#' The function for hierarchical imputation of continuous variables.
#'
#' The function is called by the wrapper.
#' @param y_imp A Vector with the variable to impute.
#' @param X_imp A data.frame with the fixed effects variables.
#' @param Z_imp A data.frame with the random effects variables.
#' @param clID A vector with the cluster ID.
#' @param nitt An integer defining number of MCMC iterations (see MCMCglmm).
#' @param thin An integer defining the thinning interval (see MCMCglmm).
#' @param burnin An integer defining the percentage of draws from the gibbs sampler
#' that should be discarded as burn in (see MCMCglmm).
#' @return A n x 1 matrix with the original and imputed values.
imp_cont_multi <- function(y_imp,
X_imp,
Z_imp,
clID,
nitt = 3000,
thin = 10,
burnin = 1000){
# -----------------------------preparing the data ------------------
# -- standardise the covariates in X (which are numeric and no intercept)
# ----------------------------- preparing the X and Z data ------------------
# remove excessive variables
X_imp <- remove_excessives(X_imp)
# standardise the covariates in X (which are numeric and no intercept)
X_imp_stand <- stand(X_imp)
# -- standardise the covariates in Z (which are numeric and no intercept)
Z_imp_stand <- stand(Z_imp)
#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_stand <- (ph - y_mean)/y_sd + 1
#Z may contain factor variables. Later we need Z to contain numeric variables,
# so we have to change those variables.
tmp <- data.frame(target = ph_stand, Z_imp_stand)
#remove intercept variable
tmp <- tmp[, get_type(tmp) != "intercept", drop = FALSE]
Z_imp_stand2 <- stats::model.matrix(stats::lm("target ~ 1 + .", data = tmp))
missind <- is.na(y_imp)
n <- nrow(X_imp_stand)
tmp_0_sub <- data.frame(target = ph_stand, X_imp_stand)[!missind, , drop = FALSE]
tmp_0_all <- data.frame(target = ph_stand, X_imp_stand)
X_model_matrix_1_sub <- stats::model.matrix(target ~ 0 + ., data = tmp_0_sub)
X_model_matrix_1_all <- stats::model.matrix(target ~ 0 + ., data = tmp_0_all)
colnames(X_model_matrix_1_sub) <- gsub("`", "", colnames(X_model_matrix_1_sub))
colnames(X_model_matrix_1_all) <- gsub("`", "", colnames(X_model_matrix_1_all))
# remove unneeded variables/categories from X_model_matrix_1
# model to determine unnneeded variables
reg_1_sub <- stats::lm(target ~ 0 + ., data = tmp_0_sub)
unneeded <- is.na(stats::coefficients(reg_1_sub))
#data, where the needed variables should be stored
xnames_1 <- paste("X", 1:ncol(X_model_matrix_1_all), sep = "")
znames_1 <- paste("Z", 1:ncol(Z_imp_stand2), sep = "")
xnames_2 <- xnames_1[!unneeded]
znames_2 <- znames_1
X_model_matrix_2_all <- X_model_matrix_1_all[, !unneeded, drop = FALSE]
tmp_2_all <- data.frame(target = ph_stand)
tmp_2_all[, xnames_2] <- X_model_matrix_2_all
tmp_2_all[, znames_2] <- Z_imp_stand2
tmp_2_all[, "ClID"] <- clID
tmp_2_sub <- tmp_2_all[!missind, , drop = FALSE]
# -------------- calling the gibbs sampler to get imputation parameters----
tmp_3_sub <- tmp_2_sub
intercept_variables <- get_type(tmp_3_sub) == "intercept"
tmp_3_sub <- tmp_2_sub[, !intercept_variables, drop = FALSE]
xnames_3 <- xnames_2[!xnames_2 %in% names(intercept_variables)[intercept_variables]]
znames_3 <- znames_2[!znames_2 %in% names(intercept_variables)[intercept_variables]]
fixformula <- stats::formula(paste("target~ 1+ ", paste(xnames_3, collapse = "+"), sep = ""))
randformula <- stats::as.formula(paste("~us(1+", paste(znames_3, collapse = "+"), "):ClID", sep = ""))
prior <- list(R = list(V = 1, nu = 0.002), # alternatice: R = list(V = 1e-07, nu = -2)
G = list(G1 = list(V = diag(ncol(Z_imp_stand2)), nu = 0.002)))
MCMCglmm_draws <- MCMCglmm::MCMCglmm(fixformula, random = randformula, data = tmp_3_sub,
verbose = FALSE, pr = TRUE, prior = prior,
saveX = TRUE, saveZ = TRUE,
nitt = 10000,
thin = 100,
burnin = 5000)
# Get the number of random effects variables
n.par.rand <- ncol(Z_imp_stand2)
ncluster <- length(table(tmp_2_sub$ClID))
length.alpha <- ncluster * n.par.rand
pointdraws <- MCMCglmm_draws$Sol
xdraws <- pointdraws[, 1:ncol(X_model_matrix_2_all), drop = FALSE]
#If a cluster cannot has random effects estimates, because there too few observations,
#we make them 0.
empty_cluster <- which(table(tmp_2_sub$ClID) == 0)
zdraws_pre <- pointdraws[, (ncol(X_model_matrix_2_all) + 1):ncol(pointdraws), drop = FALSE]
#go through all random effects
#(e.g. first the random intercepts, then the random slope of X1, then the random slope of X5)
for(l1 in 1:n.par.rand){
#go through all clusters with 0 observations
for(l2 in empty_cluster){
zdraws_pre <- cbind(zdraws_pre[, 0:((l1-1)* ncluster + (l2-1))], 0,
zdraws_pre[, ((l1-1)* ncluster + l2):ncol(zdraws_pre)])
}
}
zdraws <- zdraws_pre
variancedraws <- MCMCglmm_draws$VCV
# the last column contains the variance (not standard deviation) of the residuals
number_of_draws <- nrow(pointdraws)
select.record <- sample(1:number_of_draws, 1, replace = TRUE)
# -------------------- drawing samples with the parameters from the gibbs sampler --------
###start imputation
rand.eff.imp <- matrix(zdraws[select.record, ], ncol = n.par.rand)
fix.eff.imp <- matrix(xdraws[select.record, ], nrow = ncol(X_model_matrix_2_all))
sigma.y.imp <- sqrt(variancedraws[select.record, ncol(variancedraws)])
y_temp <- stats::rnorm(n, X_model_matrix_2_all %*% fix.eff.imp +
apply(Z_imp_stand2 * rand.eff.imp[clID,], 1, sum), sd = sigma.y.imp)
y_ret <- matrix(ifelse(missind, (y_temp - 1) * y_sd + y_mean, y_imp), ncol = 1)
# --------- returning the imputed data --------------
return(y_ret)
}
# Generate documentation with devtools::document()
# Build package with devtools::build() and devtools::build(binary = TRUE) for zips
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