
it.reb
allows to perform the iterative steps of
the REBUS-PLS Algorithm. 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, Y = NULL, stop.crit = 0.005,
iter.max = 100)
"plspm"
"res.clus"
returned by res.clus
res.clus
.pls$data
is NULL
"rebus"
Trinchera, L. (2007) Unobserved Heterogeneity in Structural Equation Models: a new approach to latent class detection in PLS Path Modeling. Ph.D. Thesis, University of Naples "Federico II", Naples, Italy.
plspm
, rebus.pls
,
res.clus
## Example of REBUS PLS with simulated data
# load simdata
data("simdata", package='plspm')
# Calculate global plspm
sim_inner = matrix(c(0,0,0,0,0,0,1,1,0), 3, 3, byrow=TRUE)
dimnames(sim_inner) = list(c("Price", "Quality", "Satisfaction"),
c("Price", "Quality", "Satisfaction"))
sim_outer = list(c(1,2,3,4,5), c(6,7,8,9,10), c(11,12,13))
sim_mod = c("A", "A", "A") # reflective indicators
sim_global = plspm(simdata, sim_inner,
sim_outer, modes=sim_mod)
sim_global
## Then compute cluster analysis on residuals of global model
sim_clus = res.clus(sim_global)
## To complete REBUS, run iterative algorithm
rebus_sim = it.reb(sim_global, sim_clus, nk=2,
stop.crit=0.005, iter.max=100)
## You can also compute complete outputs
## for local models by running:
local_rebus = local.models(sim_global, rebus_sim)
# Display plspm summary for first local model
summary(local_rebus$loc.model.1)
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