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

[Guillaume Adeux]

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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