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Re: st: Assumptions for continuous predictor in negative binomial regression model
From
Nick Cox <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: Assumptions for continuous predictor in negative binomial regression model
Date
Fri, 28 Mar 2014 09:59:03 +0000
With logit and a single continuous predictor, you can use -predict- to
get the predicted probabilities and -logit()- to take their logits,
and then plot those to get a straight line. The observed binary data
are, naturally not on the same scale, but you can satisfy yourself
that the line is straight.
sysuse auto, clear
logit foreign weight
predict pr
line pr weight, sort || scatter foreign weight, jitter(2)
gen logit_pr = logit(pr)
line logit_pr weight, sort || scatter foreign weight, jitter(2)
Nick
[email protected]
On 28 March 2014 09:47, <[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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