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Re: st: Assumptions for continuous predictor in negative binomial regression model
From
David Hoaglin <[email protected]>
To
[email protected]
Subject
Re: st: Assumptions for continuous predictor in negative binomial regression model
Date
Fri, 28 Mar 2014 08:56:26 -0400
Isabel,
Your question arises for all types of regression models.
The relation to the continuous variable does not need to be linear,
but the relation to the functional form of the continuous variable
that you use as a predictor (or in more than one predictor) does need
to be linear (or close to linear). ("Linear" in "linear regression"
refers to the requirement that the model be linear in the coefficients
of the predictors.)
For example, if the basic continuous variable is x, the outcome (e.g.,
log odds in logistic regression) might be linear in log(x). Other
common functional forms include linear splines and cubic splines.
Choosing a suitable functional form for a continuous variable is a
customary step in building a model. Ways to go about it vary with the
type of model and the amount of data.
David Hoaglin
On Fri, Mar 28, 2014 at 5:47 AM, <[email protected]> wrote:
> Dear all,
>
> I could find quite a lot of assumptions concerning a negative binomial regression model in general, but what I couldn't find was if there is assumptions about including a continuous variable as a predictor? e.g in logistic regression, it requires that the independent continuous variable is linearly related to the log odds of the outcome.
>
> 1. Is this the same for the negative binomial regression? Or are there any other assumptions concerning the inclusion of a continuous predictor variable?
>
> 2. I tried to graphically show this linear relationship of the independent continuous variable and the log odds of the outcome for logistic regression, but I didn't get a satisfying result! Any advice on that? I'm sure there must be a pretty easy way in stata, but I couldn't figure it out!
>
> Thank you for your help!
>
> Isabel
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