[R] anova(lme.model)

[Mike Marchywka]

> Date: Sat, 6 Nov 2010 07:45:26 -0700
> From: gunter.berton at gene.com
> To: sibylle.stoeckli at gmx.ch
> CC: r-help at 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
>






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