Dear Rosario,
Because of missing data in the additional variable PHt, the two models
weren't fit to the same subset of valid observations -- the default in lm()
is to use complete cases for the variables in the model.
A mechanical solution is to use na.omit() to filter your data set, only for
the variables you intend to use, to produce a data set with no NAs. Then
you'll fit each model to a consistent subset of valid cases.
Of course, if you have a substantial amount of missing data, complete-case
analysis is probably a poor strategy.
I hope this helps,
John
--------------------------------
John Fox
Senator William McMaster
Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org]
> On Behalf Of Rosario Garcia Gil
> Sent: January-21-11 9:31 AM
> To: r-help at r-project.org
> Subject: [R] Error in ANOVA for model comparison
>
> Hello
>
> I am trying to compare two models using anova(), however I get a message
> error (see below).
> In the net I only found some information on certain library(car) for
> which one should use anova with A capital letter (Anova instead of
> anova), but I could not find car library as it says it does not exist.
>
>
> > Model <- lm(interceptG ~ SW + TSC + FSC + PF + SlopeG + K,
> data=AllTrait)
> > Model1 <- lm(interceptG ~ SW + TSC + FSC + PF + SlopeG + PHt,
> data=AllTrait)
>
> Error in anova.lmlist(object, ...) :
> models were not all fitted to the same size of dataset
>
> I have NA in the datafile, should that be the problem?
>
> Kind regards and thanks in advance
> Rosario
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