miss.glm.fit: Fitting Logistic Regression Models with Missing Values
In misaem: Linear Regression and Logistic Regression with Missing Covariates

miss.glm.fit

R Documentation

Fitting Logistic Regression Models with Missing Values

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

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

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

a list with following components:

`coefficients`	Estimated `\beta`.
`ll`	Observed log-likelihood.
`var.covar`	Variance-covariance matrix for estimated parameters.
`s.err`	Standard error for estimated parameters.
`mu.X`	Estimated `\mu`.
`Sig.X`	Estimated `\Sigma`.