Dear list,

Thierry, great, thank you very much for your quick reply! I will drop 
moment as a random slope, and read up on the different hypotheses that 
are being tested.

I have one more question. Basically, I have no background in multilevel 
(as you may have guessed :-)). The reason I'm 'in over my head' like 
this, is because I basically want to 'use the proper analysis' for my 
data, and the only method is apparently mixed models. "All I want" is 
the simplest' statistically decent, way to test whether cannabis use at 
the second measurement moment is different in the group that received 
that intervention as compared to the group that didn't.

However, when I try to learn about mixed models, the sources I encounter 
approach the modelling practice very differently. They seem to be about 
much more advanced issues; whether random intercepts and slopes should 
be included, and for which variables, etc (to stick to those issues that 
I at least kind of understand). Apparently, either mixed models are only 
used by people who are statistically much more advanced (i.e. there's a 
gap between 'mainstream researchers' and the people who understand and 
use mixed models), or in fact these sources _do_ discuss the same 
things, but in mixed models the terminology just differs a lot from what 
you encounter in more basic statistical textbooks.

I basically have the idea that although my requirements are very basic, 
I have to learn lots of dark arcane issues to be able to do this 
properly. This is kind of 'scary', as, for example, matrix algebra is, 
well, scary :-)

What do people here think of this? Is mixed models just something you 
should avoid unless you're able & willing to really delve into its 
statistical innards?

Again, thank you very much, kind regards,

Gjalt-Jorn


On 06-11-2012 17:25, ONKELINX, Thierry wrote:
> Dear Gjalt-Jorn,
>
> Your null model is too complex for your data. Having only one measurement per participant per moment, you cannot fit a random 'slope' along moment per participant. Note the perfect correlation in your null model for the nested random effect.
>
> Even at the school levels, the amount of data is not that larger and you end up with near perfect correlations in this random effect. So I would advise to drop moment as a random slope.
>
> Don't forget that the summary of a model is testing different hypotheses than an LRT between two models! You might do some reading on that topic or get some local statistical advise.
>
> Best regards,
>
> Thierry
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> + 32 2 525 02 51
> + 32 54 43 61 85
> Thierry.Onkelinx@inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
>
> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces@r-project.org [mailto:r-sig-mixed-models-bounces@r-project.org] Namens Gjalt-Jorn Peters
> Verzonden: dinsdag 6 november 2012 16:23
> Aan: r-sig-mixed-models@r-project.org
> Onderwerp: [R-sig-ME] lmer: effects of forcing fixed intercepts and slopes
>
> Dear all,
>
> I run into something I don't understand: I update a model with some terms; none of the terms is significant; but the model suddenly fits A LOT better . . .
>
> The background: I am running a model to test a relatively simple
> hypothesis: that an intervention aiming to reduce cannabis use is effective. It's a repeated measures design where we measured cannabis use of each student before and after the intervention. In addition to having repeated measures, students are nested in schools. A simple plot of the percentage of cannabis users before and after the intervention, in the control and the intervention groups, is at http://sciencerep.org/files/7/plot.png (this plot ignores the schools).
>
> This is the datafile:
>
> <R CODE>
>     ### Load data
>     dat.long <-
> read.table("http://sciencerep.org/files/7/the%20cannabis%20show%20-%20data%20in%20long%20format.tsv",
> header=TRUE, sep = "\t");
>
>     ### Set 'participant' as factor
>     dat.long$participant <- factor(dat.long$id);
>
>     head(dat.long);
> </R CODE>
>
> This is what the head looks like:
>
>     id moment   school cannabisShow gender age usedCannabis_bi participant
> 1  1 before Zuidoost Intervention      2  NA NA           1
> 2  2 before Zuidoost Intervention      2  NA 0           2
> 3  3 before Zuidoost Intervention      1  NA 1           3
> 4  4 before    Noord Intervention     NA  NA NA           4
> 5  5 before    Noord Intervention     NA  NA 1           5
> 6  6 before    Noord Intervention      1  NA NA           6
>
> 'school' has 8 levels;
> 'moment' has 2 levels ('before' and 'after'); 'cannabisShow' has 2 levels, 'Intervention' and 'Control'; 'usedCannabis_bi' has 2 levels, 0 and 1; and participants is the participant identifyer.
>
> I run a null model and a 'real' model, comparing the fit. These are the formulations I use:
>
> <R CODE>
>     rep_measures.1.null  <- lmer(formula = usedCannabis_bi ~
>                                  1 + moment + (1 + moment | school / participant),
>                                  family=binomial(link = "logit"), data=dat.long);
>     rep_measures.1.model <- update(rep_measures.1.null, .~. + moment*cannabisShow);
>     rep_measures.1.null;
>     rep_measures.1.model;
>     anova(rep_measures.1.null, rep_measures.1.model); </R CODE>
>
> The second model, where I introduce the interaction between measurement moment and whether participants received the intervention (this should reflect an effect of the intervention), fits considerably better than the original model. But, the interaction is not significant. In fact, none of the fixed effects is - so I added terms to the model, none of these terms significantly contributes to the prediction of cannabis use, yet the model fits a lot better.
>
> This seems to be a paradox. Could anybody maybe explain how this is possible?
>
> I also looked at the situation where I impose fixed intercepts and slopes on the participant level (so intercepts and slopes could only vary per school):
>
> <R CODE>
>     rep_measures.2.null  <- lmer(formula = usedCannabis_bi ~
>                                  1 + moment + (1 + moment | school),
>                                  family=binomial(link = "logit"), data=dat.long);
>     rep_measures.2.model <- update(rep_measures.2.null, .~. + moment*cannabisShow);
>     rep_measures.2.null;
>     rep_measures.2.model;
>     anova(rep_measures.2.null, rep_measures.2.model); </R CODE>
>
> Now the interaction between 'measurement moment' and 'intervention' is significant, as I expected; but the improvement in fit between the null model and the 'full model' is much, much smaller.
>
> This is very counter-intuitive to me - I have the feeling I'm missing something basic, but I have no idea what. Any help is much appreciated!
>
> Thank you very much in advance, kind regards,
>
> Gjalt-Jorn
>
>
> PS: the file with the analyses is at
> http://sciencerep.org/files/7/the%20cannabis%20show%20-%20analyses%20for%20mailing%20list.r
>
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