# SIMulated Structural Equation Modeling

## Documentation for package ‘simsem’ version 0.5-17

- DESCRIPTION file

## Help Pages

A B C D E F G I L M P R S

## -- 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