SIMulated Structural Equation Modeling

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Documentation for package ‘simsem’ version 0.5-12

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analyze Data analysis using the model specification
anova-method Provide a comparison of nested models and nonnested models across replications
bind Specify matrices for Monte Carlo simulation of structural equation models
bindDist Create a data distribution object.
binds Specify matrices for Monte Carlo simulation of structural equation models
coef-method Extract parameter estimates from a simulation result
combineSim Combine result objects
continuousCoverage Find coverage rate of model parameters when simulations have randomly varying parameters
continuousPower Find power of model parameters when simulations have randomly varying parameters
createData Create data from a set of drawn parameters.
draw Draw parameters from a 'SimSem' object.
estmodel Shortcut for data analysis template for simulation.
estmodel.cfa Shortcut for data analysis template for simulation.
estmodel.path Shortcut for data analysis template for simulation.
estmodel.sem Shortcut for data analysis template for simulation.
exportData Export data sets for analysis with outside SEM program.
findCoverage Find a value of independent variables that provides a given value of coverage rate
findFactorIntercept Find factor intercept from regression coefficient matrix and factor total means
findFactorMean Find factor total means from regression coefficient matrix and factor intercept
findFactorResidualVar Find factor residual variances from regression coefficient matrix, factor (residual) correlations, and total factor variances
findFactorTotalCov Find factor total covariance from regression coefficient matrix, factor residual covariance
findFactorTotalVar Find factor total variances from regression coefficient matrix, factor (residual) correlations, and factor residual variances
findIndIntercept Find indicator intercepts from factor loading matrix, total factor mean, and indicator mean.
findIndMean Find indicator total means from factor loading matrix, total factor mean, and indicator intercept.
findIndResidualVar Find indicator residual variances from factor loading matrix, total factor covariance, and total indicator variances.
findIndTotalVar Find indicator total variances from factor loading matrix, total factor covariance, and indicator residual variances.
findPossibleFactorCor Find the appropriate position for freely estimated correlation (or covariance) given a regression coefficient matrix
findPower Find a value of independent variables that provides a given value of power.
findRecursiveSet Group variables regarding the position in mediation chain
generate Generate data using SimSem template
getCIwidth Find confidence interval width
getCoverage Find coverage rate of model parameters
getCutoff Find fit indices cutoff given a priori alpha level
getCutoffNested Find fit indices cutoff for nested model comparison given a priori alpha level
getCutoffNonNested Find fit indices cutoff for non-nested model comparison given a priori alpha level
getExtraOutput Get extra outputs from the result of simulation
getPopulation Extract the data generation population model underlying a result object
getPower Find power of model parameters
getPowerFit Find power in rejecting alternative models based on fit indices criteria
getPowerFitNested Find power in rejecting nested models based on the differences in fit indices
getPowerFitNonNested Find power in rejecting non-nested models based on the differences in fit indices
getPowerFitNonNested-method Find power in rejecting non-nested models based on the differences in fit indices
getPowerFitNonNested-methods Find power in rejecting non-nested models based on the differences in fit indices
impose Impose MAR, MCAR, planned missingness, or attrition on a data set
imposeMissing Impose MAR, MCAR, planned missingness, or attrition on a data set
inspect-method Extract information from a simulation result
likRatioFit Find the likelihood ratio (or Bayes factor) based on the bivariate distribution of fit indices
miss Specifying the missing template to impose on a dataset
model Data generation template and analysis template for simulation.
model.cfa Data generation template and analysis template for simulation.
model.lavaan Build the data generation template and analysis template from the lavaan result
model.path Data generation template and analysis template for simulation.
model.sem Data generation template and analysis template for simulation.
multipleAllEqual Test whether all objects are equal
plotCIwidth Plot a confidence interval width of a target parameter
plotCoverage Make a plot of confidence interval coverage rates
plotCutoff Plot sampling distributions of fit indices with fit indices cutoffs
plotCutoffNested Plot sampling distributions of the differences in fit indices between nested models with fit indices cutoffs
plotCutoffNonNested Plot sampling distributions of the differences in fit indices between non-nested models with fit indices cutoffs
plotDist Plot a distribution of a data distribution object
plotDist-method Class '"SimDataDist"': Data distribution object
plotLogitMiss Visualize the missing proportion when the logistic regression method is used.
plotMisfit Plot the population misfit in the result object
plotPower Make a power plot of a parameter given varying parameters
plotPowerFit Plot sampling distributions of fit indices that visualize power of rejecting datasets underlying misspecified models
plotPowerFitNested Plot power of rejecting a nested model in a nested model comparison by each fit index
plotPowerFitNonNested Plot power of rejecting a non-nested model based on a difference in fit index
popDiscrepancy Find the discrepancy value between two means and covariance matrices
popMisfitMACS Find population misfit by sufficient statistics
pValue Find p-values (1 - percentile) by comparing a single analysis output from the result object
pValueNested Find p-values (1 - percentile) for a nested model comparison
pValueNonNested Find p-values (1 - percentile) for a non-nested model comparison
rawDraw Draw values from vector or matrix objects
setPopulation Set the data generation population model underlying an object
sim Run a monte carlo simulation with a structural equation model.
SimDataDist-class Class '"SimDataDist"': Data distribution object
SimMatrix-class Matrix object: Random parameters matrix
SimMissing-class Class '"SimMissing"'
SimResult-class Class '"SimResult"': Simulation Result Object
SimSem-class Class '"SimSem"'
SimVector-class Vector object: Random parameters vector
summary-method Class '"SimDataDist"': Data distribution object
summary-method Matrix object: Random parameters matrix
summary-method Class '"SimMissing"'
summary-method Class '"SimResult"': Simulation Result Object
summary-method Class '"SimSem"'
summary-method Vector object: Random parameters vector
summaryConverge Provide a comparison between the characteristics of convergent replications and nonconvergent replications
summaryFit Provide summary of model fit across replications
summaryMisspec Provide summary of the population misfit and misspecified-parameter values across replications
summaryParam Provide summary of parameter estimates and standard error across replications
summaryPopulation Summarize the population model used for data generation underlying a result object
summarySeed Summary of a seed number
summaryShort Provide short summary of an object.
summaryShort-method Matrix object: Random parameters matrix
summaryShort-method Class '"SimResult"': Simulation Result Object
summaryShort-method Vector object: Random parameters vector
summaryShort-method Provide short summary of an object.
summaryShort-methods Provide short summary of an object.
summaryTime Time summary