How to plot results of clmm()?

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
project.org] On Behalf Of Michael Friendly
Sent: Tuesday, November 05, 2013 10:08 AM
To: thomas; r-help at r-project.org
Cc: John Fox
Subject: Re: [R] How to plot results of clmm()?

On 11/4/2013 9:13 AM, thomas wrote:

Dear list,

I'd like to create a visual plot of a clmm() I've fitted using the
'ordinal' package in R. It's possible to do this with a glm() by

using

the 'effects' package. For example:

    library(effects)
    data(BEPS)
    mod <- lm(political.knowledge ~ age + gender + vote, data=BEPS)
    eff <- effect("age", mod, default.levels=100)
    plot(eff, colors=c("black", "red"))

Produces: http://i.stack.imgur.com/elo4p.png

The 'effects' package does not support clmm:

    mod <- clmm(as.factor(political.knowledge) ~ age + gender +
(1|vote), data=BEPS)
    eff <- effect("age", mod, default.levels=100)

    > Error in UseMethod("effect", mod) :

    no applicable method for 'effect' applied to an object of class

"clmm"

How would I go about doing this? I can't find any examples with clm()

or

clmm() online. Any suggestions would be much appreciated.

You're right that clm() and clmm() models are not supported by the
effects package.  In principle, this would not be too difficult to add,
*if* the ordinal package contained the standard collection of methods
for
'clm' and 'clmm' objects --- coef(), vcov(), and importantly,
predict().
Unfortunately, there is no predict method, and clmm objects don't
inherit from anything else:

 > methods(class="clmm")

  [1] anova.clmm*      condVar.clmm*    extractAIC.clmm* logLik.clmm*
  [5] nobs.clmm*       print.clmm*      ranef.clmm       summary.clmm*
  [9] VarCorr.clmm     vcov.clmm*

 > class(modc)

[1] "clmm"

 >

If there were, you could simply do what effects does yourself --
obtain predicted values (and CIs) over a grid of values, and plot them,

xlevels <- expand.grid(list(age=seq(20,90,10),
               gender=levels(BEPS$gender), vote=levels(BEPS$vote)))

You can, of course, obtain all the fitted values, and plot those,
but that lacks the simplicity of effect plots in averaging over factors
not shown in a given plot.

library(ordinal)
  modc <- clmm(as.factor(political.knowledge) ~ age + gender +
(1|vote), data=BEPS)

BEPS$fitted <- fitted(modc)
plot(fitted~age, data=BEPS, col=c("red", "blue")[gender])



--
Michael Friendly     Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
York University      Voice: 416 736-2100 x66249 Fax: 416 736-5814
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