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

[Rabin KC]

Thank you for your suggestion, Guillaume.

However, the model you suggest is overparameterized in my case. I don't
have enough observations to fit the maximal model.

Also regarding blocks as fixed, I would like to make block inferences in a
broad sense. Is there a particular reason that 4 levels are not enough to
be modeled as a random effect? Or is it due to the risk of getting
imprecise treatment estimates as mentioned in this topic below?

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q2/003712.html

I look forward to hearing from you

Rabin

On Tue, Sep 10, 2019 at 1:51 AM Guillaume Adeux <guillaumesimon.a2 using gmail.com>
wrote:

> I agree with Peter Claussen.I am a weed ecologist and often deal with
> these experimental set-ups.
>
> The correct full intercept model ought to be:
> lmer(biomass~block+
> crop*tillage*cover+(1|block:crop)+(1|block:crop:tillage)+(1|block:crop:tillage:CC))
>
> Note that block is treated as fixed because 4 levels are not sufficient to
> estimate a random effect.
>
> Also note that (1|block:crop:tillage:CC) should only be introduced in the
> model if there is pseudoreplication at the elementary plot level, as is
> often done.
>
> For appropriate test of effects, look into the {monet} or {afex} package.
>
> For appropriate post-hoc multiple comparisons, look into the {emmeans}
> package.
>
> Cheers,
>
> GA2
>
> Le lun. 9 sept. 2019 à 20:36, Rabin KC <kc000001 using umn.edu> a écrit :
>
>> 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
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>

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