PEE {vsmi}

Penalized estimating equations for generalized linear models with multiple imputation

Description

This is a function to impute missing data, estimate coefficients of generalized linear models and select variables for multiple imputed data sets, considering the correlation of multiple imputed observations.

Usage

PEE(
  missdata,
  mice_time = 5,
  penalty,
  lamda.vec = seq(1, 4, length.out = 12),
  Gamma = c(0.5, 1, 1.5)
)

Arguments

missdata

A Matrix,missing data with variables X in the first p columns and response Y at the last column.

mice_time

an integer, number of imputation.

penalty

The method for variable selection,choose from "lasso" or "alasso".

lamda.vec

Optimal tuning parameter for penalty,default seq(1,4,length.out=12).

Gamma

Parameter for adjustment of the Adaptive Weights vector in adaptive LASSO,default c(0.5,1,1.5).

Value

A Vsmi_est object, contians estcoef and index_sig , estcoef for estimate coefficients and index_sig for selected variable index.

Examples

library(MASS)
library(mice)
library(qif)

data_with_missing <- generate_pee_missing_data(outcome="binary")
est.alasso <-PEE(data_with_missing,penalty="alasso")
est.lasso <-PEE(data_with_missing,penalty="lasso")

count_data_with_missing <- generate_pee_missing_data(outcome="count")
count_est.alasso <-PEE(data_with_missing,penalty="alasso")
count_est.lasso <-PEE(data_with_missing,penalty="lasso")