start random forest imputation

create a simple imputation function which
uses ranger::ranger() for imputing values

adresses #26

R/randomForestImp.R

@@ -0,0 +1,41 @@
+#' Random Forest Imputation
+#'
+#' Impute missing values based on a random forest model.
+#' @param formula model formula to impute one variable
+#' @param data A data.frame or survey object containing the data
+#' @param imp_var `TRUE`/`FALSE` if a `TRUE`/`FALSE` variables for each imputed
+#' variable should be created show the imputation status
+#' @param imp_suffix suffix used for TF imputation variables
+#' @examples 
+#' data(sleep)
+#' randomForestImp_work(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
+#' @export
+randomForestImp_work <- function(formula, data, imp_var = TRUE,
+                                 imp_suffix = "imp") {
+  formchar <- as.character(formula)
+  lhs <- gsub(" ", "", strsplit(formchar[2], "\\+")[[1]])
+  rhs <- formchar[3]
+  rhs2 <- gsub(" ", "", strsplit(rhs, "\\+")[[1]])
+  #Missings in RHS variables
+  rhs_na <- apply(subset(data, select = rhs2), 1, function(x) any(is.na(x)))
+  for (lhsV in lhs) {
+    form <- as.formula(paste(lhsV, "~", rhs))
+    lhs_vector <- data[[lhsV]]
+    lhs_na <- is.na(lhs_vector)
+    mod <- ranger::ranger(form, subset(data, !rhs_na & !lhs_na))
+    predictions <- predict(mod, subset(data, !rhs_na & lhs_na))$predictions
+    data[!rhs_na & lhs_na, lhsV] <- predictions
+
+    if (imp_var) {
+      if (imp_var %in% colnames(data)) {
+        data[, paste(lhsV, "_", imp_suffix, sep = "")] <- as.logical(data[, paste(lhsV, "_", imp_suffix, sep = "")])
+        warning(paste("The following TRUE/FALSE imputation status variables will be updated:",
+                      paste(lhsV, "_", imp_suffix, sep = "")))
+      } else {
+        data$NEWIMPTFVARIABLE <- is.na(lhs_vector)
+        colnames(data)[ncol(data)] <- paste(lhsV, "_", imp_suffix, sep = "")
+      }
+    }
+  }
+  data
+}