I am running an lme model with the main effects of four fixed variables (3
continuous and one categorical see below) and one random variable. The
data describe the densities of a mite species awsm in relation to four
variables: adh31 (temperature related), apsm (another plant feeding mite)
awpm (a predatory mite), and orien (sampling location within plant north
or south).
I have read in Pinheiro and Bates that anova(model) can be used to asses the
significance of fixed factors. In my case, anova(model) gives different
results than summary(model) and I am not sure which p values I should use as
a guide for model simplification.
I have tried using either as a guide, but I get to a point where
summary(model) or anova(model) suggest that a factor is not significant (p
value>0.05) but when I remove it and compare the model with and without the
F value is significant the same is true for all three factors that appear
as non significant in my final model. It makes me a bit suspicious that the
F-value for the deletion test is always 0.0099 independently of the factor
that I remove. Any suggestions greatly appreciated.
The actual data follow at the end of the R code.
Thanks,
Mel
> library(nlme)
> model<-lme(awsm~adh31+awpm+apsm+orien,random=~1|viney)
> summary(model)
Linear mixed-effects model fit by REML
Data: NULL
AIC BIC logLik
49.84102 51.22159 -17.92051
Random effects:
Formula: ~1 | viney
(Intercept) Residual
StdDev: 1.59297 0.2689783
Fixed effects: awsm ~ adh31 + awpm + apsm + orien
Value Std.Error DF t-value p-value
(Intercept) 0.7192961 0.8020099 7 0.8968669 0.3996
adh31 0.3105583 0.3175280 2 0.9780504 0.4312
awpm 0.4373813 0.2282457 2 1.9162743 0.1954
apsm 0.1487537 0.2099112 2 0.7086502 0.5520
oriensouth -0.5599473 0.2254709 2 -2.4834566 0.1310
Correlation:
(Intr) adh31 awpm apsm
adh31 -0.636
awpm -0.440 0.451
apsm 0.317 -0.756 -0.310
oriensouth 0.433 -0.608 -0.274 0.201
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.81103399 -0.31639155 -0.03371192 0.29211809 0.80633666
Number of Observations: 14
Number of Groups: 8
> intervals(model)
Approximate 95% confidence intervals
Fixed effects:
lower est. upper
(Intercept) -1.1771559 0.7192961 2.6157481
adh31 -1.0556542 0.3105583 1.6767709
awpm -0.5446806 0.4373813 1.4194432
apsm -0.7544215 0.1487537 1.0519289
oriensouth -1.5300704 -0.5599473 0.4101758
attr(,"label")
[1] "Fixed effects:"
Random Effects:
Level: viney
lower est. upper
sd((Intercept)) 0.9312527 1.59297 2.724882
Within-group standard error:
lower est. upper
0.1096949 0.2689783 0.6595509
> anova(model)
numDF denDF F-value p-value
(Intercept) 1 7 9.702400 0.0170
adh31 1 2 0.015683 0.9118
awpm 1 2 2.824076 0.2349
apsm 1 2 1.520431 0.3428
orien 1 2 6.167557 0.1310
>
>
> model2<-lme(awsm~adh31+awpm+apsm+orien,random=~1|viney,method="ML")
> model3<-lme(awsm~adh31+awpm+orien,random=~1|viney,method="ML")
> anova(model2,model3)
Model df AIC BIC logLik Test L.Ratio p-value
model2 1 7 42.44324 46.91664 -14.22162
model3 2 6 41.47847 45.31281 -14.73924 1 vs 2 1.035230 0.3089
>
> model3.1<-lme(awsm~adh31+awpm+orien,random=~1|viney)
> summary(model3.1)
Linear mixed-effects model fit by REML
Data: NULL
AIC BIC logLik
47.01767 48.83318 -17.50883
Random effects:
Formula: ~1 | viney
(Intercept) Residual
StdDev: 1.592549 0.2471161
Fixed effects: awsm ~ adh31 + awpm + orien
Value Std.Error DF t-value p-value
(Intercept) 0.5357316 0.7333251 7 0.7305512 0.4888
adh31 0.4829425 0.1911191 3 2.5269194 0.0857
awpm 0.4850814 0.1996481 3 2.4296822 0.0934
oriensouth -0.5961750 0.2031294 3 -2.9349512 0.0608
Correlation:
(Intr) adh31 awpm
adh31 -0.609
awpm -0.360 0.345
oriensouth 0.379 -0.712 -0.225
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.10623050 -0.20081291 -0.09441451 0.19507694 1.08369449
Number of Observations: 14
Number of Groups: 8
>
> model4<-lme(awsm~adh31+orien,random=~1|viney,method="ML")
> anova(model3,model4)
Model df AIC BIC logLik Test L.Ratio p-value
model3 1 6 41.47847 45.31281 -14.73924
model4 2 5 46.13353 49.32881 -18.06676 1 vs 2 6.655056 0.0099
>
>
> model5<-lme(awsm~awpm+orien,random=~1|viney,method="ML")
> anova(model3,model5)
Model df AIC BIC logLik Test L.Ratio p-value
model3 1 6 41.47847 45.31281 -14.73924
model5 2 5 46.13124 49.32653 -18.06562 1 vs 2 6.652772 0.0099
>
> model6<-lme(awsm~awpm+orien,random=~1|viney,method="ML")
> anova(model3,model6)
Model df AIC BIC logLik Test L.Ratio p-value
model3 1 6 41.47847 45.31281 -14.73924
model6 2 5 46.13124 49.32653 -18.06562 1 vs 2 6.652772 0.0099
>
# actual data used for analyses
> awsm<-log(wsmmax/days+1)
> apsm<-log(psm/days+1)
> awpm<-log(wpm/days+1)
> adh31<-log(dh31/days+1)
>
> awsm
[1] 0.52224518 3.29454964 0.01695951 1.36088200 2.01692487 4.57307785
[7] 0.41499043 2.66783465 1.02173903 2.66030752 0.83589370 1.22387225
[13] 4.93707366 2.25271004
> apsm
[1] 1.9938465 1.8572201 0.2595992 1.3926976 0.0000000 0.5222452 2.1845666
[8] 3.0942586 3.8885649 2.6691373 0.0000000 0.0000000 1.9460277 4.2546503
> awpm
[1] 0.9333715 1.9485709 0.0000000 0.1381489 1.5627542 0.0000000 0.4149904
[8] 0.0000000 0.7482365 0.5215986 0.5113811 1.4076002 1.0598621 0.1732711
> adh31
[1] 0.8329868 1.4813520 2.5733515 2.8888284 1.4217520 2.1184476 2.5843313
[8] 2.9896871 3.0351911 2.4386017 2.4736569 2.2904899 2.7930367 3.3185963
> orien
[1] int int int int int int int int south south south south
[13] south south
Levels: int south
> viney
[1] lpsm06 lwsm06 mpsm06 mwsm06 lpsm07 lwsm07 mpsm07 mwsm07 lpsm06 lwsm06
[11] mwsm06 lpsm07 lwsm07 mwsm07
Levels: lpsm06 lpsm07 lwsm06 lwsm07 mpsm06 mpsm07 mwsm06 mwsm07
--
Menelaos Stavrinides
Ph.D. Candidate
Environmental Science, Policy and Management
137 Mulford Hall MC #3114
University of California
Berkeley, CA 94720-3114 USA
Tel: 510 717 5249
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