res.clus: Clustering on communality and structural residuals

In plspm: Partial Least Squares Path Modeling (PLS-PM)

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 "plspm"
Y	Optional dataset (matrix or data frame) used when argument dataset=NULL inside pls.

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

it.reb, plspm

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)

plspm documentation built on March 31, 2023, 11:54 p.m.