semTools v0.5-2

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Useful Tools for Structural Equation Modeling

Provides useful tools for structural equation modeling.

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semTools

Useful tools for structural equation modeling.

This is an R package whose primary purpose is to extend the functionality of the R package lavaan. There are several suites of tools in the package, which correspond to the same theme. To browse these suites, open the help page at the Console:

?semTools::`semTools-package`

Additional tools are available to do not require users to rely on any R packages for SEM (e.g., lavaan, OpenMx, or sem), as long as their other software provides the information they need. Examples:

  • monteCarloMed() to calculate Monte Carlo confidence intervals for functions of parameters, such as indirect effects in mediation models
  • calculate.D2() to pool z or chi-squared statistics across multiple imputations of missing data
  • indProd() for creating product indicators of latent interactions
  • SSpower() provides analytically derived power estimates for SEMs
  • tukeySEM() for Tukey's WSD post-hoc test of mean-differences under unequal variance and sample size
  • bsBootMiss() to transform incomplete data to be consistent with the null-hypothesized model, appropriate for model-based (a.k.a. "Bollen--Stine") boostrapping

All users of R (or SEM) are invited to submit functions or ideas for functions by contacting the maintainer, Terrence Jorgensen (TJorgensen314 at gmail dot com). Contributors are encouraged to use Roxygen comments to document their contributed code, which is consistent with the rest of semTools. Read the vignette from the roxygen2 package for details:

vignette("rd", package = "roxygen2")

Functions in semTools

Name Description
EFA-class Class For Rotated Results from EFA
SSpower Power for model parameters
FitDiff-class Class For Representing A Template of Model Fit Comparisons
chisqSmallN k-factor correction for \(chi^2\) test statistic
BootMiss-class Class For the Results of Bollen-Stine Bootstrap with Incomplete Data
Net-class Class For the Result of Nesting and Equivalence Testing
calculate.D2 Calculate the "D2" statistic
auxiliary Implement Saturated Correlates with FIML
PAVranking Parcel-Allocation Variability in Model Ranking
bsBootMiss Bollen-Stine Bootstrap with the Existence of Missing Data
exLong Simulated Data set to Demonstrate Longitudinal Measurement Invariance
efaUnrotate Analyze Unrotated Exploratory Factor Analysis Model
imposeStart Specify starting values from a lavaan output
efa.ekc Empirical Kaiser criterion
htmt Assessing Discriminant Validity using Heterotrait-Monotrait Ratio
clipboard Copy or save the result of lavaan or FitDiff objects into a clipboard or a file
kurtosis Finding excessive kurtosis
lavTestLRT.mi Likelihood Ratio Test for Multiple Imputations
datCat Simulated Data set to Demonstrate Categorical Measurement Invariance
combinequark Combine the results from the quark function
dat3way Simulated Dataset to Demonstrate Three-way Latent Interaction
compareFit Build an object summarizing fit indices across multiple models
findRMSEAsamplesizenested Find sample size given a power in nested model comparison
fmi Fraction of Missing Information.
dat2way Simulated Dataset to Demonstrate Two-way Latent Interaction
findRMSEApower Find the statistical power based on population RMSEA
findRMSEAsamplesize Find the minimum sample size for a given statistical power based on population RMSEA
findRMSEApowernested Find power given a sample size in nested model comparison
loadingFromAlpha Find standardized factor loading from coefficient alpha
lavaan.mi-class Class for a lavaan Model Fitted to Multiple Imputations
maximalRelia Calculate maximal reliability
mardiaSkew Finding Mardia's multivariate skewness
mvrnonnorm Generate Non-normal Data using Vale and Maurelli (1983) method
nullRMSEA Calculate the RMSEA of the null model
lavTestScore.mi Score Test for Multiple Imputations
indProd Make products of indicators using no centering, mean centering, double-mean centering, or residual centering
measurementInvariance-deprecated Measurement Invariance Tests
kd Generate data via the Kaiser-Dickman (1962) algorithm.
lavTestWald.mi Wald Test for Multiple Imputations
net Nesting and Equivalence Testing
miPowerFit Modification indices and their power approach for model fit evaluation
longInvariance-deprecated Measurement Invariance Tests Within Person
modindices.mi Modification Indices for Multiple Imputations
monteCarloMed Monte Carlo Confidence Intervals to Test Complex Indirect Effects
mardiaKurtosis Finding Mardia's multivariate kurtosis
moreFitIndices Calculate more fit indices
measEq.syntax Syntax for measurement equivalence
measEq.syntax-class Class for Representing a Measurement-Equivalence Model
plotRMSEApower Plot power curves for RMSEA
plotRMSEApowernested Plot power of nested model RMSEA
partialInvariance Partial Measurement Invariance Testing Across Groups
permuteMeasEq-class Class for the Results of Permutation Randomization Tests of Measurement Equivalence and DIF
measurementInvarianceCat-deprecated Measurement Invariance Tests for Categorical Items
plotProbe Plot a latent interaction
plotRMSEAdist Plot the sampling distributions of RMSEA
plausibleValues Plausible-Values Imputation of Factor Scores Estimated from a lavaan Model
permuteMeasEq Permutation Randomization Tests of Measurement Equivalence and Differential Item Functioning (DIF)
probe2WayRC Probing two-way interaction on the residual-centered latent interaction
probe3WayMC Probing two-way interaction on the no-centered or mean-centered latent interaction
singleParamTest Single Parameter Test Divided from Nested Model Comparison
skew Finding skewness
orthRotate Implement orthogonal or oblique rotation
twostage Fit a lavaan model using 2-Stage Maximum Likelihood (TSML) estimation for missing data.
residualCovariate Residual-center all target indicators by covariates
twostage-class Class for the Results of 2-Stage Maximum Likelihood (TSML) Estimation for Missing Data
poolMAlloc Pooled estimates and standard errors across M parcel-allocations: Combining sampling variability and parcel-allocation variability.
reliability Calculate reliability values of factors
probe2WayMC Probing two-way interaction on the no-centered or mean-centered latent interaction
parcelAllocation Random Allocation of Items to Parcels in a Structural Equation Model
semTools-deprecated Deprecated functions in package semTools.
runMI Fit a lavaan Model to Multiple Imputed Data Sets
probe3WayRC Probing three-way interaction on the residual-centered latent interaction
splitSample Randomly Split a Data Set into Halves
tukeySEM Tukey's WSD post-hoc test of means for unequal variance and sample size
quark Quark
reliabilityL2 Calculate the reliability values of a second-order factor
semTools semTools: Useful Tools for Structural Equation Modeling
simParcel Simulated Data set to Demonstrate Random Allocations of Parcels
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Vignettes of semTools

Name
partialInvariance.Rnw
partialInvariance.bib
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