You are more likely to get a helpful (or any) response on mixed models
issues by posting to the r-sig-mixed-models list, not here.

-- Bert

On Thu, Dec 6, 2012 at 11:12 AM, Ben Gillespie <nebs...@hotmail.com> wrote:

> Hi guys,
> I'm very new to R and have been teaching myself over the past few months -
> it's a great tool and I'm hoping to use it to analyse my PhD data.As I'm a
> bit of a newb, I'd really appreciate any feedback and/or guidance with
> regards to the following questions that relate to generalized linearmixed
> modelling (or, at least, I think they do!)(if there is a 'better', more
> appropriate way that I could attempt to answer my questions, please let me
> know).
> I've spent a lot of time researching this approach on the internet, but
> can'tseem to find any directly applicable examples.
> Thanks in advance, and, if you need any further information, please let me
> know.
> # My experiment:# I have 1 site on 3 different rivers (independent)(sites
> 1,2 and 3). # I visit each site 2 times (time 1 and 2). # On each visit, I
> take 5x replicate insect samples and calculate total abundance for each
> replicate.# Site 1 is in an area called "yellow" and sites 2 and 3 are in
> an area called "blue".
> # My data frame:
>
>
> data=data.frame(site=c(rep(1,10),rep(2,10),rep(3,10)),replicate=c(rep(1:5,6)),time=c(rep(1,5),rep(2,5),rep(1,5),rep(2,5),rep(1,5),rep(2,5)),abundance=c(1,2,1,2,1,2,1,2,1,2,30,32,30,32,30,32,30,32,30,32,30,31,33,32,31,31,33,32,31,32),sitetype=c(rep("yellow",10),rep("blue",20)))
>
> data$site=factor(data$site)data$replicate=factor(data$replicate)data$time=factor(data$time)
> data
>
> # Initial remarks: # As each replicate (1-5) was taken from within each
> site (1-3) on both sampling times (1-2),# I figure that 'replicate' should
> be treated as nested within 'site' and that both should be treated as
> random factors?
> # First question: Is there is difference in abundance between sites?#
> Second question: Is there is difference in abundance between sitetypes
> (blue or yellow)?
>         #If my 'initial remarks' statement is correct (please tell me if
> not), then I think a generalized linear mixed model is appropriate and
> would be something along these lines:
> # Fitting the model:
>                 require(lme4)
> glmm1=glmer(abundance~time+sitetype+(1|site/replicate),family="poisson",data=data)
>      #I chose to use poisson as abundance is count data... not sure if
> that's a good reason...                               summary(glmm1)
>  #Output:
> ################################################################Generalized
> linear mixed model fit by the Laplace approximation Formula: abundance ~
> time + sitetype + (1 | site/replicate)    Data: data    AIC   BIC logLik
> deviance 12.31 19.31 -1.153    2.306Random effects: Groups         Name
>    Variance Std.Dev. replicate:site (Intercept)  0        0       site
>       (Intercept)  0        0      Number of obs: 30, groups:
> replicate:site, 15; site, 3
> Fixed effects:               Estimate Std. Error z value Pr(>|z|)
>  (Intercept)     3.43579    0.05641   60.91   <2e-16 ***time2
> 0.01560    0.07900    0.20    0.843    sitetypeyellow -3.03815    0.26127
>  -11.63   <2e-16 ***---Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 
> ‘*’ 0.05 ‘.’
> 0.1 ‘ ’ 1
> Correlation of Fixed Effects:            (Intr) time2 time2       -0.706
>     sitetypyllw -0.108
>  0.000################################################################
> # Inferences:
>                 #I'm unsure how to assess the variance and std dev scores
> for site... some guidance here would be appreciated....i.e. how do I answer
> my original question: Is there is difference in abundance between sites?
>        #There is no statistically significant difference between the two
> time periods (P=>0.05)                #Using the above output, the model
> suggests that there is a statistically significant difference between site
> types (p<0.05)
> # Further questions:
>                 #1 Are the above inferences correct?            #2 I have
> read about overdispersion.... how would I test for this in this example?
>            #3 How could I build an interaction term into the model and
> answer the following: "Is there a statistically significant site*time
> interaction?"         #4 Finally, are there any obvious steps or things I
> should be doing in order to get a 'robust' or 'correct' answer from this
> problem? i.e. further tests... alternative models and comparisons...
>
>         [[alternative HTML version deleted]]
>
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>


-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm

        [[alternative HTML version deleted]]

______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Reply via email to