Package: aspect
Type: Package
Title: Aspects of Multivariables
Version: 0.8-1
Date: 2009-03-23
Author: Jan de Leeuw, Patrick Mair
Maintainer: Jan de Leeuw <deleeuw@stat.ucla.edu>
Description: This package consists of two main functions: The first
function is corAspect() performs optimal scaling by maximizing
an aspect (i.e. target function such as sum eigenvalues, sum of
squared correlations, squared multiple correlations, etc.) of
the corresponding correlation matrix. The second function is
lineals() which performs optimal scaling by mimimization a
non-correlational aspect based on pairwise correlations and
correlation ratios. The resulting correlation matrix and
category scores can be used for further multivariate methods
such as SEM. A platform including related PsychoR packages is
provided on R-forge.
License: GPL
URL: http://r-forge.r-project.org/projects/psychor/
LazyLoad: yes
Packaged: Mon Mar 23 11:29:14 2009; admin
Repository: CRAN
Date/Publication: 2009-03-23 18:38:45

Description: This package consists of two main functions. The first

Author: Jan de Leeuw, Kurt Hornik, Patrick Mair
Description: This package consists of two main functions: The first function is corAspect() performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. The second function is lineals() performs optimal scaling by minimization a non-correlational aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as SEM. A platform including related PsychoR packages is provided on R-Forge.

person(given=c("Jan"), family="De Leeuw", email="deleeuw@stat.ucla.edu", role=c("aut")))

Title: A General Framework for Multivariate Analysis with Optimal
Description: Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.