FBMS (version 1.3)

impute_x_pred: Impute Missing Values in Test Data Using Training Data

Description

Imputes missing values in the test data using median imputation based on the training set.

Usage

impute_x_pred(object, x_test, x_train)

Value

A matrix with imputed values and additional columns for missingness indicators.

Arguments

object

A fitted model object with an "imputed" attribute indicating columns to impute.

x_test

A matrix or data frame containing the test data.

x_train

A matrix or data frame containing the training data.

Examples

# \donttest{
set.seed(123)
x_test <- matrix(rnorm(60), 10, 6)
colnames(x_test) <- paste0("X", 1:6)
x_test[1:2, 1] <- NA  # Introduce missing values
x_train <- matrix(rnorm(300), 50, 6)
colnames(x_train) <- paste0("X", 1:6)
model <- list(imputed = c(1))
attr(model, "imputed") <- c(1)
x_imputed <- impute_x_pred(model, x_test, x_train)
dim(x_imputed)  # 10 rows, 7 columns (6 original + 1 missingness indicator)
any(is.na(x_imputed))  # FALSE, no missing values
# }