Defining the candidates to the optimum partition for each of segmentation variables
# S3 method for part.pls
all(x, y, inner, outer, mode, scheme, scaling, scaled, method, n.node, ...)matrix or data frame containing the data.
matrix or data.frame of the segmentation variables.
A square (lower triangular) boolean matrix representing the inner model (i.e. the path relationships between latent variables).
list of vectors with column indices or column names from Data indicating the sets of manifest variables forming each block (i.e. which manifest variables correspond to each block).
character vector indicating the type of measurement for each
block. Possible values are: "A", "B", "newA", "PLScore", "PLScow".
The length of mode must be equal to the length of outer.
string indicating the type of inner weighting
scheme. Possible values are "centroid", "factorial", or
"path".
optional list of string vectors indicating the type of
measurement scale for each manifest variable specified in blocks.
scaling must be specified when working with non-metric variables.
Possible values: "num" (numeric), "raw", "nom" (nominal),
and "ord" (ordinal).
whether manifest variables should be standardized.
Only used when scaling = NULL. When (TRUE, data is
scaled to standardized values (mean=0 and variance=1).
string indicating the method: LM or LAD
number indicating a stop condition
Further arguments passed on to all.part.pls.
list containing information of the candidates to the optimum partition for each of segmentation variables
Internal function. all.part.pls is called by partopt.pls.