RE: st: statistical significance of cut points in ordered logit

[Grace Jessie <gracejessie@hotmail.com>]

Additionally, does the statistical significance of cut points in ordered logit matter? 
I found there are no z or P>|z| for cut points, though I could get it.


----------------------------------------
> Date: Mon, 17 May 2010 17:40:40 +0530
> Subject: Re: st: statistical significance of cut points in ordered logit
> From: tirthankar.chakravarty@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> Grace,
>
> That is not how it works. There is an error term involved in these
> probability calculations as well. Specifically, for the observation
> where xb=16.55703,
> Pr(y=1|x) = Pr(xb+u <= cut1) = Pr(u <= -.1289)
> which, together with the assumed logistic distribution for the "u"s
> implies Pr(y=1|x) = invlogit(-.1289) = .46781954. Similarly,
> Pr(y=2|x) = Pr(cut1 <= xb+u <= cut2) = Pr(-.1289 <= u <= 1.56567) =
> invlogit(1.56567)-invlogit(-.1289) = .35934591
> and so on. If you were to write off the error term i.e., base
> inference on the expected probabilities (xb), you'd have certainties
> and not probabilities.
>
> T
>
> 2010/5/17 Martin Weiss :
>>
>> <>
>>
>> " I got the material http://www.nd.edu/~rwilliam/xsoc63993/l91.pdf provided
>> by Maarten, which is helpful."
>>
>>
>>
>> Just to be sure, the material you are referring to is provided by Richard
>> Williams, Maarten probably pointed you to it.
>>
>> HTH
>> Martin
>>
>>
>> -----Ursprüngliche Nachricht-----
>> Von: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Grace Jessie
>> Gesendet: Montag, 17. Mai 2010 13:47
>> An: statalist@hsphsun2.harvard.edu
>> Betreff: Re: st: statistical significance of cut points in ordered logit
>>
>>
>> Statalists,
>> I got the material http://www.nd.edu/~rwilliam/xsoc63993/l91.pdf provided by
>> Maarten, which is helpful. Thank you!
>> After typing the following in the Stata, I found some obersavtions were
>> suprising to me(see table A). In table A, For example,xb[1] is obviously
>> bigger than the coefficient of cut1,so the value for Y should equal 2.
>> However, from the values for pr1 pr2 pr3, the value for pr1 is the biggest,
>> which means the most likely outcome for Y is 1. Why not consistent? The
>> doubt with other observations in table A is the same.
>> Additionally, what does the statistical significance of cut points in
>> ordered logit mean, which has not been answered in the posting before? I
>> found there are no z or P>|z| for cut points, though I could get it.
>>
>> use http://www.nd.edu/~rwilliam/stats2/statafiles/shuttle2.dta, clear
>> ologit distress date temp, nolog
>> Ordered logistic regression Number of obs =
>> 23
>> LR chi2(2) =
>> 12.32
>> Prob> chi2 =
>> 0.0021
>> Log likelihood = -18.79706 Pseudo R2 =
>> 0.2468
>> ----------------------------------------------------------------------------
>> --
>> distress | Coef. Std. Err. z P>|z| [95% Conf.
>> Interval]
>> -------------+--------------------------------------------------------------
>> --
>> date | .003286 .0012662 2.60 0.009 .0008043
>> .0057677
>> temp | -.1733752 .0834473 -2.08 0.038 -.336929
>> -.0098215
>> -------------+--------------------------------------------------------------
>> --
>> /cut1 | 16.42813 9.554813 -2.29896
>> 35.15522
>> /cut2 | 18.12227 9.722293 -.9330729
>> 37.17761
>> ----------------------------------------------------------------------------
>> --
>> predict xb,xb
>> predict pr1 pr2 pr3
>>
>> table A
>> +--------------------------------------------------------------------+
>> | distress date temp xb pr1 pr2 pr3 |
>> |--------------------------------------------------------------------|
>> | None 8732 70 16.55703 .4678189 .359285 .1728961 |
>> | 1 or 2 9341 81 16.65107 .4444934 .3687458 .1867608 |
>> | 3 plus 9434 75 17.99692 .1723883 .3589076 .468704 |
>> | 1 or 2 9461 76 17.91227 .1848028 .3675054 .4476918 |
>> +--------------------------------------------------------------------+
>> Hope for any help!
>>
>> Regards,
>> Grace
>>
>>
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>
>
>
> --
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> recursive class signs r, such that neither v Gen r nor Neg(v Gen r)
> belongs to Flg(κ) (where v is the free variable of r).
>
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