README.Rmd

---
output: github_document
---

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

# R2spa

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`R2spa` is a free and open-source R package that performs two-stage path analysis (2S-PA). With 2S-PA, researchers can perform path analysis by first obtaining factor scores and then adjusting for measurement errors using estimates of observation-specific reliability or standard error of those factor scores. As a viable alternative to SEM, 2S-PA has been shown to give equally-good estimates as SEM in relatively simple models and large sample sizes, as well as to give more accurate parameter estimates, has better control of Type I error rates, and has substantially less convergence problems in more complex models or small sample sizes.

## Installation

This package is still in developmental stage and can be installed on GitHub with:

```{r eval=FALSE}
# install.packages("remotes")
remotes::install_github("Gengrui-Zhang/R2spa")
```

## Example

```{r message=FALSE}
library(lavaan)
library(R2spa)

# Joint model
model <- '
  # latent variable definitions
    ind60 =~ x1 + x2 + x3
    dem60 =~ y1 + y2 + y3 + y4

  # regression
    dem60 ~ ind60
'
```

```{r}
# 2S-PA
# Stage 1: Get factor scores and standard errors for each latent construct
fs_dat_ind60 <- get_fs(data = PoliticalDemocracy,
                       model = "ind60 =~ x1 + x2 + x3")
fs_dat_dem60 <- get_fs(data = PoliticalDemocracy,
                       model = "dem60 =~ y1 + y2 + y3 + y4")
fs_dat <- cbind(fs_dat_ind60, fs_dat_dem60)

# get_fs() gives a dataframe with factor scores and standard errors
head(fs_dat)
```

```{r}
# Stage 2: Perform 2S-PA
tspa_fit <- tspa(
  model = "dem60 ~ ind60",
  data = fs_dat,
  se_fs = list(ind60 = 0.1213615, dem60 = 0.6756472)
)
parameterestimates(tspa_fit)
```

This package is based upon work supported by the National Science Foundation under Grant No. 2141790.

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