Overdispersion in point DS
Sven Buchmann
Good morning,
I’m a master student currently working with data from a marmot point sampling survey. I’m following a forward selection procedure with a set of covariates. I would like to hear your opinion on different ways to perform model selection when there is overdispersion in the data.
The alpine marmot occurs in family groups, meaning one should use “family” as sampling unit. In the field, however, it is not possible to distinguish the families. Therefore, only individuals that were closer than 20m were recorded as a group with corresponding group size.
In a first analysis I naively treated every individual as independent, i.e., I duplicated rows with n animal in the group n times. As described in Howe et al. 2018 the AIC selected for an overcomplex model. When analysing the data with clusters, AIC still tends to select complex models, indicating that there is still some overdispersion present (as expected by the field method), but not as pronounced as in the naïve approach (i.e., there are several competing models of different complexities and with overdispersion in mind one can argue for a less complex model). I performed four model selection approaches to deal with the overdispersion.
- No clusters + model selection with AIC: model gets way to complex
- No clusters + QAIC: Results in a model with ~Temperature (3 levels).
- Clusters + AIC: Model with ~Temperature + Weather (3 levels). However, it is followed by a more complex model (dAIC = 0.86), indicating that there is still some remaining overdispersion.
- With clusters + QAIC: selects null model. Visual inspection and field experience show that some covariates have an influence, so the penalty might be too rigorous.
I would appreciate to hear your expertise on this issue, since I see positive and negative points in all model selection approaches, and I don’t have arguments to prefer one of these.
Sincerely,
Sven
Eric Rexstad
Sent: 18 March 2022 10:26
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Subject: {Suspected Spam} [distance-sampling] Overdispersion in point DS
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Sven Buchmann
Dear Eric,
thank you for your answer!
My density estimates show the same behaviour, for all models the confidence intervals contain the density estimates. Would you agree that analysing the data with clusters is preferable over an individual based analysis, even though it does not capture real family sizes? I would argue that this still eliminates some of the overdispersion. The model selection could then be done with AIC and your suggestion on constraining the number of parameters.
All the best,
Sven
Gesendet: Freitag, 18. März 2022 13:16
An: Sven Buchmann <sven.b...@uzh.ch>; distance...@googlegroups.com <distance...@googlegroups.com>
Betreff: Re: {Suspected Spam} [distance-sampling] Overdispersion in point DS
Eric Rexstad
Sent: 18 March 2022 14:14
To: Eric Rexstad <Eric.R...@st-andrews.ac.uk>; distance...@googlegroups.com <distance...@googlegroups.com>
Subject: AW: {Suspected Spam} [distance-sampling] Overdispersion in point DS
Sven Buchmann
I'm sorry, there is a misunderstanding. The survey was a traditional one without camera traps. But still I suspect overdispersion in the data, because in the field we not able to determine to which family group an individual belonged and because AIC tends to select models with too many parameters if it is allowed to.
Gesendet: Freitag, 18. März 2022 15:31
Eric Rexstad
To: Eric Rexstad <Eric.R...@st-andrews.ac.uk>; distance-sampling <distance...@googlegroups.com>
Subject: {Suspected Spam} AW: {Suspected Spam} [distance-sampling] Overdispersion in point DS
Stephen Buckland
I agree with Eric – I would analyse individuals. There is some discussion of the issue on pp75-76 and p127 of the 2001 distance sampling book.
Steve Buckland
From: distance...@googlegroups.com <distance...@googlegroups.com> On Behalf Of Sven Buchmann
Sent: 18 March 2022 14:41
To: Eric Rexstad <Eric.R...@st-andrews.ac.uk>; distance-sampling <distance...@googlegroups.com>
To view this discussion on the web visit https://groups.google.com/d/msgid/distance-sampling/GV0P278MB01621E9F84BAAD64E1DE82E188139%40GV0P278MB0162.CHEP278.PROD.OUTLOOK.COM.