local.models | R Documentation |
Calculates PLS-PM for global and local models from a given partition
local.models(pls, y, Y = NULL)
pls |
An object of class |
y |
One object of the following classes:
|
Y |
Optional dataset (matrix or data frame) used
when argument |
local.models
calculates PLS-PM for the global
model (i.e. over all observations) as well as PLS-PM for
local models (i.e. observations of different partitions).
When y
is an object of class "rebus"
,
local.models
is applied to the classes obtained
from the REBUS algorithm.
When y
is an integer
vector or a
factor
, the values or levels are assumed to
represent the group to which each observation belongs. In
this case, the function local.models
calculates
PLS-PM for the global model, as well as PLS-PM for each
group (local models).
When the object pls
does not contain a data matrix
(i.e. pls$data=NULL
), the user must provide the
data matrix or data frame in Y
.
The original parameters modes
, scheme
,
scaled
, tol
, and iter
from the
object pls
are taken.
An object of class "local.models"
, basically a
list of length k+1
, where k
is the number
of classes.
glob.model |
PLS-PM of the global model |
loc.model.1 |
PLS-PM of segment (class) 1 |
loc.model.2 |
PLS-PM of segment (class) 2 |
loc.model.k |
PLS-PM of segment (class) k |
Each element of the list is an object of class
"plspm"
. Thus, in order to examine the results for
each local model, it is necessary to use the
summary
function.
Laura Trinchera, Gaston Sanchez
rebus.pls
## Not run: ## Example of REBUS PLS with simulated data # load simdata data("simdata", package='plspm') # Calculate global plspm sim_inner = matrix(c(0,0,0,0,0,0,1,1,0), 3, 3, byrow=TRUE) dimnames(sim_inner) = list(c("Price", "Quality", "Satisfaction"), c("Price", "Quality", "Satisfaction")) sim_outer = list(c(1,2,3,4,5), c(6,7,8,9,10), c(11,12,13)) sim_mod = c("A", "A", "A") # reflective indicators sim_global = plspm(simdata, sim_inner, sim_outer, modes=sim_mod) sim_global ## Then compute cluster analysis on residuals of global model sim_clus = res.clus(sim_global) ## To complete REBUS, run iterative algorithm rebus_sim = it.reb(sim_global, sim_clus, nk=2, stop.crit=0.005, iter.max=100) ## You can also compute complete outputs ## for local models by running: local_rebus = local.models(sim_global, rebus_sim) # Display plspm summary for first local model summary(local_rebus$loc.model.1) ## End(Not run)
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