ctmaEmpCov: ctmaEmpCov

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

ctmaEmpCov

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

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

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

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

CoTiMA documentation built on May 29, 2024, 11:39 a.m.