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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   Tue, 11 Feb 2014 11:45:27 +0100

Sorry I will try to be more clear:
In my previous question I was asking about obtaining confidence
interval for my dependent variable (willigness to pay) wich in my case
is bounded between 0 and 1. After trying different models (zero
inflated beta and beta) I fund that glm with logit  link function and
binomial family works better (test with AIC and BIC) . I previously
asked how to get the confidence intervals from constant only model and
I was (wrongly) using the predict command.
You suggested to run the regression and then to obtain the proportion
to use "meta: invlogit (constant, lower an upper confidece interval
values)" ..
In order to obtain the confidence interval for my dependent variable
you suggested to use the command: ci . I used "ci depvar,  jeffreys
binomial" (I also tried Wilson"  but the command show a blank results
(results with no number).. and I dont understand why.
My further question was: is there a test I can perform in stata to
test if the results from two different regressions are the same?
for  example I have the willigness to pay for 1 in 100 risk reduciton
is equal to 0.21 and the willigness to pay for 19 in 100 is 0.50 I
want to test they are statisitcally different I can I do ?
Hope this makes sense ..
Kind Regards
Carla

On Tue, Feb 11, 2014 at 11:24 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> 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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