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--- | ||
layout: post | ||
title: "Heteroskedasticity in bird point counts" | ||
description: "" | ||
category: [Consulting] | ||
tags: [Poisson regression,Poisson] | ||
--- | ||
{% include JB/setup %} | ||
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## Problem description | ||
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### Data | ||
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Bird (Dabbling Ducks) count data on 30 wetlands for 2 years for about 10 weeks. | ||
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### Modeling | ||
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Starting with a mixed effect Poisson regression model. | ||
Then moved to | ||
a mixed effect linear model using square root of the count as the response | ||
and week as a weight (due to heteroskedasticity by week). | ||
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#### Fixed effects | ||
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- Week (numeric or categorical) | ||
- Emergent | ||
- Year | ||
- Area | ||
- Age of wetland | ||
- WC ? | ||
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#### Random effects | ||
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- site | ||
- site x year | ||
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### Results | ||
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Heteroskedasticity appears to exist with more variability in early weeks | ||
compared to later. | ||
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## Advice | ||
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Think about | ||
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- At least for model building, use linear model with square root of count | ||
- Treat week as categorical | ||
- Consider repeated measures structure on random effects | ||
- Try the analysis in SAS so that you have easier control of random effects | ||
and error variances |