Dear All
I am trying to do a repeated measures analysis using lmer and have a number
of issues. I have non-orthogonal, unbalanced data. Count data was obtained
over 10 months for three treatments, which were arranged into 6 blocks.
Treatment is not nested in Block but crossed, as I originally designed an
orthogonal, balanced experiment but subsequently lost a treatment from 2
blocks. My fixed effects are treatment and Month, and my random effects are
Block which was repeated sampled. My model is:
Model<-lmer(Count~Treatment*Month+(Month|Block),data=dataset,family=poisson(link=sqrt))
Is this the only way in which I can specify my random effects? I.e. can I
specify them as: (1|Block)+(1|Month)?
When I run this model, I do not get any residuals in the error term or
estimated scale parameters and so do not know how to check if I have
overdispersion. Below is the output I obtained.
Generalized linear mixed model fit by the Laplace approximation
Formula: Count ~ Treatment * Month + (Month | Block)
Data: dataset
AIC BIC logLik deviance
310.9 338.5 -146.4 292.9
Random effects:
Groups Name Variance Std.Dev. Corr
Block (Intercept) 0.06882396 0.262343
Month 0.00011693 0.010813 1.000
Number of obs: 160, groups: Block, 6
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.624030 0.175827 9.237 < 2e-16 ***
Treatment2.Radiata 0.150957 0.207435 0.728 0.466777
Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009
Month -0.079955 0.022903 -3.491 0.000481 ***
Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717
Treatment3.Aldabra:Month 0.077697 0.033340 2.330 0.019781 *
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr) Trt2.R Trt3.A Month T2.R:M
Trtmnt2.Rdt -0.533
Trtmnt3.Ald -0.533 0.450
Month -0.572 0.585 0.585
Trtmnt2.R:M 0.474 -0.882 -0.402 -0.661
Trtmnt3.A:M 0.474 -0.402 -0.882 -0.661 0.454
Any advice on how to account for overdispersion would be much appreciated.
Many thanks in advance
Christine
----------------------
Christine Griffiths
School of Biological Sciences
University of Bristol
Woodland Road
Bristol BS8 1UG
Tel: 0117 9287593
Fax 0117 925 7374
Christine.Griffiths at bristol.ac.uk
http://www.bio.bris.ac.uk/research/mammal/tortoises.html
Dear Christine,
The poisson family does not allow for overdispersion (nor
underdispersion). Try using the quasipoisson family instead.
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Christine Griffiths
Verzonden: maandag 18 mei 2009 13:26
Aan: r-help at r-project.org
Onderwerp: [R] Overdispersion using repeated measures lmer
Dear All
I am trying to do a repeated measures analysis using lmer and have a
number of issues. I have non-orthogonal, unbalanced data. Count data
was obtained over 10 months for three treatments, which were arranged
into 6 blocks.
Treatment is not nested in Block but crossed, as I originally designed
an orthogonal, balanced experiment but subsequently lost a treatment
from 2 blocks. My fixed effects are treatment and Month, and my random
effects are Block which was repeated sampled. My model is:
Model<-lmer(Count~Treatment*Month+(Month|Block),data=dataset,family=pois
son(link=sqrt))
Is this the only way in which I can specify my random effects? I.e. can
I specify them as: (1|Block)+(1|Month)?
When I run this model, I do not get any residuals in the error term or
estimated scale parameters and so do not know how to check if I have
overdispersion. Below is the output I obtained.
Generalized linear mixed model fit by the Laplace approximation
Formula: Count ~ Treatment * Month + (Month | Block)
Data: dataset
AIC BIC logLik deviance
310.9 338.5 -146.4 292.9
Random effects:
Groups Name Variance Std.Dev. Corr
Block (Intercept) 0.06882396 0.262343
Month 0.00011693 0.010813 1.000
Number of obs: 160, groups: Block, 6
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.624030 0.175827 9.237 < 2e-16 ***
Treatment2.Radiata 0.150957 0.207435 0.728 0.466777
Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009
Month -0.079955 0.022903 -3.491 0.000481 ***
Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717
Treatment3.Aldabra:Month 0.077697 0.033340 2.330 0.019781 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Trt2.R Trt3.A Month T2.R:M Trtmnt2.Rdt -0.533
Trtmnt3.Ald -0.533 0.450
Month -0.572 0.585 0.585
Trtmnt2.R:M 0.474 -0.882 -0.402 -0.661
Trtmnt3.A:M 0.474 -0.402 -0.882 -0.661 0.454
Any advice on how to account for overdispersion would be much
appreciated.
