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Re: st: Median and CI with predict


From   Carla Guerriero <guerriero.carla@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Median and CI with predict
Date   Fri, 7 Feb 2014 17:00:19 +0100

Thank you so much Nick that's great!!!
Kind  Regards
Carla Guerriero

On Fri, Feb 7, 2014 at 4:56 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> I'd apply -ci- directly; indeed you have a choice of ways to do it.
>
> But as for -glm-, my answer is the same answer as before:
>
> 1. -glm- gives you confidence intervals in its main output. The only
> indirectness is that you need to invert the link.
>
> 2. -predict- is not needed.
>
> Examples:
>
> . sysuse auto
> (1978 Automobile Data)
>
> . glm foreign, link(logit)
>
> Iteration 0:   log likelihood = -53.942063
> Iteration 1:   log likelihood = -47.679133
> Iteration 2:   log likelihood = -47.065235
> Iteration 3:   log likelihood = -47.065223
> Iteration 4:   log likelihood = -47.065223
>
> Generalized linear models                          No. of obs      =        74
> Optimization     : ML                              Residual df     =        73
>                                                    Scale parameter =  .2117734
> Deviance         =  15.45945946                    (1/df) Deviance =  .2117734
> Pearson          =  15.45945946                    (1/df) Pearson  =  .2117734
>
> Variance function: V(u) = 1                        [Gaussian]
> Link function    : g(u) = ln(u/(1-u))              [Logit]
>
>                                                    AIC             =   1.29906
> Log likelihood   = -47.06522292                    BIC             = -298.7373
>
> ------------------------------------------------------------------------------
>              |                 OIM
>      foreign |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>        _cons |  -.8602013   .2560692    -3.36   0.001    -1.362088   -.3583149
> ------------------------------------------------------------------------------
>
> . mata: invlogit((-.8602013, -1.362088, -.3583149))
>                  1             2             3
>     +-------------------------------------------+
>   1 |    .29729729   .2039011571   .4113675423  |
>     +-------------------------------------------+
>
> . ci foreign, jeffreys binomial
>
>                                                          ----- Jeffreys -----
>     Variable |        Obs        Mean    Std. Err.       [95% Conf. Interval]
> -------------+---------------------------------------------------------------
>      foreign |         74    .2972973    .0531331        .2024107    .4076909
>
> . ci foreign, wilson binomial
>
>                                                          ------ Wilson ------
>     Variable |        Obs        Mean    Std. Err.       [95% Conf. Interval]
> -------------+---------------------------------------------------------------
>      foreign |         74    .2972973    .0531331        .2052722    .4093291
>
>
> Nick
> njcoxstata@gmail.com
>
>
> On 7 February 2014 15:45, Carla Guerriero <guerriero.carla@gmail.com> wrote:
>> Hi Nick my dependent variable is a proportion (of the budget that
>> given a budget constraint individuals are willing to give up)
>> so I used  logit link function to ensure linearity and binomial family
>> distribution.. For example for 19 in 100 risk reduction I get a
>> coefficent of -.657211*** and If i use predict the mean WTP is 0.20
>> which makes sense .. but the SD is 0 .. I want to get CI for the mean
>> .. maybe boostrapping is an option? I know how to do for DCE where you
>> have a ratio of the coefficent (delta or boostrapping or parametric
>> boostrapping) but I have no clue how to make CI for eman WTP estimate
>> from regression ..
>>
>>
>> On Fri, Feb 7, 2014 at 4:26 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>>> -glm- with no covariates gives you confidence intervals for mean
>>> response, directly or indirectly, depending on the link. No need to
>>> use -predict- at all. I don't think you can get confidence  intervals
>>> for the median that way.
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