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Re: st: Median and CI with predict


From   Nick Cox <njcoxstata@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Median and CI with predict
Date   Tue, 11 Feb 2014 10:24:47 +0000

Sorry, but I don't understand almost any of this.

meta:  ?

ic ?

wtp ? WTP? (I think that means "willingness to pay", but please note
that only some people here are economists)

Note that -ci- is limited to single variables and that its -wilson-
and -jeffreys- options don't travel to other commands.

Whatever you did sounds at some considerable distance from your last
question and my last answer. If someone else can't work out what you
are saying, please read the FAQ advice again and give much more detail
on your problem.
Nick
njcoxstata@gmail.com


On 11 February 2014 10:18, Carla Guerriero <guerriero.carla@gmail.com> wrote:
> Hi Nick
> I used your coding meta:... and the proportion come out ..
> I eventually apply  the ic command to my wtp dependent variable and it
> runs without error  but the output is blank ..with both the approaches
> ..(Wilson and Jeffreys)
> also another quesiton I need to test that the WTP values for different
> health risk redcution are the same or they statistically different ..
> usually I do the test command on coefficient but in this case I need
> to compare the values the come from different regression with
> intercpet only model .. there is a way to do that in stata ?
> Kind Regards
> Carla
>
> On Fri, Feb 7, 2014 at 5:00 PM, Carla Guerriero
> <guerriero.carla@gmail.com> wrote:
>> 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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