## example of rebus analysis
data(sim.data)
## First compute GLOBAL model
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(sim.data, inner=sim.inner,
outer=sim.outer, modes=sim.mod)
sim.global
## Then compute cluster on residual from 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 )
## Computation of local models
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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