Defining the matrices needed for the comparison test
F.data.pls(x, inner, outer, mode, scheme, scaling, scaled, modtwo, ...)matrix or data frame containing the data.
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).
vector indicating the binary partition
Further arguments passed on to F.data.pls.
matrix or data.frame of the segmentation variables.
list containing matrices needed for the comparison test
Internal function. F.data.pls is called by test.partition.pls.