https://github.com/jslefche/piecewiseSEM

Piecewise Structural Equation Modeling in R
https://github.com/jslefche/piecewiseSEM

r sem

Last synced: about 2 months ago
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Piecewise Structural Equation Modeling in R

• Host: GitHub
• URL: https://github.com/jslefche/piecewiseSEM
• Owner: jslefche
• Created: 2014-08-04T13:55:07.000Z (about 10 years ago)
• Default Branch: main
• Last Pushed: 2024-06-11T13:09:24.000Z (3 months ago)
• Last Synced: 2024-06-12T01:38:22.701Z (3 months ago)
• Topics: r, sem
• Language: R
• Homepage:
• Size: 2.73 MB
• Stars: 147
• Watchers: 14
• Forks: 48
• Open Issues: 58
• Metadata Files:
  • Readme: README.md

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README

        
# piecewiseSEM: Piecewise Structural Equation Modeling in R

[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/piecewiseSEM)](https://cran.r-project.org/package=piecewiseSEM)

## Version 2.3.0.1

## Last updated: 11 June 2024

## To install

Run the following code to install the latest version from CRAN:

```

install.packages("piecewiseSEM")

```

Run the following code to install the development version:

```

devtools::install_github("jslefche/piecewiseSEM@devel")

```

Note: the development version may be unstable and lead to unanticipated bugs.

Contact the package developer via Github with any bugs or issues.

## Getting Help

See our website at [piecewiseSEM](http://jslefche.github.io/piecewiseSEM/)

There is an online resource available for SEM, including `piecewiseSEM` and `lavaan`, available [https://jslefche.github.io/sem_book/](https://jslefche.github.io/sem_book/)

Version 2 is a major update to the `piecewiseSEM` package that uses a completely revised syntax that better reproduces the base R syntax and output. It is highly recommended that consult the resource above even if you have used the package before as it documents the many changes.

Currently supported model classes: `lm, glm, gls, Sarlm, lme, glmmPQL, lmerMod, merModLmerTest, glmerMod. glmmTMB, gam`

### Example

```

# Load library

library(piecewiseSEM)

# Create fake data

set.seed(1)

data <- data.frame(

  x = runif(100),

  y1 = runif(100),

  y2 = rpois(100, 1),

  y3 = runif(100)

)

# Create SEM using `psem`

modelList <- psem(

  lm(y1 ~ x, data),

  glm(y2 ~ x, "poisson", data),

  lm(y3 ~ y1 + y2, data),

  data

)

# Run summary

summary(modelList)

# Address conflict using conserve = T

summary(modelList, conserve = T)

# Address conflict using direction = c()

summary(modelList, direction = c("y2 <- y1"))

# Address conflict using correlated errors

modelList2 <- update(modelList, y2 %~~% y1)

summary(modelList2)

```