[R-sig-ME] Query

[Doran, Harold]

In the fixed portion of the model, you would use the typical R conventions of ':' or '*'. So, as an example, x1:x2 on the RHS gives only the interaction between two variables and then the use of x1 * x2 gives both the interaction and main effects.



From: EZEQUIEL ROSSI [mailto:EZEQUIEL_455 at hotmail.com]
Sent: Thursday, September 14, 2017 8:04 AM
To: Doran, Harold <HDoran at air.org>; Jake Westfall <jake.a.westfall at gmail.com>
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Query


Thank you so much for your response. One question more.



I need estimate the interaction genotype x environment. How should I make this? The interaction between Checks and environment or between Testlines and environments?

Best regards,

Ezequiel Rossi
________________________________
De: Doran, Harold <HDoran at air.org<mailto:HDoran at air.org>>
Enviado: miércoles, 13 de septiembre de 2017 04:20 p.m.
Para: 'EZEQUIEL ROSSI'; Jake Westfall
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Asunto: RE: [R-sig-ME] Query

I can think of a few ways to do this; but perhaps a straightforward way is to estimate environment in a slightly different way than what you have below

lmer(Alt_Planta ~ 1 + (1|Environment) + (1|Bloque) + Checks + (1|Testlines), data = data)

This will give you the marginal variance between environments and then you can get the conditional variance of the conditional mean for each environment.


From: EZEQUIEL ROSSI [mailto:EZEQUIEL_455 at hotmail.com]
Sent: Wednesday, September 13, 2017 3:05 PM
To: Jake Westfall <jake.a.westfall at gmail.com<mailto:jake.a.westfall at gmail.com>>; Doran, Harold <HDoran at air.org<mailto:HDoran at air.org>>
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Query

My model is the follow:

lmer(Alt_Planta ~ Environment + (1|Bloque) + Checks + (1|Testlines), data = data)

where I have two environment and in each environment the design was a randomized complete block design with 3 replications for the Checks (30 genotypes) and the Testlines had one replications.

I need estimate the variance components for each environment and across environment. If you can help me with this, I will thank him very much.

Thank you very much,

Best regards,

Ezequiel Rossi

________________________________________
De: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org<mailto:r-sig-mixed-models-bounces at r-project.org>> en nombre de Jake Westfall <jake.a.westfall at gmail.com<mailto:jake.a.westfall at gmail.com>>
Enviado: miércoles, 13 de septiembre de 2017 03:46 p.m.
Para: Doran, Harold
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Asunto: Re: [R-sig-ME] Query

Well in the old ANOVA-based mixed model framework we talk about
interactions between fixed and random factors, although in modern mixed
models we call those interactions "random slopes." (The coefficient for a
fixed predictor X "depends on" the level of the random factor.) So Ezequiel
could just be using the ANOVA-type terminology (used a lot in DoE) to refer
to random slopes.

Jake

On Wed, Sep 13, 2017 at 1:41 PM, Doran, Harold <HDoran at air.org<mailto:HDoran at air.org>> wrote:

> Perhaps a bit OT, but what *is* an interaction between a fixed and random
> factor? The fixed effects are estimates, BLUPs are not estimates really.
>
> I can't quite consider what the estimand is in this instance
>
> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org]
> On Behalf Of Ben Bolker
> Sent: Wednesday, September 13, 2017 2:34 PM
> To: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] Query
>
>
>    I'm going to say something here to get the conversation started, but
> this information comes with a giant caveat.  I hope that someone with more
> knowledge of experimental designs comes forward ...
>
>   in general an interaction between a random factor r and a fixed factor f
> is either
>
> (1|r:f)
>
> (assuming a positive, compound-symmetric variance-covariance matrix) or
>
> (f|r)
>
> (assuming an unstructured variance-covariance matrix).  The latter is
> likely to be very expensive if f has more than a few levels.
>
>   Interaction between two random factors would be (1|r1:r2) (you would
> have (1|r1) and (1|r2) in the model as well).
>
> On 17-09-13 02:12 PM, EZEQUIEL ROSSI wrote:
> > Dears,
> >
> >
> > I am working with a Federer's augmented block design in lmer function
> > and I need indicate interaction between ramdom and fixed factors and
> > between two ramdom factors . Can you say me how I should make this?
> >
> >
> > Thank you very much,
> >
> >
> > Best regards,
> >
> >
> > Ezequiel Rossi
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> >
>
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