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semTools (version 0.4-12)

Useful Tools for Structural Equation Modeling

Description

Provides useful tools for structural equation modeling packages.

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Install

install.packages('semTools')

Monthly Downloads

14,750

Version

0.4-12

License

GPL (>= 2)

Issues

6

Pull Requests

3

Stars

75

Forks

36

Maintainer

Sunthud Pornprasertmanit

Last Published

June 10th, 2016

Functions in semTools (0.4-12)

combinequark

Combine the results from the quark function
compareFit

Build an object summarizing fit indices across multiple models
dat2way

Simulated Dataset to Demonstrate Two-way Latent Interaction
BootMiss-class

Class For the Results of Bollen-Stine Bootstrap with Incomplete Data
dat3way

Simulated Dataset to Demonstrate Three-way Latent Interaction
bsBootMiss

Bollen-Stine Bootstrap with the Existence of Missing Data
ci.reliability

Confidence Interval for a Reliability Coefficient
boreal

The Boreal Vegetation Dataset
clipboard_saveFile

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

Analyzing data with full-information maximum likelihood with auxiliary variables
fitMeasuresMx

Find fit measures from an MxModel result
EFA-class

Class For Rotated Results from EFA
datCat

Simulated Data set to Demonstrate Categorical Measurement Invariance
FitDiff-class

Class For Representing A Template of Model Fit Comparisons
findRMSEAsamplesizenested

Find sample size given a power in nested model comparison
findRMSEApowernested

Find power given a sample size in nested model comparison
findRMSEApower

Find the statistical power based on population RMSEA
efaUnrotate

Analyze Unrotated Exploratory Factor Analysis Model
exLong

Simulated Data set to Demonstrate Longitudinal Measurement Invariance
findRMSEAsamplesize

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

Specify starting values from a lavaan output
lisrel2lavaan

Latent variable modeling in lavaan using LISREL syntax
indProd

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

Find standardized factor loading from coefficient alpha
htmt

Assessing Discriminant Validity using Heterotrait-Monotrait Ratio
fmi

Fraction of Missing Information.
longInvariance

Measurement Invariance Tests Within Person
kd

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

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

Measurement Invariance Tests
monteCarloMed

Monte Carlo Confidence Intervals to Test Complex Indirect Effects
net

Nesting and Equivalence Testing
measurementInvarianceCat

Measurement Invariance Tests for Categorical Items
mvrnonnorm

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

Modification indices and their power approach for model fit evaluation
nullMx

Analyzing data using a null model
Net-class

Class For the Result of Nesting and Equivalence Testing
partialInvariance

Partial Measurement Invariance Testing Across Groups
PAVranking

Parcel-Allocation Variability in Model Ranking
parcelAllocation

Random Allocation of Items to Parcels in a Structural Equation Model
permuteMeasEq-class

Class for the Results of Permutation Randomization Tests of Measurement Equivalence and DIF
residualCovariate

Residual centered all target indicators by covariates
plotRMSEApowernested

Plot power of nested model RMSEA
permuteMeasEq

Permutation Randomization Tests of Measurement Equivalence and Differential Item Functioning (DIF)
poolMAlloc

Pooled estimates and standard errors across M parcel-allocations: Combining sampling variability and parcel-allocation variability.
quark

Quark
plotProbe

Plot the graphs for probing latent interaction
probe3WayRC

Probing three-way interaction on the residual-centered latent interaction
probe2WayRC

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

Plot power curves for RMSEA
spatialCorrect

Calculate reliability values of factors
runMI

Multiply impute and analyze data using lavaan
singleParamTest

Single Parameter Test Divided from Nested Model Comparison
rotate

Implement orthogonal or oblique rotation
standardizeMx

Find standardized estimates for OpenMx output
SSpower

Power for model parameters
saturateMx

Analyzing data using a saturate model
simParcel

Simulated Data set to Demonstrate Random Allocations of Parcels