regression_types: Types of regression models

In mkearney/tidyversity: Tidy Tools for Academics

Description

Conducts regression analysis to model outcome variable using OLS

Conducts logistic regression analysis to model binary outcome variable using a generalized (binomial with logit link) linear model

Conducts logistic regression analysis to model approximations of binary outcome variables (doesn't have to be 2 levels) using a generalized (quasi-binomial with logit link) linear model

Conducts poisson regression to model count outcome variables using a generalized (poisson with logit link) linear model.

Conducts poisson regression analysis to model approximations of count outcome variables (doesn't have to be integers) using a generalized (quasi-poisson with logit link) linear model

Conducts negative binomial regression (generalized linear models for overdispersed count data)

Usage

ols_regression(data, model, robust = FALSE, ...)

logistic_regression(data, model, robust = FALSE, ...)

quasilogistic_regression(data, model, robust = FALSE, ...)

poisson_regression(data, model, robust = FALSE, ...)

quasipoisson_regression(data, model, robust = FALSE, ...)

negbinom_regression(data, model, robust = FALSE, ...)

Arguments

data	Data frame containing variables in model
model	Model formula to be estimated.
robust	Logical indicating whether to estimate a robust model. This is available for all models but negative binomial.
...	Other arguments passed to modeling function.

Details

Available types of regression models.

mkearney/tidyversity documentation built on May 28, 2019, 2:32 a.m.