1
$\begingroup$

I have data on damages on flowers from different treatments. The damage was originally count data (number of damages per flower) but the person collecting the data categorized the data in four levels - no damage (0) to a high damage level (3).

I know I could use logistic regression to model the odds but I wonder if it is possible to use poisson regression instead and consider the observed level of damage as a count variable?

If possible, would the intepretation of the dichotomized treatment coefficients be the difference in the logs of expected counts of damage levels for treatment compared to baseline while holding the other variables constant in the model?

The weird intepretation makes me think this is not at all possible.

Improve this question
$\endgroup$
11
  • $\begingroup$ I don't see how you can consider it a count. It won't have the properties of a count. Do you know how the recoding was done? $\endgroup$
    – Glen_b
    Commented Aug 9, 2014 at 7:43
  • $\begingroup$ No you are absolutely correct, the variable is clearly bounded! Thank you. $\endgroup$
    – Notquitesure
    Commented Aug 9, 2014 at 11:09
  • $\begingroup$ Further, it won't have the variance = mean property that you see with a Poisson, for example. $\endgroup$
    – Glen_b
    Commented Aug 9, 2014 at 11:34
  • $\begingroup$ Altough I guess that could be solved with negbin or quasipoisson? $\endgroup$
    – Notquitesure
    Commented Aug 9, 2014 at 23:49
  • $\begingroup$ Yes -- presuming we're happy about treating ordinal as interval then at least in some cases those should provide reasonable approximations - suitable for estimation of the mean, at least (but not necessarily suitable models for getting a prediction interval from, because of issues like the boundedness). $\endgroup$
    – Glen_b
    Commented Aug 10, 2014 at 0:02

1 Answer 1

Reset to default
3
$\begingroup$

A Poisson random variable can take any integer value from 0 to $\infty$. If you fit a Poisson regression model to your data, you may get fitted values other than 0, 1, 2, 3. For this reason, I would not use a Poisson regression model.

Improve this answer
$\endgroup$
6
  • $\begingroup$ This is exactly what I thought. The idea was originally suggested to me from another fellow, I have also read Agresti (2013) and I am certain that it is not the correct method. I should have read it before asking the question! Thank you very much for taking the time to write your answer, the variable is clearly bounded. $\endgroup$
    – Notquitesure
    Commented Aug 9, 2014 at 11:09
  • $\begingroup$ What model would you recommend? The the response is clustered in blocks and I have one nominal predictor. $\endgroup$
    – Notquitesure
    Commented Aug 11, 2014 at 7:45
  • $\begingroup$ With only one predictor variable, I would do a simple multinomial regression. You can do this with the glm function in R with setting the family option equal to "multinomial". $\endgroup$
    – TrynnaDoStat
    Commented Aug 11, 2014 at 13:16
  • $\begingroup$ Even with the clustering? $\endgroup$
    – Notquitesure
    Commented Aug 11, 2014 at 13:40
  • $\begingroup$ If I'm understanding correctly, you don't know the clustering rule. So you are limited to modelling the categories. $\endgroup$
    – TrynnaDoStat
    Commented Aug 11, 2014 at 13:58

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.