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simsem (version 0.4-6)

SIMulated Structural Equation Modeling.

Description

This package can be used to generate data using the structural equation modeling framework. This package is tailored to use those simulated data for various purposes, such as model fit evaluation, power analysis, or missing data handling and planning.

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Version

Install

install.packages('simsem')

Monthly Downloads

1,283

Version

0.4-6

License

GPL (>= 2)

Maintainer

Sunthud Pornprasertmanit

Last Published

December 31st, 2012

Functions in simsem (0.4-6)

SimResult-class

Class "SimResult": Simulation Result Object

SimMissing-class

Class "SimMissing"

Class "SimSem"

Generate data using SimSem template

continuousPower

Find power of model parameters when simulations have randomly varying parameters

getCutoffNested

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

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

getPowerFitNested

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

pValueNonNested

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

Provide summary of model fit across replications

summaryPopulation

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

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

findIndTotalVar

Find indicator total variances from factor loading matrix, total factor covariance, and indicator residual variances.

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

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

Find p-values (1 - percentile)

Plot a distribution of a data distribution object

Find the discrepancy value between two means and covariance matrices

findPossibleFactorCor

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

Find factor total means from regression coefficient matrix and factor intercept

findRecursiveSet

Group variables regarding the position in mediation chain

Data generation template and analysis template for simulation.

plotCutoffNested

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

summaryConverge

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

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

Set the data generation population model underlying an object

Create a data distribution object.

findFactorIntercept

Find factor intercept from regression coefficient matrix and factor total means

findIndResidualVar

Find indicator residual variances from factor loading matrix, total factor covariance, and total indicator variances.

Provide a comparison of nested models and nonnested models across replications

Plot the population misfit in the result object

Get extra outputs from the result of simulation

plotPowerFitNonNested

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

Run a monte carlo simulation with a structural equation model.

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

SimMatrix-class

Matrix object: Random parameters matrix

Shortcut for data analysis template for simulation.

getCutoffNonNested

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

Find fit indices cutoff given a priori alpha level

findIndIntercept

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

plotCutoffNonNested

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

Make a power plot of a parameter given varying parameters

Provide summary of parameter estimates and standard error across replications

Export data sets for analysis with outside SEM program.

Specify matrices for Monte Carlo simulation of structural equation models

Create data from a set of drawn parameters.

Find power in rejecting alternative models based on fit indices criteria

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

Draw values from vector or matrix objects

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

Plot sampling distributions of fit indices with fit indices cutoffs

Provide short summary of an object.

SimDataDist-class

Class "SimDataDist": Data distribution object

Draw parameters from a SimSem object.

Extract the data generation population model underlying a result object

Specifying the missing template to impose on a dataset

Find population misfit by sufficient statistics

Data analysis using the model specification

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

SimVector-class

Vector object: Random parameters vector

Find power of model parameters

getPowerFitNonNested

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

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

findFactorTotalVar

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

multipleAllEqual

Test whether all objects are equal