README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{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
```
# tidysem <img src="man/figures/logo.png" width="160px" align="right" /> 

[![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

<!-- You can install the released version of tidysem from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("tidysem")
```
-->

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

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

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

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

## load tidysem
library(tidysem)
```


## Latent variable models

### Structural equation modeling (SEM)

Conduct latent variable analysis using structural equation modeling.

```{r sem}
## mutate data and then specify and estimate model
sem1 <- polcom %>%
  mutate(therm_2 = therm_2 / 10, 
    therm_1 = 10 - therm_1 / 10) %>%
  tidy_sem_model(news =~ news_1 + news_2 + news_3 + news_4 + news_5 + news_6,
    ambiv_sexism =~ ambiv_sexism_1 + ambiv_sexism_2 + ambiv_sexism_3 + 
      ambiv_sexism_4 + ambiv_sexism_5 + ambiv_sexism_6,
    partisan =~ a*therm_1 + a*therm_2,
    ambiv_sexism ~ age + sex + hhinc + edu + news + partisan) %>%
  tidy_sem()

## print model summary
sem1 %>%
  tidy_summary()
```