I haven't tried it yet (because I have to download the most recent version
of R); however, it's possible to use the link in the paper to download the
program to calculate the ICC. I think the website for the program has sample
code. However, I cc'd the author of the program just in case he might have
some sample code.



On Tue, Sep 13, 2011 at 3:26 AM, Dr C B Stride
<c.b.stride@sheffield.ac.uk>wrote:

> Hi
>
> Was wondering if anyone knows of a snippet of R code that will perform the
> ICC calculations recommended in this paper?
>
> cheers
> Chris
>
> Jarrod Hadfield said the following on 09/09/2011 16:51:
>
>> Hi,
>>
>> You can find the relevant equations in Table 2 of:
>>
>> Repeatability for Gaussian and non-Gaussian data: a practical guide for
>> biologists. Biological reviews. 2010. Nakagawa1 & Schielzeth
>>
>> Cheers,
>>
>> Jarrod
>>
>> Quoting Elizabeth Oliva <elizabeth.oliva@gmail.com> on Fri, 9 Sep 2011
>> 08:34:50 -0700:
>>
>>  Hi all,
>>>
>>> I’ve been searching at length for a way to figure out how to calculate
>>> the
>>> ICC for a mixed effects quasipoisson model in R from the output below
>>> (i.e.,
>>> intercept only model) and can't figure out the correct equation to use.
>>> For
>>> example, I've run mixed effects logistic regression models for which I
>>> used
>>> the following equation:
>>>
>>>               c<-a*a
>>>
>>>               icc<-c/(3.289868+c)
>>>
>>> [a being the standard deviation of the intercept]
>>>
>>>
>>>
>>> I'm not sure what the corollary type of equation is for quasipoisson
>>> mixed
>>> models.
>>>
>>>
>>>
>>> If you had any suggestions I would greatly appreciate it.
>>>
>>>
>>>
>>> Best,
>>>
>>> Elizabeth
>>>
>>>
>>>
>>>
>>>
>>> womentxengintonly <- glmmPQL(NewSpecOut_SUM ~ 1, random = ~1 |NEPEC3N,
>>> family =quasipoisson, data = women)
>>>
>>>
>>>
>>>
>>>
>>> summary(womentxengintonly)
>>>
>>>
>>>
>>> Linear mixed-effects model fit by maximum likelihood
>>>
>>> Data: women
>>>
>>>  AIC BIC logLik
>>>
>>>   NA  NA     NA
>>>
>>>
>>>
>>> Random effects:
>>>
>>> Formula: ~1 | NEPEC3N
>>>
>>>        (Intercept) Residual
>>>
>>> StdDev:   0.4679934  7.26936
>>>
>>>
>>>
>>> Variance function:
>>>
>>> Structure: fixed weights
>>>
>>> Formula: ~invwt
>>>
>>> Fixed effects: NewSpecOut_SUM ~ 1
>>>
>>>               Value  Std.Error   DF  t-value p-value
>>>
>>> (Intercept) 3.046308 0.04805773 4960 63.38852       0
>>>
>>>
>>>
>>> Standardized Within-Group Residuals:
>>>
>>>       Min         Q1        Med         Q3        Max
>>>
>>> -1.0333625 -0.5507037 -0.3717204  0.2138915 12.4856954
>>>
>>>
>>>
>>> Number of Observations: 5099
>>>
>>> Number of Groups: 139
>>>
>>>    [[alternative HTML version deleted]]
>>>
>>>
>>>
>>
>>
>> --The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>> ______________________________**_________________
>> R-sig-mixed-models@r-project.**org <R-sig-mixed-models@r-project.org>mailing list
>> https://stat.ethz.ch/mailman/**listinfo/r-sig-mixed-models<https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
>>
>
>
> --
> Dr Chris Stride, C. Stat, Statistician, Institute of Work Psychology,
> University of Sheffield
> Telephone: 0114 2223262
> Fax: 0114 2727206
>
> “Figure It Out”
> Statistical Consultancy and Training Service for Social Scientists
>
> Visit www.figureitout.org.uk for details of my consultancy services, and
> forthcoming training courses, which are also available on an in-house basis:
> - Data Management using SPSS syntax
> - Multiple Regression using SPSS
> - Multilevel Modelling using SPSS
> - Structural Equation Modelling using MPlus
>

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