Hello,
I'm quite new to LMEs and have a question to which I did not find an
answer in the archives or in the P&B chapter 5 (allthough I have
problems undestanding the latter, fully).
Im trying to fit a linear mixed model to my data (DV = numerical, IV =
numerical and factorial).
I have some issues with variance heterogeneity within two of my factors:
1) experimental type, two levels: "lab" and "field")
2) system, two levels: "marine" and "limnic"
When adding both variance structures to my models, my results differ
slightly, depending on whether I use "weights =
varIdent(form=~1|system*exp.type)" or "weights =
varComb(varIdent(form=~1|exp.type), varIdent(form=~1|system))".
Which approach would be the better one? What does the " * " exactly
do? It somehow uses one additional df.
Model df AIC BIC
logLik Test L.Ratio p-value
lmaicresisa 1 14 127.1082 160.2804 -49.55408
lmaicresisb 2 15 128.8289 164.3707
-49.41447 1 vs 2 0.279231 0.5972
And a general question: I've seen (read) many people arguing that one
should not use the varFunc too excessively. With only one varIdent
Factor, my models have indeed much better p-values (all though I
understand that those are less relevant in LME) but my AIC and LogLik
increase. Should I use the more parsimonious model with both variance
factors (as indicated by AIC) ? Are the low p-values with 2
var-functions an indication of a bad model? QQ-Plots indicate a
slightly better model fit with 2 var-functions.
It is difficult to add a nice example here, since my data are
relatively complex (meta-analysis). If it is necessary, I can try to
create a comparable dataset, allthough not sure how to.
This is my model:
lmresisc =
lme(resis.log~evenness+exp.type+exp.type:evenness+org.type.merged+org.type.merged:evenness+system+log(exact.duration)+system2,
random =~1|authors.year,
data =
data[(!is.na(data$evenness)&!is.na(data$resis.log)),], weights =
varIdent(form=~1|system*exp.type), method = "ML")
summary(lmresisc)
lmaicresisc = stepAIC(lmresisc)
summary(lmaicresisc)
This is the last step of the stepAIC:
Step: AIC=128.83
resis.log ~ evenness + exp.type + org.type.merged + system +
log(exact.duration) + evenness:exp.type + evenness:org.type.merged
Df AIC
<none> 128.83
- evenness:exp.type 1 129.38
- system 1 132.48
- evenness:org.type.merged 2 133.55
- log(exact.duration) 1 134.20
and this is the summary of the final model
Value Std.Error DF t-value p-value
(Intercept) -1.963651 0.6959103 62 -2.8217014 0.0064
evenness -2.231725 1.4724964 62 -1.5156064 0.1347
exp.typelab -0.487326 0.9387122 7 -0.5191437 0.6197
org.type.mergedheterotroph -2.245062 1.3380510
7 -1.6778601 0.1373
org.type.mergedmixed -2.494429 1.1662591
7 -2.1388289 0.0698
systemmarine 0.505430 0.2204285 7 2.2929430 0.0556
log(exact.duration) 0.406731 0.1536198 62 2.6476456 0.0103
evenness:exp.typelab 2.295373 1.4621236
62 1.5698901 0.1215
evenness:org.type.mergedheterotroph 3.686948 1.6697807
62 2.2080430 0.0309
evenness:org.type.mergedmixed 5.930363 2.0642291
62 2.8729191 0.0056
I hope I gave enough information, please forgive me, if not. This is
my first question here, so i'm not sure about that.
Thanks in advance!
Friso