[S] Re: Interpreting Logistic regression - CORRECTION to Wald Statistic

Renaud Lancelot (lancelot@telecomplus.sn)
Fri, 30 Oct 1998 18:29:50 +0000


I was on the field this week and could not read S-news. Many thanks to
Siftikhar Sikder for noticing my error about the t-value in logistic
regression results. I apologize if this caused any trouble to anybody. I
do agree that one must square the t-value before performing the Wald
test, and that Wald test sometimes gives strange results. However, it
might be a useful statistics in many cases. In my practical experience,
disagreement between Wald test and likelihood ratio test is an
indication whether results are unstable.

Best regards,

Renaud

andrew_white@hmsa.com wrote:
>
> Please note one error in the comments that Renaud Lancelot provided in
> [snews subject "Re: [S] Logistic regression problems in Standard Version",
> from: siftikhar sikder, date: october 18, 1998].
>
> In interpreting the significance of the individual coefficients using the
> t-values supplied in the summary(glm.object.fit) function, you must treat
> the SQUARE of these t-values as asymptotically Chi-Squared with 1 df, hence
> can test against, e.g., the critical value of 3.84 for 5% confidence level.
>
> Do NOT apply the t-value straight as the Chi-Squared value. The t-value
> reflects a Wald statistic indirectly, and there is S-News prior messages on
> the meaning of Wald statistics, as well as how to be VERY CAUTIOUS about
> Wald statistics in logistic regression (due to the obscure "Hauck-Donner
> effect") when the Beta coefficients are very large. But the t-value straight
> off is an approximation to a z-score, and better treated by SQUARING it to
> approximate a Chi-Square value.

-- 
Renaud Lancelot
ISRA-LNERV
BP 2057 Dakar-Hann
Senegal

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