In mkearney/tidyreg: Tidy Regression Tools for Academics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  eval = TRUE
)
options(width = 100)
polcom <- tidyversity::polcom

tidyreg

🎓 Tidy tools for academics

* This package is in very early development. Feedback is encouraged!!! *

Installation

Install the development version from Github with:

## install devtools if not already
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
## install tidyreg from Github
devtools::install_github("mkearney/tidyreg")

Load the package (it, of course, plays nicely with tidyverse).

## load tidyverse
library(tidyverse)

## load tidyreg
library(tidyreg)

Regression models

Ordinary Least Squares (OLS)

Conduct an Ordinary Least Squares (OLS) regression analysis.

polcom %>%
  tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1) %>%
  tidy_summary()

Logistic (dichotomous)

Conduct a logistic regression analysis for binary (dichotomous) outcomes.

polcom %>%
  tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1, type = "logistic") %>%
  tidy_summary()

Poisson (count)

Conduct a poisson regression analysis for count data.

polcom %>%
  mutate(polarize = abs(therm_1 - therm_2)) %>%
  tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "poisson") %>%
  tidy_summary()

Negative binomial (overdispersed)

Conduct a negative binomial regression analysis for overdispersed count data.

polcom %>%
  mutate(polarize = abs(therm_1 - therm_2)) %>%
  tidy_regression(polarize ~ news_1 + ambiv_sexism_1, type = "negbinom") %>%
  tidy_summary()

Robust and quasi- models

polcom %>%
  mutate(polarize = abs(therm_1 - therm_2)) %>%
  tidy_regression(polarize ~ news_1 + ambiv_sexism_1, 
    type = "quasipoisson", robust = TRUE) %>%
  tidy_summary()