[R-sig-ME] Fwd: GLM-normal distribution
Ben Bolker
bbolker at gmail.com
Tue Apr 4 23:47:19 CEST 2017
This isn't really a mixed model question: it would be more appropriate
for a generic stats or stats-ecology forum (e.g.
r-sig-ecology at r-project.org, or CrossValidated
[http://stats.stackexchange.com]
A couple of quick points:
- you don't need lme4 at all since you don't have a random effect in
your model
- a rule of thumb is that you shouldn't try to fit more than 1 model
parameter per 10-15 data points, so this model (4 parameters for 19 data
points) is pushing it a bit
- you should not assess normality based on the *marginal* distribution;
instead you should look at the residuals from the model (e.g. see
plot(M2) below)
- if you weight the linear model by number of species (as is probably
appropriate) you get a p-value of 0.052
- your data are slightly underdispersed (less variance than expected
from binomial); if you account for this by using family=quasibinomial
you get almost identical results to the linear model.
Overall I would say you have *weak* evidence at best for an effect of
anchom.
M1 <- glm(cbind(exot, nativ) ~ anchom + tipdecamp + exph500,
data =mis.datos1, family =binomial)# the syntax of my model
summary(M1)
M2 <- lm(exot/(nativ+exot) ~ anchom + tipdecamp + exph500,
data =mis.datos1, weight=nativ+exot)
summary(M2)
plot(M2)
library(ggplot2); theme_set(theme_bw())
library(dplyr)
library(tidyr)
d2 <- mis.datos %>%
mutate(tot=exot+nativ,
prop_exot=exot/tot) %>%
select(prop_exot,tot,anchom,tipdecamp,exph500) %>%
gather(var,value,-c(prop_exot,tot,tipdecamp))
ggplot(d2 ,aes(value,prop_exot,colour=tipdecamp))+
geom_point(aes(size=tot))+facet_wrap(~var,scale="free_x")+
geom_smooth(method="glm",aes(weight=tot),
method.args=list(family=binomial))
deviance(M1)/df.residual(M1)
M3 <- update(M1, family =quasibinomial)
## scale parameters
d3 <- mis.datos %>%
mutate(anchom=scale(anchom),
exph500=scale(exph500))
M4 <- update(M3, data=d3)
library(dotwhisker)
dwplot(list(M4))+geom_vline(xintercept=0,lty=2)
On 17-04-04 05:15 PM, Marcos Monasterolo wrote:
> Dear all. I am doing an analysis on proportion data resulting from counts.
> As I do have the count data available I am running a glm with binomial
> distribution. However, after realizing the response variable is normal
> (Anderson-Darling test did not reject normality of the calculated
> proportions) I am now having second thoughts as to whether it might also be
> possible to run a normal lm with proportion as the response variable. The
> thing is one of the explanatory variables ("ancho", which I am really
> interested in) is not significant in the binomial glm but significant in
> the lm. My understanding is that I should stick with the binomial GLM, but
> I wanted to have an expert opinion on this.
> I provide a working code below. Thanks in advance for your help.
> Marcos
>
>
> id <- "0B6X3EoqLHXG-dnZqTXpWSkRPYkE" # google file ID
> mis.datos <- read.table(sprintf("https://docs.google.com/uc?id=%s&
> export=download", id), header = TRUE,sep=";",dec=",")
> mis.datos1<-mis.datos[-c(3,6,7,8),] #these data points I don't need
> library(nortest)
> ad.test(mis.datos1$propexot)#evaluate normality
> hist(mis.datos1$propexot)
> library(lme4)
> M1 <- glm(cbind(exot, nativ) ~ anchom + tipdecamp + exph500, data =
> mis.datos1, family =binomial)# the syntax of my model
> summary(M1)
>
> ----
> Biól. Marcos Monasterolo
> Becario doctoral - Cátedra de Botánica General, Facultad de Agronomía, UBA
>
> [[alternative HTML version deleted]]
>
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