[R-sig-ME] ICC for quasipoisson?
Mike Lawrence
Mike.Lawrence at dal.ca
Tue Sep 20 00:11:12 CEST 2011
I'm not sure if this will be of any help or not, but I just read this
paper myself and generated this code for computing the repeatability
point estimate of the intercept for the Gaussian case:
fit = lmer(
formula = DV ~ ( 1 | ID )
, data = my_data
)
#obtain the residual and effect variance
vc = VarCorr(fit)
residual_var = attr(vc,'sc')^2
intercept_var = attr(vc$ID,'stddev')[1]^2
#compute the raw repeatability
R = intercept_var/(intercept_var+residual_var)
#prepare to compute the repeatability extrapolated to the experiment's size
n = as.data.frame(table(my_data$ID))
k = nrow(n)
N = sum(n$Freq)
n0 = (N-(sum(n$Freq^2)/N))/(k-1)
#compute the repeatability given the experiment's size
Rn = R/(R+(1-R)/n0)
print(Rn)
In the binomial case (where you have raw 1s/0s in the DV and do the
fit with family=binomial) you do the same as above with the exception
that residual_var isn't estimated from the data but fixed at (pi^2)/3
As noted over at http://stats.stackexchange.com/questions/15768 I'm
still trying to figure out how to compute the confidence/credible
intervals...
Cheers,
Mike
--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University
Looking to arrange a meeting? Check my public calendar:
http://goo.gl/BYH99
~ Certainty is folly... I think. ~
On Fri, Sep 16, 2011 at 7:30 AM, Dr C B Stride
<c.b.stride at sheffield.ac.uk> wrote:
> Thanks Elizabeth - have downloaded rptR, got it up and running and tested on
> a few simple examples - however, in my example (I should have mentioned!) I
> am modelling a rate rather than a raw count hence I have an offset term -
> does anyone know if/how that can be incorporated into the rptR code?
> Couldn't see anything in Nakagawa & Schielzeth, and the rpt function only
> has arguments for DV, grouping var and link fn)
>
> Likewise my data is cross-classified i.e I have multiple random effects
> hence multiple grouping vars...
>
>
>
> Elizabeth Oliva said the following on 13/09/2011 15:18:
>>
>> 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 at sheffield.ac.uk
>> <mailto:c.b.stride at 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 at gmail.com
>> <mailto:elizabeth.oliva at 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.
>>
>> _________________________________________________
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>>
>>
>>
>> -- 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 <http://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
>>
>>
>
>
> --
> 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
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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