Computes variance inflation factors from the covariance matrix of
parameter estimates, using the method of Davis et al. (1986), which
is based on the correlation matrix from the information matrix.

models

regression

Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  'rms' is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  'rms' works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.

Frank Harrell Jr

Regression Modeling Strategies

vif function

<dl><dt>fit</dt>
<dd>an object created by <code>lrm</code>, <code>ols</code>, <code>psm</code>, <code>cph</code>,
<code>Rq</code>, <code>Glm</code>, <code>glm</code></dd>
</dl>

Arguments

Frank Harrell
 
Department of Biostatistics
 
Vanderbilt University
 
fh@fharrell.com

Author

Variance Inflation Factors — vif

<dl>

<dt>fit</dt>
<dd>an object created by <code>lrm</code>, <code>ols</code>, <code>psm</code>, <code>cph</code>,
<code>Rq</code>, <code>Glm</code>, <code>glm</code></dd>
</dl>

Frank Harrell
 
Department of Biostatistics
 
Vanderbilt University
 
fh@fharrell.com

vif: Variance Inflation Factors

Description

Usage

Value

Arguments

Author

References

See Also

Examples