cbsem (version 1.0.0)

clustergscairls: Clustering gsc-models

Description

clustergscairls clusters data sets in that way that each cluster has a its own set of coefficients in the gsc-model.

Usage

clustergscairls(dat, B, indicatorx, indicatory, loadingx = FALSE,
  loadingy = FALSE, k, minmem = FALSE, wieder)

Arguments

dat

(n,p)-matrix, the values of the manifest variables

B

(q,q) lower triangular matrix describing the interrelations of the latent variables: b_ij= 1 regression coefficient of eta_j in the regression relation in which eta_i is b_ij= 0 if eta_i does not depend on eta_j in a direct way (b_ii = 0 !)

indicatorx

vector describing with which exogenous composite the X-variables are connected

indicatory

vector describing with which endogenous composite the Y-variables are connected

loadingx

logical TRUE when there are loadings for the X-variables in the model

loadingy

logical TRUE when there are loadings for the Y-variables in the model

k

scalar, the number of clusters to be found

minmem

number of the cluster's members or FALSE (then ist is set to 2*number of indicators)

wieder

scalar, the number of random starts

Value

out list with components

member	(n,1)-vector, indicator of membership
Bhat	(k,q,q)-array, the path coefficients of the clusters
lambda	(p,k)-matrix, the loadings of the clusters
fitall	the total fit measure for the structural models only
fit	vector of length k, the fit values of the different models

Examples

# NOT RUN {
data(twoclm)
dat <- twoclm[,-10]
B <- matrix(c( 0,0,0, 0,0,0, 1,1,0),3,3,byrow=TRUE)
indicatorx <- c(1,1,1,2,2,2)
indicatory <- c(1,1,1)  
out <- clustergscairls(dat,B,indicatorx,indicatory,loadingx=FALSE,loadingy=FALSE,2,minmem=6,1) 
# }