fiMI {DLMRMV} | R Documentation |
fiMI: Predict Missing Response Variables using Multiple Imputation
Description
This function predicts missing response variables in a linear regression dataset
using multiple imputation. It leverages the FimIMI
function to perform multiple runs
of improved multiple imputation and averages the regression coefficients to predict
the missing response values.
Usage
fiMI(data, R, n, M)
Arguments
data |
|
R |
Number of runs for multiple imputation. |
n |
Number of rows in the dataset. |
M |
Number of multiple imputations per run. |
Details
This function assumes that the first column of data
is the response variable
and the remaining columns are the independent variables. The function uses the FimIMI
function to perform multiple runs of improved multiple imputation and averages the
regression coefficients to predict the missing response values.
Value
A list containing:
Yhat |
Predicted response values with missing values imputed. |
Examples
# Example data
set.seed(123)
n <- 1000 # Number of rows
p <- 5 # Number of independent variables
data <- data.frame(Y = rnorm(n), X1 = rnorm(n), X2 = rnorm(n))
data[sample(n, 100), 1] <- NA # Introduce missing response values
# Call fiMI function
result <- fiMI(data, R = 10, n = n, M = 20)
# View results
print(result$Yhat) # Predicted response values