
reps <- reps
mod <- lme4::lmer( target ~ reps + dataset[, signature] + (1|group), weights = wei, REML = FALSE )
}
}
}
bic <- BIC(mod)
if ( is.null( colnames(dataset) ) ) {
names(signature) = paste("Var", signature, sep = " ")
} else names(signature) = colnames(dataset)[signature]
signature <- c(signature, bic)
names(signature)[length(signature)] = "bic"
res <- list(mod = mod, signature = signature)
} ## if ( sum( is.na(mmpcglmm.Object@selectedVars) ) > 0 ) {
}
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