#' The function for imputation of continuous variables.
#'
#' The function is called by the wrapper (hmi). It uses \code{mice} with the method "norm".
#' @param y_imp A vector with the variable to impute.
#' @param X_imp A data.frame with the fixed effects variables.
#' @param pvalue A numeric between 0 and 1 denoting the threshold of p-values a variable in the imputation
#' model should not exceed. If they do, they are excluded from the imputation model.
#' @param k An integer defining the allowed maximum of levels in a factor covariate.
#' @return A n x 1 data.frame with the original and imputed values.
imp_cont_single <- function(y_imp,
X_imp,
pvalue = 0.2,
k = Inf){
#the missing indactor indicates, which values of y are missing.
missind <- is.na(y_imp)
# ----------------------------- preparing the X data ------------------
# remove excessive variables
X_imp <- cleanup(X_imp, k = k)
# standardise the covariates in X (which are numeric and no intercept)
X <- stand(X_imp)
n <- length(y_imp)
# ----------- set up a maximal model matrix with all possible relevant (dummy) variables -----
# In the imputation model only actually relevant (dummy) variables shall be present.
# THis is done by setting up a mirror of the initial model matrix.
# Then step by step this model matrix is reduced to all actually relevant (dummy) variables.
# This reduction is based on models using the observed data.
# The last step prior to the imputation-parameters estimation is to restrict the initial mode matrix
# to those variables, left in the reduced mirror model matrix.
#define a place holder (ph)
ph <- sample_imp(y_imp)[, 1]
y_mean <- mean(ph, na.rm = TRUE)
y_sd <- stats::sd(ph, na.rm = TRUE)
ph <- (ph - y_mean)/y_sd + 1
tmp_0_all <- data.frame(target = ph, X)
xnames_1 <- colnames(X)
tmp_formula <- paste("target~ 0 + ", paste(xnames_1, collapse = "+"), sep = "")
reg_1_all <- stats::lm(stats::formula(tmp_formula), data = tmp_0_all)
X_model_matrix_1_all <- stats::model.matrix(reg_1_all)
xnames_1 <- paste("X", 1:ncol(X_model_matrix_1_all), sep = "")
colnames(X_model_matrix_1_all) <- xnames_1
tmp_0_all <- data.frame(target = ph)
tmp_0_all[, xnames_1] <- X_model_matrix_1_all
#From this initial model matrix X_model_matrix_1_all
#now step by step irrelavant variables are removed.
X_model_matrix_1_sub <- X_model_matrix_1_all[!missind, , drop = FALSE]
# The first step of the reduction is to remove variables having a non-measurable effect
# (e.g. due to colinearity) on y.
# tmp_1 shall include the covariates (like X_model_matrix) and additionally the target variable
ph_sub <- ph[!missind]
tmp_1_sub <- data.frame(target = ph_sub)
xnames_1 <- colnames(X_model_matrix_1_sub)
tmp_1_sub[, xnames_1] <- X_model_matrix_1_sub
tmp_formula <- paste("target~ 0 + ", paste(xnames_1, collapse = "+"), sep = "")
reg_1_sub <- stats::lm(stats::formula(tmp_formula) , data = tmp_1_sub)
#remove unneeded variables
X_model_matrix_1_sub <- X_model_matrix_1_sub[, !is.na(stats::coefficients(reg_1_sub)),
drop = FALSE]
# Remove insignificant variables from the imputation model
check <- TRUE
while(check){
tmp_1_sub <- data.frame(target = ph_sub)
xnames_1 <- colnames(X_model_matrix_1_sub)
tmp_1_sub[, xnames_1] <- X_model_matrix_1_sub
tmp_formula <- paste("target~ 0 + ", paste(xnames_1, collapse = "+"), sep = "")
reg_1_sub <- stats::lm(stats::formula(tmp_formula), data = tmp_1_sub)
pvalues <- summary(reg_1_sub)$coefficients[, 4]
insignificant_variables <- which(pvalues > pvalue)
most_insignificant <- insignificant_variables[which.max(pvalues[insignificant_variables])]
if(length(most_insignificant) == 0){
check <- FALSE
}else{
#.. drop the insignificant variable from the model.matrix, but only if at least 1 variable remains
tmp_MM <- stats::model.matrix(reg_1_sub)[, -most_insignificant, drop = FALSE]
if(ncol(tmp_MM) == 0){
check <- FALSE
}else{
X_model_matrix_1_sub <- tmp_MM
}
}
}
tmp_2_all <- tmp_0_all[, colnames(tmp_1_sub), drop = FALSE]
tmp_2_all$target[missind] <- NA
everything <- mice::mice(data = tmp_2_all, m = 1,
method = "norm",
predictorMatrix = (1 - diag(1, ncol(tmp_2_all))),
visitSequence = (1:ncol(tmp_2_all))[apply(is.na(tmp_2_all),2,any)],
post = vector("character", length = ncol(tmp_2_all)),
defaultMethod = "norm",
maxit = 10,
diagnostics = TRUE,
printFlag = FALSE,
seed = NA,
imputationMethod = NULL,
defaultImputationMethod = NULL,
data.init = NULL)
y_ret <- data.frame(y_ret = y_imp)
y_ret[missind, 1] <- (everything$imp[[1]][, 1] - 1) * y_sd + y_mean
return(y_ret)
}
# Generate documentation with devtools::document()
# Build package with devtools::build() and devtools::build(binary = TRUE) for zips
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