simsem

Class "SimDataDist": Data distribution object

Matrix object: Random parameters matrix

Class "SimMissing"

Class "SimResult": Simulation Result Object

Class "SimSem"

Vector object: Random parameters vector

Data analysis using the model specification

Provide a comparison of nested models and nonnested models across replications

Specify matrices for Monte Carlo simulation of structural equation models

Create a data distribution object.

Extract parameter estimates from a simulation result

Combine result objects

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

Find power of model parameters when simulations have randomly varying parameters

Create data from a set of drawn parameters.

Draw parameters from a SimSem object.

Shortcut for data analysis template for simulation.

Export data sets for analysis with outside SEM program.

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

Find factor intercept from regression coefficient matrix and factor total means

Find factor total means from regression coefficient matrix and factor intercept

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

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

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

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

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

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

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

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

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

Group variables regarding the position in mediation chain

Generate data using SimSem template

Find confidence interval width

Find coverage rate of model parameters

Find fit indices cutoff given a priori alpha level

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

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

Get extra outputs from the result of simulation

Extract the data generation population model underlying a result object

Find power of model parameters

Find power in rejecting alternative models based on fit indices criteria

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

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

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

Extract information from a simulation result

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

Specifying the missing template to impose on a dataset

Data generation template and analysis template for simulation.

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

Test whether all objects are equal

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

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

Plot a confidence interval width of a target parameter

Make a plot of confidence interval coverage rates

Plot sampling distributions of fit indices with fit indices cutoffs

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

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

Plot a distribution of a data distribution object

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

Plot the population misfit in the result object

Make a power plot of a parameter given varying parameters

Plot sampling distributions of fit indices that visualize power of rejecting datasets underlying misspecified models

Plot power of rejecting a nested model in a nested model comparison by each fit index

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

Find population misfit by sufficient statistics

Draw values from vector or matrix objects

Set the data generation population model underlying an object

Run a Monte Carlo simulation with a structural equation model.

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

Provide summary of model fit across replications

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

Provide summary of parameter estimates and standard error across replications

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

Summary of a seed number

Provide short summary of an object.

Time summary