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

Rabin KC kc000001 @end|ng |rom umn@edu
Tue Sep 10 10:39:45 CEST 2019

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?


I look forward to hearing from you


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

> 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
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

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