alpha.optim: Optimal value of alpha in Gradient Projection algorithm.

Description Usage Arguments Value Author(s) References See Also

View source: R/alpha.optim.R

Description

This function gives a value of alpha which guarantees monotone decrease of the objective function in "obliclus".

Usage

1
	alpha.optim(A, T, G, cluster, info, maxit = 1000)

Arguments

A

The loading matrix for rotation.

T

The current value of rotation matrix.

G

The gradient of the objective function at T on the set of oblique rotation matrices.

cluster

The vector of cluster parameters which indicate a cluster where each variable is assigned.

info

The list including an initial value of alpha.

maxit

The limit of the iteration of partial step modification for the value of alpha.

Value

The value of alpha which is calculated by the partial step modification described in Jennrich (2002).

Author(s)

Michio Yamamoto
michio.koko@gmail.com

References

Jennrich, R.I. (2012). A simple general method for oblique rotation. Psychometrika, 67, 7-20.

See Also

obliclus


obliclus documentation built on May 2, 2019, 6:10 a.m.