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Re: st: Odd ratio / relative risk in logistic regression
From
Srinivas <[email protected]>
To
[email protected]
Subject
Re: st: Odd ratio / relative risk in logistic regression
Date
Tue, 9 Apr 2013 04:28:57 -0700 (PDT)
Hi
after the variable list insert ,or
rgds
dr.srinivasan
--- On Mon, 4/8/13, Ching Wong <[email protected]> wrote:
> From: Ching Wong <[email protected]>
> Subject: st: Odd ratio / relative risk in logistic regression
> To: [email protected]
> Date: Monday, April 8, 2013, 9:06 PM
> Hi,
>
> My analysis involves two steps:
>
> 1. Chi-square testing:
> I did a few chi-sqare testing with different variables.
> -tab grade var1, chi2
> -tab grade var2, chi2
> -tab grade var 3, ch2 etc.
> Basesd on the result of the chi-sqaure testings, the
> variables which
> are significant (i.e. p<0.05) will then put into the
> logistic
> regression.
>
> 2. logistic regression:
> I put the command as followings:
> - binreg grade var1 var3 var4 etc.
> And I have got the following output.
>
> Iteration 1: deviance = 113.0721
> Iteration 2: deviance = 92.10798
> Iteration 3: deviance = 87.45499
> Iteration 4: deviance = 86.88055
> Iteration 5: deviance = 86.86395
> Iteration 6: deviance = 86.86393
> Iteration 7: deviance = 86.86393
> Generalized linear models
> No.
> of obs =
> 297
> Optimization : MQL Fisher
> scoring
> Residual df =
> 294
>
> (IRLS EIM)
> Scale
> parameter = 1
> Deviance =
> 86.86392755
> (1/df) Deviance = .2954555
> Pearson =
> 311.8670508
> (1/df) Pearson = 1.060772
> Variance function: V(u) = u*(1-u/1)
> [Binomial]
> Link function : g(u) = ln(u/(1-u))
> [Logit]
>
>
>
> BIC
> = -1587.093
> ------------------------------------------------------------------------------
> |
>
> EIM
> grade | Coef. Std.
> Err. z P>|z|
> [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> var1
> | 2.955512 1.066853
> 2.77 0.006
> .8645186 5.046506
> var4| .4058033 1.07797
> 0.38 0.707
> -1.70698 2.518587
> _cons |
> -4.464928 .6125685
> -7.29 0.000
> -5.665541 -3.264316
> ------------------------------------------------------------------------------
>
>
> In this case, I can tell var 1 is significant in the
> logistic
> regression model, since it has a p-value =0.006. However,
> how can I
> find out the odd ratio or the relative risk of this model?
> Did I use
> the wrong command?
>
> Thanks.
>
> Regards,
>
> Wong
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