ctmaEmpCov: ctmaEmpCov In CoTiMA: Continuous Time Meta-Analysis ('CoTiMA')

Description

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.

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 ctmaEmpCov( targetVariables = NULL, recodeVariables = c(), combineVariables = c(), combineVariablesNames = c(), missingVariables = c(), nlatents = 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 nlatents 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

Value

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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 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, nlatents = 2, sampleSize = sampleSize17, Tpoints = 2, empcov = empcov17) empcov17 <- results17\$r

CoTiMA documentation built on Jan. 20, 2022, 5:06 p.m.