it.reb {plspm} | R Documentation |
REBUS-PLS is an iterative algorithm for performing response based clustering in a PLS-PM framework.
This function allows to perform the iterative steps of the REBUS-PLS Algorithm (Steps 5-9).
It provides summarized results for final local models and the final partition of the units.
Before running this function, it is necessary to run the res.clus
function to choose the number of classes
to take into account.
it.reb(pls, hclus.res, nk, stop.crit = 0.005, iter.max = 100)
pls |
Object of class "plspm" returned by plspm . |
hclus.res |
Object of class "hclust" returned by res.clus |
nk |
Number of classes to take into account. This value should be defined according to the dendrogram
obtained by performing res.clus . |
stop.crit |
Number indicating the stop criterion for the iterative algorithm. The author suggests using the threshold of less than 0.05% of units changing class from one iteration to the other as stopping rule. |
iter.max |
An integer indicating the maximum number of iterations. Default value = 100. |
This function allows us to perform Steps 5-9 and the iteration procedure of the REBUS-PLS Algorithm.
Moreover, this function provides the final class membership for each unit and summary results
for the final local models.
A threshold of 6 units per class is fixed. If there is a class with less than 6 units, the algorithm stops.
For more details refer to rebus.pls
.
For expert users: if you want to test REBUS-PLS on several number of classes you need to run this
function several times by changing nk
.
An object of class "rebus"
, basically a list with the following elements:
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, the average redundancies, the R2 values, and the GoF index for each local model. |
segments |
Vector defining the class membership of each unit. |
origdata.clas |
The numeric matrix with original data and with a new column defining class membership of each unit. |
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.
http://www.fedoa.unina.it/view/people/Trinchera,_Laura.html
## 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 residuals of global model sim.res.clus <- res.clus(sim.global) ## To conclude run iteration algorithm rebus.sim <- it.reb(sim.global, sim.res.clus, nk=2, 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) ## End(Not run)