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simsem (version 0.5-16)

SIMulated Structural Equation Modeling

Description

Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.

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Version

Install

install.packages('simsem')

Monthly Downloads

653

Version

0.5-16

License

GPL (>= 2)

Maintainer

Terry Jorgensen

Last Published

March 28th, 2021

Functions in simsem (0.5-16)

SimMissing-class

Class "SimMissing"

Provide a comparison of nested models and nonnested models across replications

Class "SimSem"

Data analysis using the model specification

SimDataDist-class

Class "SimDataDist": Data distribution object

SimVector-class

Vector object: Random parameters vector

SimMatrix-class

Matrix object: Random parameters matrix

SimResult-class

Class "SimResult": Simulation Result Object

Create a data distribution object.

Specify matrices for Monte Carlo simulation of structural equation models

Find a value of independent variables that provides a given value of coverage rate

Create data from a set of drawn parameters.

Shortcut for data analysis template for simulation.

continuousPower

Find power of model parameters when simulations have randomly varying parameters

Find indicator total means from factor loading matrix, total factor mean, and indicator intercept.

findIndIntercept

Find indicator intercepts from factor loading matrix, total factor mean, and indicator mean.

continuousCoverage

Find coverage rate of model parameters when simulations have randomly varying parameters

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.

Export data sets for analysis with outside SEM program.

Find a value of independent variables that provides a given value of power.

findFactorTotalCov

Find factor total covariance from regression coefficient matrix, factor residual covariance

findPossibleFactorCor

Find the appropriate position for freely estimated correlation (or covariance) given a regression coefficient matrix

findFactorIntercept

Find factor intercept from regression coefficient matrix and factor total means

Combine result objects

Extract parameter estimates from a simulation result

Find fit indices cutoff given a priori alpha level

Draw parameters from a '>SimSem object.

getCutoffNonNested

Find fit indices cutoff for non-nested model comparison given a priori alpha level

findFactorTotalVar

Find factor total variances from regression coefficient matrix, factor (residual) correlations, and factor residual variances

getPowerFitNested

Find power in rejecting nested models based on the differences in fit indices

findFactorResidualVar

Find factor residual variances from regression coefficient matrix, factor (residual) correlations, and total factor variances

Generate data using SimSem template

Find factor total means from regression coefficient matrix and factor intercept

findRecursiveSet

Group variables regarding the position in mediation chain

Find power in rejecting alternative models based on fit indices criteria

Extract the data generation population model underlying a result object

Find power of model parameters

Make a plot of confidence interval coverage rates

Build the data generation template and analysis template from the lavaan result

multipleAllEqual

Test whether all objects are equal

Extract information from a simulation result

getCutoffNested

Find fit indices cutoff for nested model comparison given a priori alpha level

Plot sampling distributions of fit indices with fit indices cutoffs

plotCutoffNonNested

Plot sampling distributions of the differences in fit indices between non-nested models with fit indices cutoffs

plotCutoffNested

Plot sampling distributions of the differences in fit indices between nested models with fit indices cutoffs

Find confidence interval width

Specifying the missing template to impose on a dataset

Get extra outputs from the result of simulation

Data generation template and analysis template for simulation.

Plot a confidence interval width of a target parameter

pValueNonNested

Find p-values (1 - percentile) for a non-nested model comparison

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

Summary of a seed number

plotPowerFitNonNested

Plot power of rejecting a non-nested model based on a difference in fit index

Find the discrepancy value between two means and covariance matrices

summaryPopulation

Summarize the population model used for data generation underlying a result object

Find p-values (1 - percentile) by comparing a single analysis output from the result object

Find p-values (1 - percentile) for a nested model comparison

summaryConverge

Provide a comparison between the characteristics of convergent replications and nonconvergent replications

Make a power plot of a parameter given varying parameters

Plot the population misfit in the result object

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

Find coverage rate of model parameters

Set the data generation population model underlying an object

Run a Monte Carlo simulation with a structural equation model.

Provide summary of the population misfit and misspecified-parameter values across replications

getPowerFitNonNested

Find power in rejecting non-nested models based on the differences in fit indices

Provide summary of parameter estimates and standard error across replications

Find population misfit by sufficient statistics

Plot a distribution of a data distribution object

Draw values from vector or matrix objects

Visualize the missing proportion when the logistic regression method is used.

Impose MAR, MCAR, planned missingness, or attrition on a data set

Provide short summary of an object.

Provide summary of model fit across replications