miss.glm.fit: Fitting Logistic Regression Models with Missing Values

In misaem: Linear Regression and Logistic Regression with Missing Covariates

Description

This function is used inside miss.glm to fit logistic regression model with missing values, by algorithm SAEM.

Usage

miss.glm.fit(x, y, control = list())

Arguments

x	design matrix with missingness N * p.
y	response vector N * 1.
control	a list of parameters for controlling the fitting process. For miss.glm.fit this is passed to miss.glm.control.

Value

a list with following components:

coefficients	Estimated β.
ll	Observed log-likelihood.
var.covar	Variance-covariance matrix for estimated parameters.
s.err	Standard error for estimated parameters.
mu.X	Estimated μ.
Sig.X	Estimated Σ.

Examples

## For examples see example(miss.glm)

Example output

Warning message:
replacing previous import ‘mice::filter’ by ‘stats::filter’ when loading ‘misaem’ 

misaem documentation built on April 12, 2021, 9:06 a.m.