[R] What is the most useful way to detect nonlinearity in logisticregression?
John Fox
jfox at mcmaster.ca
Sun Dec 5 05:41:07 CET 2004
Dear Patrick,
Component+residual plots can be defined for generalized linear models
(including logistic regression) as for linear models, but they may require
smoothing for interpretation. See, e.g., the cr.plots() functions in the car
package, which works with glm objects.
I hope this helps,
John
--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox
--------------------------------
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Patrick Foley
> Sent: Saturday, December 04, 2004 7:49 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] What is the most useful way to detect
> nonlinearity in logisticregression?
>
> It is easy to spot response nonlinearity in normal linear
> models using plot(something.lm).
> However plot(something.glm) produces artifactual
> peculiarities since the diagnostic residuals are constrained
> by the fact that y can only take values 0 or 1.
> What do R users find most useful in checking the linearity
> assumption of logistic regression (i.e. log-odds =a+bx)?
>
> Patrick Foley
> patfoley at csus.edu
>
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