fit_glm: Function that fits generalized linear models

In mfp2: Multivariable Fractional Polynomial Models with Extensions

Function that fits generalized linear models

Description

Function that fits generalized linear models

Usage

fit_glm(x, y, family, weights, offset, fast = TRUE)

Arguments

x	a matrix of predictors with nobs observations.
y	a vector for the outcome variable.
family	a family function e.g. stats::gaussian().
weights	a numeric vector of length nobs of 'prior weights' to be used in the fitting process. see stats::glm() for details.
offset	a numeric vector of length nobs of of a priori known component to be included in the linear predictor during fitting.
fast	a logical which determines how the model is fitted. The default TRUE uses fast fitting routines (i.e. stats::glm.fit()), while FALSE uses the normal fitting routines (stats::glm()) (used for the final output of mfp2). The difference is mainly due to the fact that normal fitting routines have to handle data.frames, which is a lot slower than using the model matrix and outcome vectors directly.

Value

A list with the following components:

• logl: the log likelihood of the fitted model.

• coefficients: regression coefficients.

• df: number of parameters (degrees of freedom).

• sse: residual sum of squares.

• fit: the fitted model object.

mfp2 documentation built on Nov. 15, 2023, 1:06 a.m.