[R-sig-ME] (no subject)

[ONKELINX, Thierry]

Dear Emily,

It sounds like you could use some reading on this topic. I would recommend Zuur et al (2009), Bolker (2008) and Pinheiro & Bates (2000).

1. No. The assumption of normality is only for the **residuals** of a **linear** (mixed) model. So neither the fixed effects nor the response has to be normally distributed. Note that the residuals of a **generalized** linear (mixed) model don't have the assumption of normality.
2. Yes, these are assumed to be normally distributed. Small deviations from normality are not problematic. Strong deviations usually indicate that you are missing an important covariate. Maybe the assumption of i.i.d. is not valid due to the spatial structure of the plots (spatial autocorrelation).
3. Plot the residuals against available covariates. There shouldn't be a pattern in the residuals.
4. The deviance of a single model is not that informative. It is mainly used to compare models.

Note that DBH+Elevation+(DBH*Elevation) can be written as DBH * Elevation. Andaddingspacestocodemakesitmuchmorereadable.

Best regards,

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 [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Emily L. De Stigter
Verzonden: donderdag 11 december 2014 8:38
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] (no subject)

Hello,

I'm just out of undergrad and working in an ecology lab as the leading statistical investigator for a project studying conifers in Northern California. I'm using lme4 to do a GLMM. My response variable is binary:
status of the trees (live vs dead). I also have two fixed effects: diameter at breast height (DBH) and elevation, plus the interaction of the two. My random effect is plot.

Here are my questions:

   1.

   Do the fixed effects (DBH and elevation) need to be normally
   distributed? Both DBH and elevation are not normally distributed and the
   basic transformations I've tried did not correct the issue.
   2.

   The random effect I have (plot) is not normally distributed as I know
   that it needs to be. I have tried a couple different transformations (log,
   sqrt...) but, again, nothing the corrected the issue. What should I try
   next to fix it?
   3.

   How should I best check that my model is fitting the data appropriately?
   4.

   How do I interpret the deviance of the model?

Here's some of my R code:

PSME stands for Psuedotsuga menziesii, one of the species of interest.

stat<-read.csv("/Users/emilydestigter/Documents/Sawyer/status.csv")

head(stat)

dim(stat)

psme<-glmer(Status~DBH+Elevation+(DBH*Elevation)+(1|Plot),data=stat,family=
"binomial")

summary(psme)

Thanks for reading and let me know if anyone has any follow-up questions. I appreciate greatly any and all advice I get. I realize these questions are not exactly related to this forum, so I would also be glad for some suggestions on resources to check out.

Thanks again,

Emily

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