Usage
splitopt.pls(x, inner, outer, mode, scheme, scaling, scaled, splits, fact,
method, n.node, ...)
Arguments
x
matrix or data frame containing the data.
inner
A square (lower triangular) boolean matrix representing the inner
model (i.e. the path relationships between latent variables).
outer
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).
mode
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.
scheme
string indicating the type of inner weighting
scheme. Possible values are "centroid", "factorial", or
"path".
scaling
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" (numer
scaled
whether manifest variables should be standardized.
Only used when scaling = NULL. When (TRUE, data is
scaled to standardized values (mean=0 and variance=1).
splits
vector indicating the binary partition
fact
vector indicating the variable
method
string indicating the method: LM or LAD
n.node
number indicating a stop condition