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Re: st: Median and CI with predict
From
Carla Guerriero <[email protected]>
To
[email protected]
Subject
Re: st: Median and CI with predict
Date
Tue, 11 Feb 2014 11:18:56 +0100
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
<[email protected]> wrote:
> Thank you so much Nick that's great!!!
> Kind Regards
> Carla Guerriero
>
> On Fri, Feb 7, 2014 at 4:56 PM, Nick Cox <[email protected]> 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
>> [email protected]
>>
>>
>> On 7 February 2014 15:45, Carla Guerriero <[email protected]> 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 <[email protected]> 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.
>>>> *
>>>> * For searches and help try:
>>>> * http://www.stata.com/help.cgi?search
>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/
>>>> * http://www.ats.ucla.edu/stat/stata/
>>> *
>>> * For searches and help try:
>>> * http://www.stata.com/help.cgi?search
>>> * http://www.stata.com/support/faqs/resources/statalist-faq/
>>> * http://www.ats.ucla.edu/stat/stata/
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/faqs/resources/statalist-faq/
>> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
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