da.lmWithCov.fit

base.cov

<p>Uses \(R^2\) (coefficient of determination)
See <code><a rd-options="" href="/link/lmWithCov?package=dominanceanalysis&version=2.0.0" data-mini-rdoc="dominanceanalysis::lmWithCov">lmWithCov</a></code></p>

Dominance analysis is a method that allows to compare the
relative importance of predictors in multiple regression models:
ordinary least squares, generalized linear models,
hierarchical linear models, beta regression and dynamic linear models.
The main principles and methods of
dominance analysis are described in
Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and
Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>
for ordinary least squares regression. Subsequently, the extensions
for multivariate regression, logistic regression and
hierarchical linear models were described in
Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>,
Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and
Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>,
respectively.

Claudio Bustos Navarrete

dominanceanalysis

Dominance Analysis

Filipa Coutinho Soares

da.lmWithCov.fit function

<p>Uses \(R^2\) (coefficient of determination)
See <code><a rd-options='' href='lmWithCov'>lmWithCov</a></code></p>

The Learning Leader's Guide to AI Literacy

da.lmWithCov.fit: Provides coefficient of determination for linear models, using covariance/correlation matrix.

Description

Usage

Arguments

See Also