Count the number of elements in the sufficient statistics
Count how many free parameters in the target object
Extract a part of an object
Extract the fit indices reported by the OpenMx result
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.
Create a free parameters object from a model specification
Find fit indices cutoff given a priori alpha level
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
Find standardized factor loading from coefficient alpha
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
Find skewness
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
Class "MatrixSet"
Class "SimDataOut"
Class "SimParam"
Create a constant vector object
Make a division on each element of the object
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
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
Write a lavaan code for a null model
Standardize the parameter estimates within an object
Class "SimModel"
Combine exogenous factor correlation and endogenous factor correlation into a single matrix
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
Summarize the data generation population model underlying an object
Write a lavaan code given the matrices of free parameter
Class "SimGenLabels"
Extract only converged replications in the result object
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
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
Class "SimModelOut"
Class "SimFunction"
Extract only converged replications in the result objects
Create parameter sets (with or without model misspecification) from the parameter with or without misspecification set
Find a value of varying parameters that provides a given value of power.
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)
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
Plot multiple logistic curves for predicting whether rejecting a misspecified model
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
Check the value argument in the matrix, symmetric matrix, or vector objects
Class "SimModelMIOut"
Find factor residual variances from regression coefficient matrix, factor (residual) correlations, and total factor variances
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
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
Find two-tailed p value from one-tailed p value
Validate whether all elements provides a good covariance matrix
Class "SimREqualCon"
Matrix object: Random parameters matrix
Class "SimResult"
Change all elements in the non-null objects to be all NAs.
Fit the 2D Kernel Density Estimate
Symmetric matrix object: Random parameters symmetric matrix
Find the appropriate position for freely estimated correlation (or covariance) given a regression coefficient matrix
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
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
Change an object to a vector with labels
Class "SimDataDist"
Vector object: Random parameters vector
Provide a comparison of nested models and nonnested models across replications
Convert a covariance matrix to a correlation matrix
Create model implied mean vector and covariance matrix
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
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.
Class "SimMissing"
Class "SimResultParam"
Calculate central moments of a variable
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
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
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"
Class "SimEqualCon"
Distribution Objects
Change an element in SimMatrix, SymMatrix, or SimVector.
Find factor intercept from regression coefficient matrix and factor total means
Find factor total covariance from regression coefficient matrix, factor residual covariance
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
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
Class "SimMisspec"
Get a quantile of a variable given values of predictors
Find power of model parameters when simulations have randomly varying parameters
Class "SimData"
Combine the regression coefficient matrices
Impose an equality constraint in an object
Calculate the k -statistic of a variable
Null Objects
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
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
Plot power of rejecting a nested model in a nested model comparison by each fit index
Create a model object
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
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
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
Rearrange starting values for OpenMx