res.clus {plspm} | R Documentation |
This function computes communality and structural residuals from the global model and performs a Hierarchical Cluster Analysis on these residuals according to the REBUS algorithm.
res.clus(pls)
pls |
Object of class "plspm" returned by plspm . |
The function res.clus
comprises the second and third steps of the REBUS-PLS Algorithm.
In particular it computes communality and structural residuals (as defined in Trinchera (2007) and
Esposito Vinzi et al. (2008)) of each unit from the global model (step two of REBUS-PLS Algorithm).
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.
The number of classes (K) to be taken into account during the successive steps of the REBUS-PLS
Algorithm (performed by it.reb
), and the initial composition of the classes are obtained according
to the results of the Hierarchical Cluster Analysis. Users must choose K according to this dendrogram and use it as an argument in the
it.reb
function.
An Object of class "hclust"
containing the results of the Hierarchical Cluster Analysis
on the communality and structural residuals.
Laura Trinchera, Gaston Sanchez
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.
## Not run: ## example of rebus analysis with simulated data data(sim.data) ## First compute GLOBAL model sim.mat <- matrix(c(0,0,0,0,0,0,1,1,0),3,3,byrow=TRUE) dimnames(sim.mat) <- list(c("Price","Quality","Satisfaction"), c("Price","Quality","Satisfaction")) sim.sets <- list(c(1,2,3,4,5),c(6,7,8,9,10),c(11,12,13)) sim.mod <- c("A","A","A") ## reflective indicators sim.global <- plspm(sim.data, sim.mat, sim.sets, sim.mod) sim.global ## Then compute cluster analysis on the residuals of global model sim.res.clus <- res.clus(sim.global) ## End(Not run)