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

Last synced: 3 months ago
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🎓 Tidy regression tools for academics

• Host: GitHub
• URL: https://github.com/mkearney/tidyreg
• Owner: mkearney
• Created: 2018-06-02T21:38:35.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-08-02T21:03:03.000Z (almost 6 years ago)
• Last Synced: 2024-01-19T14:22:50.719Z (6 months ago)
• Topics: generalized-linear-models, linear-models, quantitative-methods, regression, statistics, tidyversity
• Language: R
• Homepage:
• Size: 541 KB
• Stars: 23
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

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• awesome-stars - mkearney/tidyreg - 🎓 Tidy regression tools for academics (R)

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()

```