Learn R Programming

⚠️There's a newer version (0.5-17) of this package.Take me there.

simsem (version 0.2-8)

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.

Copy Link

Version

Install

install.packages('simsem')

Monthly Downloads

1,207

Version

0.2-8

License

GPL (>= 2)

Maintainer

Sunthud Pornprasertmanit

Last Published

July 9th, 2012

Functions in simsem (0.2-8)

Count the number of elements in the sufficient statistics

countFreeParameters

Count how many free parameters in the target object

Extract a part of an object

extractOpenMxFit

Extract the fit indices reported by the OpenMx result

extractVectorNames

Extract a vector of parameter names based on specified elements

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

Find rows in a matrix that all elements are zero in non-fixed subset rows and columns.

createFreeParameters

Create a free parameters object from a model specification

Find fit indices cutoff given a priori alpha level

getCutoffNested

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

Finding excessive kurtosis

Check whether a matrix is a possible correlation matrix

loadingFromAlpha

Find standardized factor loading from coefficient alpha

plotCutoffNested

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

Create a Data object

Find the discrepancy value between two means and covariance matrices

Create random geometric distribution object

Create random F distribution object

Construct a SimMissing object to create data with missingness and analyze missing data.

Set of model misspecification for CFA model.

Create random log normal distribution object

Create a set of matrices of parameter and parameter values to generate and analyze data that belongs to Path analysis model

Validate whether the drawn parameters are good.

Provide summary of model misspecification imposed across replications

writeLavaanIndividualConstraint

Write a lavaan code for a given equality constraint for each parameter

MatrixSet-class

Class "MatrixSet"

SimDataOut-class

Class "SimDataOut"

Class "SimParam"

Create a constant vector object

Make a division on each element of the object

extractMatrixNames

Extract a vector of parameter names based on specified rows and columns

Expand the set of intercept and covariance matrices into the set of intercept/mean and covariance/correlation/variance objects

findFactorTotalVar

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

Create data from model parameters

Find power in rejecting alternative models based on fit indices criteria

Check whether the object is the NULL type of that class

Check whether a vector object is default

Build a scatterplot with overlaying line of quantiles of predicted values

Draw actual parameters and model misspecification

Create random exponential distribution object

Sort two objects in a list

Create random gamma distribution object

Create random normal distribution object

Create random Poisson distribution object

Create symmetric simMatrix that save free parameters and starting values, as well as fixed values

writeLavaanNullCode

Write a lavaan code for a null model

Standardize the parameter estimates within an object

Class "SimModel"

combineLatentCorExoEndo

Combine exogenous factor correlation and endogenous factor correlation into a single matrix

defaultStartingValues

Make ad hoc starting values

Find the density (likelihood) of a pair value in 2D Kernel Density Estimate

Extract the data generation population model underlying an object

List of all keywords used in the simsem package

Function to pool imputed results

Provide basic summary of each object if that object is not NULL.

Multiply impute and analyze data using lavaan

Create random binomial distribution object

Create random t distribution object

summaryPopulation

Summarize the data generation population model underlying an object

writeLavaanCode

Write a lavaan code given the matrices of free parameter

SimGenLabels-class

Class "SimGenLabels"

Extract only converged replications in the result object

findIndTotalVar

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

Create a free parameters vector with a starting values in a vector object

isMeanConstraint

Check whether all rownames in a constraint matrix containing symbols of means vectors

Plot the population misfit in parameter result object

Function to get predicted probabilities from logistic regression

Create a set of matrices of parameter and parameter values to generate and analyze data that belongs to SEM model

SimModelOut-class

Class "SimModelOut"

SimFunction-class

Class "SimFunction"

Extract only converged replications in the result objects

drawParametersMisspec

Create parameter sets (with or without model misspecification) from the parameter with or without misspecification set

findTargetPower

Find a value of varying parameters that provides a given value of power.

getPowerFitNested

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

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

Find p-values (1 - percentile)

pValueCondCutoff

Find a p value when the target is conditional (valid) on a specific value of a predictor

Plot sampling distributions of fit indices with fit indices cutoffs

plotLogisticFit

Plot multiple logistic curves for predicting whether rejecting a misspecified model

plotCutoffNonNested

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

Equality Constraint Object

Reduce the model constraint to data generation parameterization to analysis model parameterization.

Create simMatrix that save free parameters and starting values, as well as fixed values

Create a set of matrices of parameters for analyzing data that belongs to CFA model.

Transform the analysis model object into the object for data generation

Calculate the weighted mean of a variable

checkInputValue

Check the value argument in the matrix, symmetric matrix, or vector objects

SimModelMIOut-class

Class "SimModelMIOut"

findFactorResidualVar

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

findIndIntercept

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

Find fit indices from the discrepancy values of the target model and null models.

Find factor total means from regression coefficient matrix and factor intercept

Plot multiple overlapping histograms

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

isVarianceConstraint

Check whether all rownames in a constraint matrix containing symbols of variance vectors

Search for the keywords and check whether the specified text match one in the name vector

Build a parameter result object that the data-generation parameters are from the result of analyzing real data

Create random beta distribution object

Create random Cauchy distribution object

Create a data distribution object.

