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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 with any bugs or issues.
See our website at piecewiseSEM
There is an online resource available for SEM, including
piecewiseSEM and lavaan, available 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
# 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)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.