findIndResidualVar: Find indicator residual variances from factor loading matrix,...

In simsem: SIMulated Structural Equation Modeling

Description

Find indicator (measurement) residual variances from a factor loading matrix, total factor covariance matrix, and total indicator variances.

Usage

findIndResidualVar(lambda, totalFactorCov, totalVarTheta = NULL, 
	kappa = NULL, covcov = NULL)

Arguments

lambda	Factor loading matrix
totalFactorCov	Total (model-implied) covariance matrix among factors.
totalVarTheta	Indicator total variances. As a default, all total variances are 1.
kappa	Regression coefficient matrix from covariates (column) to indicators (rows)
covcov	A covariance matrix among covariates

Value

A vector of indicator residual variances.

Author(s)

Sunthud Pornprasertmanit ([e-mail])

See Also

• findIndIntercept to find indicator (measurement) intercepts

• findIndMean to find indicator (measurement) total means

• findIndTotalVar to find indicator (measurement) total variances

• findFactorIntercept to find factor intercepts

• findFactorMean to find factor means

• findFactorResidualVar to find factor residual variances

• findFactorTotalVar to find factor total variances

• findFactorTotalCov to find factor covariances

Examples

loading <- matrix(0, 6, 2)
loading[1:3, 1] <- c(0.6, 0.7, 0.8)
loading[4:6, 2] <- c(0.6, 0.7, 0.8)
facCov <- matrix(c(1, 0.5, 0.5, 1), 2, 2)
totalVar <- rep(1, 6)
findIndResidualVar(loading, facCov, totalVar)

Example output

Loading required package: lavaan
This is lavaan 0.5-23.1097
lavaan is BETA software! Please report any bugs.
 
###############################################################################################
This is simsem 0.5-13
simsem is BETA software! Please report any bugs.
simsem was developed at the University of Kansas Center for Research Methods and Data Analysis.
###############################################################################################
[1] 0.64 0.51 0.36 0.64 0.51 0.36

simsem documentation built on March 29, 2021, 1:07 a.m.