I said nothing about legitimacy. I only suggested what I thought was a
more satisfactory way the OP could get the issues resolved, since they
seemed to go beyond R. The R-sig-mixed-models (check spelling) list
might be a good place to look.
I believe Th R-help archives not the packages contain Doug Bates's
comments on the issue.
-- Bert
On Sat, Nov 6, 2010 at 11:30 AM, Mike Marchywka <marchy...@hotmail.com> wrote:
>
>> Date: Sat, 6 Nov 2010 07:45:26 -0700
>> From: gunter.ber...@gene.com
>> To: sibylle.stoec...@gmx.ch
>> CC: r-help@r-project.org
>> Subject: Re: [R] anova(lme.model)
>>
>> Sounds to me like you should really be seeking help from your local
>> statistician, not this list. What you request probably cannot be done.
>
>
> I'm still bringing my install up to speed so I can't immediately
> read the cited R stuff below but it sounds like the OP
> mentions a controversy documented in the R packages. Is there
> a list for discussing these topics? Offhand that seems legitimate
> for a user help list unless you want people to believe that
> " it came out of a computer so it must be right, whatever a P value
> is."
>
>
>>
>> What is wrong with what you get from lme, whose results seem fairly
>> clear whether the P values are accurate or not?
>>
>> Cheers,
>> Bert
>>
>>
>>
>>
>>
>> On Sat, Nov 6, 2010 at 4:04 AM, "Sibylle Stöckli"
>> wrote:
>> > Dear R users
>> >
>> > Topic: Linear effect model fitting using the nlme package (recomended by
>> > Pinheiro et al. 2008 for unbalanced data set).
>> >
>> > The R help provides much info about the controversy to use the
>> > anova(lme.model) function to present numerator df and F values.
>> > Additionally different p-values calculated by lme and anova are reported.
>> > However, I come across the same problem, and I would very much appreciate
>> > some R help to fit an anova function to get similar p-values compared to
>> > the lme function and additionally to provide corresponding F-values. I
>> > tried to use contrasts and to deal with the ‚unbalanced data set’.
>> >
>> > Thanks
>> > Sibylle
>> >
>> >> Kaltenborn<-read.table("Kaltenborn_YEARS.txt", na.strings="*",
>> >> header=TRUE)
>> >>
>> >>
>> >> library(nlme)
>> >
>> >> model5c<-lme(asin(sqrt(PropMortality))~Diversity+
>> >> Management+Species+Height+Height*Diversity, data=Kaltenborn,
>> >> random=~1|Plot/SubPlot, na.action=na.omit,
>> >> weights=varPower(form=~Diversity), subset=Kaltenborn$ADDspecies!=1,
>> >> method="ML")
>> >
>> >> summary(model5c)
>> > Linear mixed-effects model fit by maximum likelihood
>> > Data: Kaltenborn
>> > Subset: Kaltenborn$ADDspecies != 1
>> > AIC BIC logLik
>> > -249.3509 -205.4723 137.6755
>> >
>> > Random effects:
>> > Formula: ~1 | Plot
>> > (Intercept)
>> > StdDev: 0.06162279
>> >
>> > Formula: ~1 | SubPlot %in% Plot
>> > (Intercept) Residual
>> > StdDev: 0.03942785 0.05946185
>> >
>> > Variance function:
>> > Structure: Power of variance covariate
>> > Formula: ~Diversity
>> > Parameter estimates:
>> > power
>> > 0.7302087
>> > Fixed effects: asin(sqrt(PropMortality)) ~ Diversity + Management +
>> > Species + Height + Height * Diversity
>> > Value Std.Error DF t-value p-value
>> > (Intercept) 0.5422893 0.05923691 163 9.154585 0.0000
>> > Diversity -0.0734688 0.02333159 14 -3.148896 0.0071
>> > Managementm+ 0.0217734 0.02283375 30 0.953562 0.3479
>> > Managementu -0.0557160 0.02286694 30 -2.436532 0.0210
>> > SpeciesPab -0.2058763 0.02763737 163 -7.449198 0.0000
>> > SpeciesPm 0.0308005 0.02827782 163 1.089210 0.2777
>> > SpeciesQp 0.0968051 0.02689327 163 3.599602 0.0004
>> > Height -0.0017579 0.00031667 163 -5.551251 0.0000
>> > Diversity:Height 0.0005122 0.00014443 163 3.546270 0.0005
>> > Correlation:
>> > (Intr) Dvrsty Mngmn+ Mngmnt SpcsPb SpcsPm SpcsQp Height
>> > Diversity -0.867
>> > Managementm+ -0.173 -0.019
>> > Managementu -0.206 0.005 0.499
>> > SpeciesPab -0.253 0.085 0.000 0.035
>> > SpeciesPm -0.239 0.058 0.001 0.064 0.521
>> > SpeciesQp -0.250 0.041 -0.001 0.032 0.502 0.506
>> > Height -0.518 0.532 -0.037 -0.004 0.038 0.004 0.033
>> > Diversity:Height 0.492 -0.581 0.031 -0.008 -0.149 -0.099 -0.069 -0.904
>> >
>> > Standardized Within-Group Residuals:
>> > Min Q1 Med Q3 Max
>> > -2.99290873 -0.60522612 -0.05756772 0.62163049 2.80811502
>> >
>> > Number of Observations: 216
>> > Number of Groups:
>> > Plot SubPlot %in% Plot
>> > 16 48
>> >
>> >> anova(model5c)
>> > numDF denDF F-value p-value
>> > (Intercept) 1 163 244.67887 <.0001
>> > Diversity 1 14 1.53025 0.2364
>> > Management 2 30 6.01972 0.0063
>> > Species 3 163 51.86699 <.0001
>> > Height 1 163 30.08090 <.0001
>> > Diversity:Height 1 163 12.57603 0.0005
>> >>
>> >
>>
>> --
>> Bert Gunter
>> Genentech Nonclinical Biostatistics
>>
>
>
>
>
>
>
> Mike Marchywka | V.P. Technology
>
> 415-264-8477
> marchy...@phluant.com
>
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>
>
>
--
Bert Gunter
Genentech Nonclinical Biostatistics
467-7374
http://devo.gene.com/groups/devo/depts/ncb/home.shtml
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