stepwise_multivariate_model_selection: stepwise_multivariate_model_selection

In LewisLabUCSD/RegressionModelPipeline: Pipeline for feature selection, interaction assessment, regression modeling and prediction assessment for 1-1000s of features

Description

Multivariate model selection on all (significant) observations variables with respect to response.

Usage

stepwise_multivariate_model_selection(df, observations, response, family, model,
  interactions = FALSE, aic_k = 2, ...)

Arguments

df,	a data.frame containing response and observations variables. Factors with more than 2 levels have only been implimented for test='LRT'
observations,	a character vector of the names of independent/observations variables in df
response,	a character vector of the names of dependent/response variables in df
family,	a character string indicating the family associated with the submitted model c('gaussian','binomial','poisson'...)
model,	a model associated for testing the variables c(glm,lm)
interactions,	a boolean indicating if interactions should be assessed. Default is False.

Value

a multivariate model

LewisLabUCSD/RegressionModelPipeline documentation built on Jan. 11, 2021, 10:33 p.m.