Create random hypergeometric distribution object

Set the data generation population model underlying an object

Set of model misspecification for Path analysis model.

Create a set of matrices of parameter and parameter values to generate and analyze data that belongs to CFA model.

Create simVector that save free parameters and starting values, as well as fixed values

Make a subtraction of each element in an object

Tag names to each element

twoTailedPValue

Find two-tailed p value from one-tailed p value

validateCovariance

Validate whether all elements provides a good covariance matrix

SimREqualCon-class

Class "SimREqualCon"

SimMatrix-class

Matrix object: Random parameters matrix

SimResult-class

Class "SimResult"

blankParameters

Change all elements in the non-null objects to be all NAs.

Fit the 2D Kernel Density Estimate

SymMatrix-class

Symmetric matrix object: Random parameters symmetric matrix

findPossibleFactorCor

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

findIndResidualVar

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

Find the value of one vector relative to a value of another vector by interpolation

plotIndividualScatter

Plot an overlaying scatter plot visualizing the power of rejecting misspecified models

Run data by the model object by the lavaan package

Find starting values by averaging random numbers

vectorizeObject

Change an object to a vector with labels

SimDataDist-class

Class "SimDataDist"

SimVector-class

Vector object: Random parameters vector

Provide a comparison of nested models and nonnested models across replications

Convert a covariance matrix to a correlation matrix

createImpliedMACS

Create model implied mean vector and covariance matrix

combineLoadingExoEndo

Combine factor loading from the exogenous and endogenous sides into a single matrix

Fill in other objects based on the parameter values of current objects

multipleAllEqual

Test whether all objects are equal

Run a particular object in simsem package.

Run one replication within a big simulation study

Provide short summary of an object.

Set of model misspecification for SEM model.

SimMissing-class

Class "SimMissing"

SimResultParam-class

Class "SimResultParam"

Calculate central moments of a variable

extractLavaanFit

Extract fit indices from the lavaan object

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

Find power of model parameters

getCutoffNonNested

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

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

Check whether the object contains any random parameters

Make parameter names for each element in matrices or vectors or the name for the whole object

pValueNonNested

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

Make a power plot of a parameter given varying parameters

Plot overlaying scatter plots visualizing the power of rejecting misspecified models

Reassign the name of equality constraint

Plot multiple logistic curves given a significance result matrix

Create a set of matrices of parameters for analyzing data that belongs to Path analysis model

The constructor of the parameter result object

Provide summary of model fit across replications

Provide summary of parameter estimates and standard error across replications

Class "VirtualRSet", "SimLabels" and "SimRSet"

SimEqualCon-class

Class "SimEqualCon"

VirtualDist-class

Distribution Objects

Change an element in SimMatrix, SymMatrix, or SimVector.

findFactorIntercept

Find factor intercept from regression coefficient matrix and factor total means

findFactorTotalCov

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

findRecursiveSet

Group variables regarding the position in mediation chain

Build a persepctive plot or contour plot of a quantile of predicted values

Plot a distribution of a distribution object or data distribution object

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

reduceConstraint

Reduce the model constraint to data generation parameterization to analysis model parameterization.

Create a set of matrices of parameters for analyzing data that belongs to SEM model

Create random Weibull distribution object

Export the distribution object to a function command in text that can be evaluated directly.

Collapse all exogenous variables and put all in endogenous side only.

Combine by summing or binding two objects together.

combineMeasurementErrorExoEndo

Combine measurement error correlation from the exogenous and endogenous sides into a single matrix

SimMisspec-class

Class "SimMisspec"

Get a quantile of a variable given values of predictors

continuousPower

Find power of model parameters when simulations have randomly varying parameters

Class "SimData"

combinePathExoEndo

Combine the regression coefficient matrices

constrainMatrices

Impose an equality constraint in an object

Calculate the k-statistic of a variable

Create simResult.

Function to pool imputed results that saved in a matrix format

Find population misfit by sufficient statistics

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

plotPowerFitNonNested

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

Create random logistic distribution object

Function to pool chi-square statistics from the result from multiple imputation

plotPowerFitNested

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

Create a model object

getPowerFitNonNested

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

Class "SimSet"

Reverse the proportion value by subtracting it from 1

pValueVariedCutoff

Find a p value when the cutoff is specified as a vector given the values of predictors

Create function object

Create random negative binomial distribution object

Plot overlapping histograms

Calculate population misfit

Create random uniform distribution object

Create random chi-squared distribution object

Build a Monte Carlo simulation that the data-generation parameters are from the result of analyzing real data

writeLavaanConstraint

Write a lavaan code for a given set of equality constraints

Extract a part of a vector that is monotonically increasing or decreasing

Validate whether the regression coefficient (or loading) matrix is good

Create parameter sets (with or without model misspecification) from the data object

setOpenMxObject

Rearrange starting values for OpenMx