plspm (version 0.4.1)

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)

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.

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.

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.

http://www.fedoa.unina.it/view/people/Trinchera,_Laura.html

See Also

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

Examples

## 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, inner=sim_inner,
                    outer=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)