Description Usage Arguments Value Author(s) See Also Examples
Find factor total covariances from regression coefficient matrix, factor residual covariance matrix. The residual covaraince matrix might be derived from factor residual correlation, total variance, and error variance. This function can be applied for path analysis model as well.
1 2 |
beta |
Regression coefficient matrix among factors |
psi |
Factor or indicator residual covariances. This argument can be skipped if factor residual correlation and either total variances or error variances are specified. |
corPsi |
Factor or indicator residual correlation. This argument must be specified with total variances or error variances. |
totalVarPsi |
Factor or indicator total variances. |
errorVarPsi |
Factor or indicator residual variances. |
gamma |
Regression coefficient matrix from covariates (column) to factors (rows) |
covcov |
A covariance matrix among covariates |
A matrix of factor (model-implied) total covariance
Sunthud Pornprasertmanit (psunthud@gmail.com)
findIndIntercept
to find indicator (measurement) intercepts
findIndMean
to find indicator (measurement) total means
findIndResidualVar
to find indicator (measurement) residual variances
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
1 2 3 4 5 6 7 8 9 10 11 12 | path <- matrix(0, 9, 9)
path[4, 1] <- path[7, 4] <- 0.6
path[5, 2] <- path[8, 5] <- 0.6
path[6, 3] <- path[9, 6] <- 0.6
path[5, 1] <- path[8, 4] <- 0.4
path[6, 2] <- path[9, 5] <- 0.4
facCor <- diag(9)
facCor[1, 2] <- facCor[2, 1] <- 0.4
facCor[1, 3] <- facCor[3, 1] <- 0.4
facCor[2, 3] <- facCor[3, 2] <- 0.4
residualVar <- c(1, 1, 1, 0.64, 0.288, 0.288, 0.64, 0.29568, 0.21888)
findFactorTotalCov(path, corPsi=facCor, errorVarPsi=residualVar)
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