splitopt.pls: Defining optimum partition for a specific variable.

Description

Defining optimum partition for a specific variable.

Usage

splitopt.pls(
  x,
  inner,
  outer,
  mode,
  scheme,
  scaling,
  scaled,
  splits,
  fact,
  method,
  n.node,
  ...
)

Arguments

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" (numeric), "raw", "nom" (nominal), and "ord" (ordinal).

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

…

Further arguments passed on to splitopt.pls.

Value

list containing information of the optimum partition for a specific variable

Details

Internal function. splitopt.pls is called by all.part.pls.