res.clus {plspm} | R Documentation |
Clustering on communality and structural residuals
Description
Computes communality and structural residuals from a global PLS-PM model and performs a Hierarchical Cluster Analysis on these residuals according to the REBUS algorithm.
Usage
res.clus(pls, Y = NULL)
Arguments
pls |
Object of class |
Y |
Optional dataset (matrix or data frame) used
when argument |
Details
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.
Value
An Object of class "hclust"
containing the results
of the Hierarchical Cluster Analysis on the communality
and structural residuals.
Author(s)
Laura Trinchera, Gaston Sanchez
References
Esposito Vinzi V., Trinchera L., Squillacciotti S., and Tenenhaus M. (2008) REBUS-PLS: A Response-Based Procedure for detecting Unit Segments in PLS Path Modeling. Applied Stochastic Models in Business and Industry (ASMBI), 24, pp. 439-458.
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.
See Also
Examples
## Not run:
## 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)
## End(Not run)