proximity

plot.proximity

plot.semforest.proximity

A <code><a rd-options="" href="/link/semforest?package=semtree&version=0.9.12" data-mini-rdoc="semtree::semforest">semforest</a></code> object.

forest

A dataset to compute proximity values for.

dataset

Missingness accounted for. (0 = no, 1 = yes)

type

Boolean marker to compute aggregate proximity scores.

aggregate

An object of class "cluster" representing a parallel socket cluster. See package <a rd-options="parallel" href="/link/makeCluster?package=semtree&version=0.9.12&to=parallel" data-mini-rdoc="parallel::makeCluster">makeCluster</a>.

cluster

&#8230;

A <code><a rd-options="" href="/link/semforest?package=semtree&version=0.9.12" data-mini-rdoc="semtree::semforest">semforest</a></code> process to represent proportion of trees where each case are in the same leaf nodes. The values are bounded (0,1), where higher values are closer in proximity.

 ~kwd1 

 ~kwd2 

SEM Trees and SEM Forests -- an extension of model-based decision
trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
split empirical data into homogeneous groups sharing similar data patterns
with respect to a SEM by recursively selecting optimal predictors of these
differences. SEM forests are an extension of SEM trees. They are ensembles of
SEM trees each built on a random sample of the original data. By aggregating
over a forest, we obtain measures of variable importance that are more robust
than measures from single trees.

Andreas M. Brandmaier andy@brandmaier.de

semtree

Recursive Partitioning for Structural Equation Models

Andreas M. Brandmaier 

John J. Prindle 

proximity function

A <code><a rd-options='' href='semforest'>semforest</a></code> object.

An object of class "cluster" representing a parallel socket cluster. See package <a rd-options='parallel' href='makeCluster'>makeCluster</a>.

A <code><a rd-options='' href='semforest'>semforest</a></code> process to represent proportion of trees where each case are in the same leaf nodes. The values are bounded (0,1), where higher values are closer in proximity.

proximity: SEM Forest Case Proximity

Description

Usage

Arguments

Value

References

See Also