plspm (version 0.5.1)

rebus.pls: Response Based Unit Segmentation (REBUS)

Description

Performs all the steps of the REBUS-PLS algorithm. Starting from the global model, REBUS allows us to detect local models with better performance.

Usage

rebus.pls(pls, Y = NULL, stop.crit = 0.005,
    iter.max = 100)

Value

An object of class "rebus", basically a list with:

loadings

Matrix of standardized loadings (i.e. correlations with LVs.) for each local model.

path.coefs

Matrix of path coefficients for each local model.

quality

Matrix containing the average communalities, average redundancies, R2 values, and GoF values for each local model.

segments

Vector defining for each unit the class membership.

origdata.clas

The numeric matrix with original data and with a new column defining class membership of each unit.

Arguments

pls

Object of class "plspm"

Y

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

stop.crit

Number indicating the stop criterion for the iterative algorithm. Use a threshold of less than 0.05% of units changing class from one iteration to the other as stopping rule.

iter.max

integer indicating the maximum number of iterations.

Author

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

plspm, res.clus, it.reb, rebus.test, local.models

Examples

if (FALSE) {
 ## typical example of PLS-PM in customer satisfaction analysis
 ## model with six LVs and reflective indicators
 ## example of rebus analysis with simulated data

 # load data
 data(simdata)

 # Calculate plspm
 sim_inner = matrix(c(0,0,0,0,0,0,1,1,0), 3, 3, byrow=TRUE)
 dimnames(sim_inner) = list(c("Price", "Quality", "Satisfaction"),
                            c("Price", "Quality", "Satisfaction"))
 sim_outer = 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(simdata, sim_inner,
                    sim_outer, modes=sim_mod)
 sim_global

 # run rebus.pls and choose the number of classes
 # to be taken into account according to the displayed dendrogram.
 rebus_sim = rebus.pls(sim_global, stop.crit = 0.005, iter.max = 100)

 # You can also compute complete outputs for local models by running:
 local_rebus = local.models(sim_global, rebus_sim)
 }