1d:T7a2,# Find factor residual variances from regression coefficient matrix, factor (residual) correlations, and total factor variances Find factor residual variances from regression coefficient matrix, factor (residual) correlation matrix, and total factor variances for latent variable models. In the path analysis model, this function will find indicator residual variances from regression coefficient, indicator (residual) correlation matrix, and total indicator variances. ```r findFactorResidualVar(beta, corPsi, totalVarPsi = NULL, gamma = NULL, covcov = NULL) ``` ## Arguments - `beta`: Regression coefficient matrix among factors - `corPsi`: Factor or indicator residual correlations. - `totalVarPsi`: Factor or indicator total variances. The default is that all factor or indicator total variances are 1. - `gamma`: Regression coefficient matrix from covariates (column) to factors (rows) - `covcov`: A covariance matrix among covariates ## Returns A vector of factor (indicator) residual variances ## Author(s) Sunthud Pornprasertmanit (psunthud@gmail.com ) ## See Also * `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 * `findFactorTotalVar` to find factor total variances * `findFactorTotalCov` to find factor covariances ## Examples ```r 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 totalVar <- rep(1, 9) findFactorResidualVar(path, facCor, totalVar) ```

findFactorResidualVar function