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
>
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