[R-sig-ME] Mixed Effects Model

Fri Jun 7 16:43:35 CEST 2013

Dear Stephen,

I prefer to keep those random effects in the model because they reflect the design of the experiment.

Best regards,

Thierry

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: Stephen Sefick [mailto:ssefick op gmail.com]
Verzonden: donderdag 6 juni 2013 20:52
Aan: ONKELINX, Thierry
CC: Stephen Sefick; r-sig-mixed-models op r-project.org
Onderwerp: Re: [R-sig-ME] Mixed Effects Model

Thierry,

Thank you for the help.  I needed to also include a PLANT:POSITION nesting to account for pseudoreplication within plant.

part of the output reads:

  PLANT_POSITION (Intercept) 3.5074e-16 1.8728e-08
  TP_PLANT       (Intercept) 5.7686e+00 2.4018e+00
  EX_TP          (Intercept) 0.0000e+00 0.0000e+00
  EX             (Intercept) 0.0000e+00 0.0000e+00
  Residual                   1.3626e+01 3.6913e+00

My interpretation of this table is that EX and EX_TP are not important and I can drop them from the model because they do not account for any variation in the response, correct?  Does it effect the estimates to leave these in the model?

many thanks,

Stephen

On Thu 06 Jun 2013 09:12:56 AM CDT, ONKELINX, Thierry wrote:
>
> Dear Stefan,
>
> Your model specification seems to be correct.
>
> Nested random effects are straightforward in lme
>
> lme(HEIGHT~ISO, random = ~ 1|EX/EX_TP/TP_PLANT, data=z)
>
> Crossed random effects are harder to do. I think it can be done with
> the pdBlocked function
>
> Best regards,
>
> Thierry
>
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality
> Assurance Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> + 32 2 525 02 51
> + 32 54 43 61 85
> Thierry.Onkelinx op inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no
> more than asking him to perform a post-mortem examination: he may be
> able to say what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does
> not ensure that a reasonable answer can be extracted from a given body
> of data.
> ~ John Tukey
>
> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces op r-project.org
> [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Stephen
> Sefick
> Verzonden: woensdag 5 juni 2013 23:17
> Aan: r-sig-mixed-models op r-project.org
> Onderwerp: [R-sig-ME] Mixed Effects Model
>
> Hello all:
>
> This is my first foray into mixed effects modelling and I have a
> couple of questions.
>
> I have data that I would like to analyize and I believe that a mixed
> effects model is the proper thing to use:
>
> fixed effects isolate (factor); response plant height
>
> nested factors:
> Time Point (TP) nested in Exp
> Plant nested in TP
>
> non-nested factor:
> Position (leaf position)
>
> I have explicitly nested the TP in Exp by creating an interaction
> factor TP:Exp=EX_TP and explicitly nested TP in PLANT and Exp with the
> factor variable Exp:TP:PLANT=TP_PLANT
>
> I have used the code
>
> lmer(HEIGHT~ISO+(1|EX)+(1|EX_TP)+(1|TP_PLANT)+(1|POSITION), data=z)
>
> to fit this model. I believe this is the correct specification. Is
> this correct?
>
> I would also like to be able to fit this in lme (to be used to create
> a decision tree in package REEM tree). I would like to use the REEM
> tree package to investigate metal concentrations in the data
> predicting isolate incorperating the experimental structure.
>
> Please let me know if any more information is needed to help answer my
> questions. Thank you in advance for all of the help.
> kindest regards,
>
> --
> Stephen Sefick
> **************************************************
> Auburn University
> Biological Sciences
> 331 Funchess Hall
> Auburn, Alabama
> 36849
> **************************************************
> sas0025 op auburn.edu
> http://www.auburn.edu/~sas0025
> **************************************************
>
> Let's not spend our time and resources thinking about things that are
> so little or so large that all they really do for us is puff us up and
> make us feel like gods. We are mammals, and have not exhausted the
> annoying little problems of being mammals.
>
> -K. Mullis
>
> "A big computer, a complex algorithm and a long time does not equal
> science."
>
> -Robert Gentleman
>
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Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document.
The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.