# ctmaEmpCov changes a full covariance matrix by selecting target variables, recoding them, combining them (compute the mean of two or more variables), and by adding rows/columns with NA if focal variables are not available. ```r ctmaEmpCov( targetVariables = NULL, recodeVariables = c(), combineVariables = c(), combineVariablesNames = c(), missingVariables = c(), n.latent = NULL, Tpoints = NULL, sampleSize = NULL, pairwiseN = NULL, empcov = NULL ) ``` ## Arguments - `targetVariables`: (col-/row-) number or names of the target variables - `recodeVariables`: (col-/row-) number or names of the target variables require inverse coding - `combineVariables`: list of vectors, which put together the targeted variables that should be used for composite variables - `combineVariablesNames`: new names for combined variables - not really important - `missingVariables`: missing variables - `n.latent`: number of (latent) variables - actually it is the number of all variables - `Tpoints`: number of time points. - `sampleSize`: sample size - `pairwiseN`: matrix of same dimensions as empcov containing possible pairwiseN. - `empcov`: empirical correlation matrix ## Returns returns a list with two elements. The first element (results$r) contains the adapted correlation matrix, and the second element (results$pairwiseNNew) an adapted version of a matrix of pairwise N if pariwiseN was provided for the original correlation matrix supplied. ## Examples ```r source17 <- c() delta_t17 <- c(12) sampleSize17 <- 440 empcov17 <- matrix( c( 1.00, -0.60, -0.36, 0.20, 0.62, -0.47, -0.18, 0.20, -0.60, 1.00, 0.55, -0.38, -0.43, 0.52, 0.27, -0.21, -0.36, 0.55, 1.00, -0.47, -0.26, 0.37, 0.51, -0.28, 0.20, -0.38, -0.47, 1.00, 0.15, -0.28, -0.35, 0.56, 0.62, -0.43, -0.26, 0.15, 1.00, -0.63, -0.30, 0.27, -0.47, 0.52, 0.37, -0.28, -0.63, 1.00, 0.55, -0.37, -0.18, 0.27, 0.51, -0.35, -0.30, 0.55, 1.00, -0.51, 0.20, -0.21, -0.28, 0.56, 0.27, -0.37, -0.51, 1.00), nrow=8, ncol=8) moderator17 <- c(3, 2) rownames(empcov17) <- colnames(empcov17) <- c("Workload_1", "Exhaustion_1", "Cynicism_1", "Values_1", "Workload_2", "Exhaustion_2", "Cynicism_2", "Values_2") targetVariables17 <- c("Workload_1", "Exhaustion_1", "Cynicism_1", "Workload_2", "Exhaustion_2", "Cynicism_2") recodeVariables17 <- c("Workload_1", "Workload_2") combineVariables17 <- list("Workload_1", c("Exhaustion_1", "Cynicism_1"), "Workload_2", c("Exhaustion_2", "Cynicism_2")) combineVariablesNames17 <- c("Demands_1", "Burnout_1", "Demands_2", "Burnout_2") missingVariables17 <- c(); results17 <- ctmaEmpCov(targetVariables = targetVariables17, recodeVariables = recodeVariables17, combineVariables = combineVariables17, combineVariablesNames = combineVariablesNames17, missingVariables = missingVariables17, n.latent = 2, sampleSize = sampleSize17, Tpoints = 2, empcov = empcov17) empcov17 <- results17$r ```

ctmaEmpCov function