Many thanks in advance
Christine
----------------------
Christine Griffiths
School of Biological Sciences
University of Bristol
Woodland Road
Bristol BS8 1UG
Tel: 0117 9287593
Fax 0117 925 7374
Christine.Griffiths at bristol.ac.uk
http://www.bio.bris.ac.uk/research/mammal/tortoises.html
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Thanks. I did try using quasipoisson and a negative binomial error but am unsure of the degree of overdispersion and whether it is simply due to missing values. I am investigating to see if I can replace these missing values so that I can have a balanced orthogonal design and use lme or aov instead which is easier to interpret. Any ideas on whether it is feasible to replace missing values for a small dataset with repeated measures? I have 6 blocks with 3 treatments sampled over 10 months. Two blocks are missing one treatment, albeit a different one. Also any suggestions about how I would go about this would be much appreciated. I am also unsure of whether my random effects (Month|Block) for repeated measures with random slope and intercept is correct and whether (1|Month) + (1|Block) represents repeated measures. Any confirmation would be great. Cheers Christine Christine Griffiths-2 wrote:> > Dear All > > I am trying to do a repeated measures analysis using lmer and have a > number > of issues. I have non-orthogonal, unbalanced data. Count data was > obtained > over 10 months for three treatments, which were arranged into 6 blocks. > Treatment is not nested in Block but crossed, as I originally designed an > orthogonal, balanced experiment but subsequently lost a treatment from 2 > blocks. My fixed effects are treatment and Month, and my random effects > are > Block which was repeated sampled. My model is: > > Model<-lmer(Count~Treatment*Month+(Month|Block),data=dataset,family=poisson(link=sqrt)) > > Is this the only way in which I can specify my random effects? I.e. can I > specify them as: (1|Block)+(1|Month)? > > When I run this model, I do not get any residuals in the error term or > estimated scale parameters and so do not know how to check if I have > overdispersion. Below is the output I obtained. > > Generalized linear mixed model fit by the Laplace approximation > Formula: Count ~ Treatment * Month + (Month | Block) > Data: dataset > AIC BIC logLik deviance > 310.9 338.5 -146.4 292.9 > Random effects: > Groups Name Variance Std.Dev. Corr > Block (Intercept) 0.06882396 0.262343 > Month 0.00011693 0.010813 1.000 > Number of obs: 160, groups: Block, 6 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) 1.624030 0.175827 9.237 < 2e-16 *** > Treatment2.Radiata 0.150957 0.207435 0.728 0.466777 > Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009 > Month -0.079955 0.022903 -3.491 0.000481 *** > Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717 > Treatment3.Aldabra:Month 0.077697 0.033340 2.330 0.019781 * > --- > Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 > > Correlation of Fixed Effects: > (Intr) Trt2.R Trt3.A Month T2.R:M > Trtmnt2.Rdt -0.533 > Trtmnt3.Ald -0.533 0.450 > Month -0.572 0.585 0.585 > Trtmnt2.R:M 0.474 -0.882 -0.402 -0.661 > Trtmnt3.A:M 0.474 -0.402 -0.882 -0.661 0.454 > > > Any advice on how to account for overdispersion would be much appreciated. > > Many thanks in advance > Christine > > ---------------------- > Christine Griffiths > School of Biological Sciences > University of Bristol > Woodland Road > Bristol BS8 1UG > Tel: 0117 9287593 > Fax 0117 925 7374 > Christine.Griffiths at bristol.ac.uk > http://www.bio.bris.ac.uk/research/mammal/tortoises.html > > ______________________________________________ > R-help at 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. > >-- View this message in context: http://www.nabble.com/Overdispersion-using-repeated-measures-lmer-tp23595955p23612349.html Sent from the R help mailing list archive at Nabble.com.