[R-sig-ME] Understanding and analysing split split design using lmer()

[Rabin KC]

Dear Thierry,

Thank you for your reply.

The link to the file is here
<https://drive.google.com/open?id=1sQ0O6FgT5eOQKlhc_ejzfR_wJbRvLzrY>. You
advised naming the subplots from 1 to 36. But with 4 replicates, 2 crops
and 3 tillage, should not the number of subplots equal to 24? Am I missing
something?
Even if I name the subplots 1 to 36, how should I name the remaining 36
plots (total 72 plots)?

Sorry, my analysis skills are at beginner's level, but I would appreciate
the clarification.

Thank you,
Rabin

On Mon, Sep 9, 2019 at 2:13 PM Thierry Onkelinx <thierry.onkelinx using inbo.be>
wrote:

> Dear Rabin,
>
> The mail list has stripped your csv file. Send small files as txt. Or put
> the data somewhere online and send the link.
>
> You want to use a random intercept to model the effect of an experimental
> unit which is not captured by your fixed effects. E.g. for each combination
> of replicate, crop and tillage you have 3 types of cover. So you want a
> "tillage" level random intercept to model this. You can do this by
> assigning a unique id to each combination of replicate (4), crop (2) and
> tillage (3) = 36 subplot. So number your subplots for 1 to 36. With such
> unique ids you don't have to worry about (partially) nested or (partially)
> crossed effects. See
> https://www.muscardinus.be/2017/07/lme4-random-effects/
>
> You can do the same thing at the crop level: unique combinations of
> replicate (4) and crop (2) = 8 combinations. The problem is that for a
> descend estimate of the random effect variance, you need a lot of levels (>
> 200) see https://www.muscardinus.be/2018/09/number-random-effect-levels/.
> If you only want to use random intercept to correct for dependencies within
> the data, then you can get a way with a smaller number (>10). In your case,
> you have too few replicates to take the replicate and replicate/crop plot
> effect into account. So I recommend that you only take the subplots
> (replicate/crop/tillage) into account. Note that this effect will take up
> any effect at a higher level (replicate/crop) when you don't model that
> effect.
>
> I'd go for lmer(biomass~crop*tillage*cover+ (1|subplot),data=data)
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> 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
>
> ///////////////////////////////////////////////////////////////////////////////////////////
>
>
> <https://www.inbo.be>
>
>
> Op ma 9 sep. 2019 om 20:36 schreef Rabin KC <kc000001 using umn.edu>:
>
>> Hello R mixed models community,
>>
>> I am trying to fit a model for a split- split-plot design using lmer. I
>> know how to do this with aov and ssp.plot function in agricolae, but I
>> need
>> to use lmer in this case.
>>
>> I am interested in the response variable biomass of cover crops. I have
>> the
>> main plots as crops (corn and soybean). The subplot is tillage, which is
>> randomized within the crops and has 3 levels (conventional-till, no-till,
>> and strip-till). Within tillage(sub plot), 3 cover crop strategies are
>> randomly assigned (AR, ARCC, ARCCFR). Therefore 1 rep (block) has 18
>> treatments. The whole experiment is replicated 4 times, therefore, total
>> experimental units equal to 72 units.
>>
>> Data arranged in a csv file is attached in this email.
>>
>> Now the analysis part:
>>
>> I know how to do this using aov/ ssp.plot( agricolae). The code is as
>> follows:
>>
>> model<-with(data,ssp.plot(rep,crop,tillage,cover,biomass)).
>> Same thing with aov:
>>
>>
>> model1<aov(cover_coverage~rep+crop*tillage*cover+Error(rep/crop/tillage),data=data)
>>
>> For my  analysis using lmer, and lme,  I have the following code:
>>
>> model2<-lmer(biomass~crop*tillage*cover+ (1|rep),data=data)
>>
>> model3<-lme(fixed=biomass~crop*tillage*cover, data=data, random=~1|rep)
>>
>> Now my confusion and questions are:
>> 1. I have used crop, tillage, and cover as fixed effects. And only the rep
>> (block) as random. Is the random effect properly assigned for this design?
>>
>> 2. I have read a lot about crossed and nested design. Crawley and Oehlert
>> give particular examples about it, but I have trouble understanding if
>> this
>> design has nested factors or crossed factors.
>>
>> I would be very relieved to receive feedback with the matter in hand.
>>
>> Looking forward, and thank  you  a lot,
>>
>> Sincerely,
>> Rabin
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>

	[[alternative HTML version deleted]]