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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
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 <[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.
>>> *
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>>> * http://www.stata.com/help.cgi?search
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>>> * http://www.ats.ucla.edu/stat/stata/
>> *
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>> * http://www.ats.ucla.edu/stat/stata/
> *
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*
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