lrm_model: Logistic Regression Model

Description Usage Arguments Value Examples

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

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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

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

yangx227/SimmonsResearchR documentation built on Oct. 5, 2017, 4:09 p.m.