[R-sig-eco] Proper treatment of Proportion Response Data with Two Categorical Predictors

[Aitor Gastón]

Following your example, you have 2 positive cases and 8 negative cases, i.e. 
a binary response as you can code the data as 0 (not recovered) and 1 
(recovered).

An example of the GLM approach using simulated data:

set.seed(100)#set random number generator  to get reproducible results
N<-round(runif(51,1,10))#simulate number of available seeds
rp<-runif(51,0,1)#simulate proportion of recovered seeds
r<-round(N*rp)#simulate numer of recovered seeds
u<-factor(sample(c("K","L"),51,replace=T)) #simulate units
f<-factor(sample(c("f1","f2","f3"),51,replace=T)) #simulate fields
mod<-glm(cbind(r,N-r)~u + f, family="binomial") #fit a GLM
anova (mod,test="Chisq") #anova test
summary(mod) #summary of the model with "treatment contrasts"

This is a fixed effects model, but it can be adapted to mixed models using 
the glmer function of the lme4 package. An example available in ?glmer

  ## generalized linear mixed model
     (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                   family = binomial, data = cbpp))

Hope this helps

Aitor


--------------------------------------------------
From: "Everett" <ehanna23 at uwo.ca>
Sent: Tuesday, December 11, 2012 8:46 PM
To: <r-sig-ecology at r-project.org>
Subject: Re: [R-sig-eco] Proper treatment of Proportion Response Data with 
Two Categorical Predictors

> Aitor,
>
> Perhaps I am missing something, but I do not think that my original data 
> can
> take binary form. Each sampling point had a unique number of seeds (0 -
> +infinity). I sampled at each site and collected a proportion of the seeds
> that were available, thus, I would have, for example, 10 seeds available 
> of
> which 2 seeds were collected = 0.200 recovery (or 20% recovery). I do not
> think that logistic (binary) regression applies here but I am relatively
> novice with certain aspects of these topics.
>
> -Everett
>
>
>
> --
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