SIMulated Structural Equation Modeling

Documentation for package ‘simsem’ version 0.5-16

DESCRIPTION file.

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-- A --

analyze	Data analysis using the model specification
anova-method	Provide a comparison of nested models and nonnested models across replications

-- B --

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

-- C --

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.

-- D --

draw	Draw parameters from a 'SimSem' object.

-- E --

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.

-- F --

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

-- G --

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

-- I --

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	Extract information from a simulation result
inspect-method	Extract information from a simulation result

-- L --

likRatioFit

Find the likelihood ratio (or Bayes factor) based on the bivariate distribution of fit indices

-- M --

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

-- P --

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

-- R --

rawDraw

Draw values from vector or matrix objects

-- S --

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