Class "SimMissing"
Provide a comparison of nested models and nonnested models across replications
Class "SimSem"
Data analysis using the model specification
Class "SimDataDist"
: Data distribution object
Vector object: Random parameters vector
Matrix object: Random parameters matrix
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.
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.
Find indicator intercepts from factor loading matrix, total factor mean, and indicator mean.
Find coverage rate of model parameters when simulations have randomly varying parameters
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.
Export data sets for analysis with outside SEM program.
Find a value of independent variables that provides a given value of power.
Find factor total covariance from regression coefficient matrix, factor residual covariance
Find the appropriate position for freely estimated correlation (or covariance) given a regression coefficient matrix
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. Find fit indices cutoff for non-nested model comparison given a priori alpha level
Find factor total variances from regression coefficient matrix, factor (residual) correlations, and factor residual variances
Find power in rejecting nested models based on the differences in fit indices
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
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
Test whether all objects are equal
Extract information from a simulation result
Find fit indices cutoff for nested model comparison given a priori alpha level
Plot sampling distributions of fit indices with fit indices cutoffs
Plot sampling distributions of the differences in fit indices between non-nested models with fit indices cutoffs
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
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
Plot power of rejecting a nested model in a nested model comparison by each fit index
Summary of a seed number
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
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
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
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
Time summary
Provide short summary of an object.
Provide summary of model fit across replications