Learn R Programming

⚠️There's a newer version (0.5-7) of this package.Take me there.

semTools (version 0.4-6)

Useful tools for structural equation modeling.

Description

This package provide useful tools for structural equation modeling analysis. Note that the OpenMx package can be obtained from the following URL: http://openmx.psyc.virginia.edu/

Copy Link

Version

Install

install.packages('semTools')

Monthly Downloads

16,422

Version

0.4-6

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/simsem/semTools/wiki

Maintainer

Sunthud Pornprasertmanit

Last Published

October 3rd, 2014

Functions in semTools (0.4-6)

Fraction of Missing Information.

Modification indices and their power approach for model fit evaluation

Finding excessive kurtosis

Plot power curves for RMSEA

findRMSEAsamplesizenested

Find sample size given a power in nested model comparison

findRMSEAsamplesize

Find the minimum sample size for a given statistical power based on population RMSEA

Multiply impute and analyze data using lavaan

Simulated Data set to Demonstrate Longitudinal Measurement Invariance

Find the statistical power based on population RMSEA

Calculate maximal reliability

Analyzing data using a null model

Simulated Dataset to Demonstrate Three-way Latent Interaction

Probing two-way interaction on the residual-centered latent interaction

measurementInvariance

Measurement Invariance Tests

Generate Non-normal Data using Vale and Maurelli (1983) method

Nesting and Equivalence Testing

partialInvariance

Partial Measurement Invariance Testing Across Groups

Plot the sampling distributions of RMSEA

Combine the results from the quark function

Plot the graphs for probing latent interaction

Class For Rotated Results from EFA

Calculate the reliability values of a second-order factor

Class For Representing A Template of Model Fit Comparisons

Class For the Result of Nesting and Equivalence Testing

findRMSEApowernested

Find power given a sample size in nested model comparison

Finding skewness

Bollen-Stine Bootstrap with the Existence of Missing Data

Find standardized estimates for OpenMx output

Analyzing data using a saturate model

Simulated Dataset to Demonstrate Two-way Latent Interaction

Simulated Data set to Demonstrate Categorical Measurement Invariance

Specify starting values from a lavaan output

Probing two-way interaction on the residual-centered latent interaction

Calculate reliability values of factors

singleParamTest

Single Parameter Test Divided from Nested Model Comparison

residualCovariate

Residual centered all target indicators by covariates

Implement orthogonal or oblique rotation

impliedFactorStat

Calculate the model-implied factor means and covariance matrix.

Calculate multivariate Wald statistics

Finding Mardia's multivariate kurtosis

Analyzing data with full-information maximum likelihood with auxiliary variables

Find fit measures from an MxModel result

Finding Mardia's multivariate skewness

measurementInvarianceCat

Measurement Invariance Tests for Categorical Items

loadingFromAlpha

Find standardized factor loading from coefficient alpha

Power for model parameters

Class For the Results of Bollen-Stine Bootstrap with Incomplete Data

clipboard_saveFile

Copy or save the result of lavaan or FitDiff objects into a clipboard or a file

Make products of indicators using no centering, mean centering, double-mean centering, or residual centering

Build an object summarizing fit indices across multiple models

lavaanStar-class

Class For Representing A (Fitted) Latent Variable Model with Additional Elements

Probing two-way interaction on the residual-centered latent interaction

Latent variable modeling in lavaan using LISREL syntax

Analyze Unrotated Exploratory Factor Analysis Model

Measurement Invariance Tests Within Person

parcelAllocation

Random Allocation of Items to Parcels in a Structural Equation Model

Calculate the RMSEA of the null model

plotRMSEApowernested

Plot power of nested model RMSEA

Simulated Data set to Demonstrate Random Allocations of Parcels

Generate data via the Kaiser-Dickman (1962) algorithm.

Calculate more fit indices

Monte Carlo Confidence Intervals to Test Complex Indirect Effects

Probing three-way interaction on the residual-centered latent interaction