lrm_model: Logistic Regression Model

In yangx227/SimmonsResearchR: An implementation of R functions used in Stat team.

Logistic Regression Model

Description

Fit binary logistic regression models using MLE or penalized MLE. All the dependent variables are modeled using selected independent variables automatically. Multi-colliearity is removed in the final models using correlation analysis.

Usage

lrm_model(data = data, DVList = DVList, IDVList = IDVList,
  Included = NULL, all = FALSE, parallel = FALSE)

Arguments

data	The data. It must contain the variables (columns) that should be used, directly or indirectly, in the modelling procedures. Missing values (NA) are allowed.
DVList	Dependent variables list which are needed to be modelled.
IDVList	Independent variables list used to model dependent variables.
Included	A variable list whose elements are forced to be included in the models, no matter variable selection is conducted or not. Default: NULL.
all	Logical. If TRUE, the function will not do variables selection, only model fitting will be done. Default: FALSE.

Value

A list data structure which contains model results.

Examples

model.Psyco <- lrm_model(data=ModelBase.new, 
                         DVList=DVList,
                         IDVList=PsycoVars)

yangx227/SimmonsResearchR documentation built on April 24, 2022, 6:44 a.m.