Sounds to me like you should really be seeking help from your local
statistician, not this list. What you request probably cannot be done.
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"
<sibylle.stoec...@gmx.ch> 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
>>
>
> --
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Bert Gunter
Genentech Nonclinical Biostatistics
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
