plspm (version 0.1-5)

res.clus: Cluster Analysis on communality and structural residuals of a PLS-PM object

Description

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.

Usage

res.clus(pls)

Arguments

pls

Object of class "plspm" returned by plspm .

Value

• An Object of class "hclust" containing the results of the Hierarchical Cluster Analysis on the communality and structural residuals.

Details

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.

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

plspm, it.reb

Examples

## 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)