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Re: st: Median and CI with predict
From
Nick Cox <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: Median and CI with predict
Date
Fri, 7 Feb 2014 15:56:22 +0000
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.ats.ucla.edu/stat/stata/
> *
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