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https://github.com/mkearney/tidyreg

🎓 Tidy regression tools for academics
https://github.com/mkearney/tidyreg

generalized-linear-models linear-models quantitative-methods regression statistics tidyversity

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🎓 Tidy regression tools for academics

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README

        

---
output: github_document
---

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

[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

🎓 Tidy tools for academics

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

## Installation

Install the development version from [Github](https://github.com/mkearney/tidyreg) with:

```{r install, eval=FALSE}
## 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).

```{r library}
## load tidyverse
library(tidyverse)

## load tidyreg
library(tidyreg)
```

## Regression models

### Ordinary Least Squares (OLS)

Conduct an Ordinary Least Squares (OLS) regression analysis.

```{r ols}
polcom %>%
tidy_regression(follow_trump ~ news_1 + ambiv_sexism_1) %>%
tidy_summary()
```

### Logistic (dichotomous)

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

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

### Poisson (count)

Conduct a poisson regression analysis for count data.

```{r poisson}
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.

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

### Robust and quasi- models

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