Linear Regression and Logistic Regression with Missing Covariates
combinations
Function for imputing single point for linear regression model
Initialize Missing Data in Data Frame
miss.glm.model.select
likelihood_saem
log_reg
louis_lr_saem
Auxiliary for Controlling Fitting
Fitting Logistic Regression Models with Missing Values
Statistical Inference for Logistic Regression Models with Missing Valu...
Auxiliary for Controlling Fitting
Fitting Linear Regression Model with Missing Values
miss.lm.model.select
Statistical Inference for Linear Regression Models with Missing Values
Prediction on test with missing values for the logistic regression mod...
Prediction on test with missing values for the linear regression model...
Print miss.glm
Print miss.lm
Print Summary of miss.glm
Print Summary of miss.lm
Summarizing Fits for miss.glm
Summarizing Fits for miss.lm
Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.