CoTiMA (version 0.7.0)

ctmaEmpCov: 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(),
  nlatents = NULL,
  Tpoints = NULL,
  sampleSize = NULL,
  pairwiseN = NULL,
  empcov = NULL
)

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.

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

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,
                        nlatents = 2, sampleSize = sampleSize17,
                        Tpoints = 2, empcov = empcov17)
empcov17 <- results17$r