Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jslefche/piecewiseSEM
Piecewise Structural Equation Modeling in R
https://github.com/jslefche/piecewiseSEM
r sem
Last synced: about 1 month ago
JSON representation
Piecewise Structural Equation Modeling in R
- Host: GitHub
- URL: https://github.com/jslefche/piecewiseSEM
- Owner: jslefche
- Created: 2014-08-04T13:55:07.000Z (over 10 years ago)
- Default Branch: main
- Last Pushed: 2024-06-11T13:09:24.000Z (7 months ago)
- Last Synced: 2024-08-06T03:04:27.800Z (5 months ago)
- Topics: r, sem
- Language: R
- Homepage:
- Size: 2.73 MB
- Stars: 152
- Watchers: 14
- Forks: 48
- Open Issues: 60
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# piecewiseSEM: Piecewise Structural Equation Modeling in R
[](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)
```