[R-sig-ME] strip plot design with pseudoreplication

[Javier Moreira]

Hi, thanks for your help.
I was reading my question, and find
I have tried that way, but still I'm getting 2 errors. I should edit the
question because later i found the correct way on lmer

m2_lmer<-lmer(REND.~AMBIENTE*TRATAMIENTO+

(1|BLOQUE)+(1|BLOQUE:AMBIENTE)+(1|BLOQUE:TRATAMIENTO)+(1|BLOQUE:AMBIENTE:TRATAMIENTO),
       data=data1)

I see your answer is the right response for my question. The error was on
the question.
Actually what i need is the translation of this model to lme. I have
another random term:  (1|BLOQUE:AMBIENTE:TRATAMIENTO) that makes the model
to account for every error term needed.

again, thanks for your help.

regards

El mar, 5 abr 2022 a las 23:19, Phillip Alday (<me using phillipalday.com>)
escribió:

> I am not commenting on whether the model makes sense, but you can use
> the same nesting syntax in lme4 that you used in lme:
>
> m2_lmer<-lmer(REND.~AMBIENTE*TRATAMIENTO+
>          (1|BLOQUE/AMBIENTE/parcela),
>         data=data1)
>
>
> On 22/3/22 8:06 am, Javier Moreira wrote:
> > hi, members of the group.
> >
> > I'm trying to find the way to express a model within R.
> >
> > My intention is to use a set of data from a wheat trial, harvested with
> GPS
> > mapping. I'm comparing the standard model that use the average of the
> plot
> > (experimental unit) vs. one that use every pseudo-replication
> (observation
> > unit-harvest point).
> >
> > The design is a strip plot or split block, with 2 factors and 3 error
> > terms. For the first step, I use the Agricolae package.
> >
> > model0_fijo = with(data21,strip.plot(BLOCK = BLOQUE,
> >                    COL = AMBIENTE,
> >                    ROW = TRATAMIENTO,
> >                    Y = RTO))
> > *data21, n=72
> > and this produces an anova with 3 error terms (Ea,Eb,Ec) and the correct
> > distribution of degrees of freedom (Ambiente, Ea/ Tratamiento,Eb/
> > interacción, Ec).
> >
> > When I try to use the pseudo-replication, i first try to use lme(), and
> to
> > take into account the correct grouping, it produces 2 different Error
> > terms, and i get (Ambiente, Ea, and tratamamiento and interaction, Eb).
> > This makes the design as it was a split plot (2 error terms).
> >
> > modelo2_MM<-lme(REND.~1+TRATAMIENTO*AMBIENTE,
> >                 random=~1|BLOQUE/AMBIENTE/TRATAMIENTO,
> >                 data=data1)
> > *data1, n=3095
> >
> > According to a tutorial
> > <
> https://vsni.co.uk/case-studies/dealing-with-pseudo-replication-in-linear-mixed-models
> >for
> > the use of ASreml package i was able to use lmer() to account for the
> > block:plot interaction (1|BLOQUE:parcela) and get the same result as the
> > tutorial. But, this leaves the model with only 1 error term applied to
> > every factor and interaction. So, it takes into account the
> > pseudo-replication but not the correct error assign for the anova.
> >
> > m2_lmer<-lmer(REND.~AMBIENTE*TRATAMIENTO+
> >         (1|BLOQUE)+(1|BLOQUE:AMBIENTE)+(1|BLOQUE:parcela),
> >        data=data1)
> >
> > In the initial steps, to find a way with lmer() to get a 3 error model
> that
> > accounts for pseudo-replication would be great.
> > Following that, i also have to account for spatial correlation, and that
> > isn't an option within lmer() so, i have to get the same model translate
> to
> > lme().
> >
> > Thanks a lot for your help. This data analysis is for a on farm trial
> from
> > my Msc thesis.
> >
> > best regards,
> >
> > Ing. Agr. Javier Moreira
> >
>


-- 
Javier Moreira de Souza
Ingeniero Agrónomo
099 406 006

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