plspm (version 0.4.9)

rebus.test: Permutation Test for REBUS Multi-Group Comparison

Description

Performs permutation tests for comparing pairs of groups from a REBUS object.

Usage

rebus.test(pls, reb, Y = NULL)

Arguments

pls
Object of class "plspm" returned by plspm
reb
Object of class "rebus" returned by either rebus.pls or it.reb.
Y
Optional dataset (matrix or data frame) used when argument dataset=NULL inside pls.

Value

An object of class "rebus.test", basically a list containing the results of each pair of compared classes. In turn, each element of the list is also a list with the results for the path coefficients, loadings, and GoF index.

Details

A permutation test on path coefficients, loadings, and GoF index is applied to the classes obtained from REBUS, by comparing two classes at a time. That is to say, a permutation test is applied on pair of classes. The number of permutations in each test is 100. In turn, the number of classes handled by rebus.test is limited to 6. When pls$data=NULL (there is no data matrix), the user must provide the data matrix or data frame in Y.

References

Chin, W.W. (2003) A permutation procedure for multi-group comparison of PLS models. In: Vilares M., Tenenhaus M., Coelho P., Esposito Vinzi V., Morineau A. (Eds.) PLS and Related Methods - Proceedings of the International Symposium PLS03. Decisia, pp. 33-43.

See Also

rebus.pls, local.models

Examples

Run this code
## Not run: ------------------------------------
#  ## 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_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_mod = c("A", "A", "A")  # reflective indicators
#  sim_global = plspm(simdata, sim_path,
#                     sim_blocks, modes=sim_mod)
#  sim_global
# 
#  # Cluster analysis on residuals of global model
#  sim_clus = res.clus(sim_global)
# 
#  # Iterative steps of REBUS algorithm on 2 classes
#  rebus_sim = it.reb(sim_global, sim_clus, nk=2,
#                     stop.crit=0.005, iter.max=100)
# 
#  # apply rebus.test
#  sim_permu = rebus.test(sim_global, rebus_sim)
# 
#  # inspect sim.rebus
#  sim_permu
#  sim_permu$test_1_2
# 
#  # or equivalently
#  sim_permu[[1]]
#  
## ---------------------------------------------

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