CSLMI: CSLMI: Consensus-based Stochastic Linear Multiple Imputation...

In DLMRMV: Distributed Linear Regression Models with Response Missing Variables

CSLMI: Consensus-based Stochastic Linear Multiple Imputation (Simplified Version)

Description

Performs multiple imputation and parameter estimation using a consensus-based approach. The response variable is in the first column, all other columns are predictors, missing values are automatically detected, the whole dataset is treated as one block.

Usage

CSLMI(data, M)

Arguments

data	Dataframe with response variable in 1st column and predictors in others
M	Number of imputations

Value

A list containing:

Yhat	Imputed response values.
betahat	Average regression coefficients across imputations.
comm	Communication cost (number of messages passed).

A list containing the following components:

Yhat	Imputed response vector with missing values filled in.
betahat	Final regression coefficients.

Examples

set.seed(123)
data <- data.frame(
  y = c(rnorm(50), rep(NA, 10)),
  x1 = rnorm(60),
  x2 = rnorm(60)
)
result <- CSLMI(data = data, M = 10)
head(result$Yhat)
print(result$betahat)
print(result$comm)

DLMRMV documentation built on Aug. 8, 2025, 6:27 p.m.