
res.clus(pls, Y = NULL)
"plspm"
dataset=NULL
inside pls
."hclust"
containing the results
of the Hierarchical Cluster Analysis on the communality
and structural residuals.res.clus()
comprises the second and third steps of
the REBUS-PLS Algorithm. It computes communality and
structural residuals. Then it performs a Hierarchical
Cluster Analysis on these residuals (step three of
REBUS-PLS Algorithm). As a result, this function directly
provides a dendrogram obtained from a Hierarchical
Cluster Analysis.Trinchera, L. (2007) Unobserved Heterogeneity in Structural Equation Models: a new approach to latent class detection in PLS Path Modeling. Ph.D. Thesis, University of Naples "Federico II", Naples, Italy.
it.reb
, plspm
## example of rebus analysis with simulated data
# load data
data(simdata)
# Calculate plspm
sim_path = matrix(c(0,0,0,0,0,0,1,1,0), 3, 3, byrow=TRUE)
dimnames(sim_path) = list(c("Price", "Quality", "Satisfaction"),
c("Price", "Quality", "Satisfaction"))
sim_blocks = list(c(1,2,3,4,5), c(6,7,8,9,10), c(11,12,13))
sim_modes = c("A", "A", "A")
sim_global = plspm(simdata, sim_path,
sim_blocks, modes=sim_modes)
sim_global
# Then compute cluster analysis on the residuals of global model
sim_clus = res.clus(sim_global)
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