Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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. >>>>>> * >>>>>> * 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: >> * 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/

**Follow-Ups**:**Re: st: Median and CI with predict***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: Median and CI with predict***From:*Carla Guerriero <guerriero.carla@gmail.com>

**Re: st: Median and CI with predict***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Median and CI with predict***From:*Carla Guerriero <guerriero.carla@gmail.com>

**Re: st: Median and CI with predict***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Median and CI with predict***From:*Carla Guerriero <guerriero.carla@gmail.com>

**Re: st: Median and CI with predict***From:*Carla Guerriero <guerriero.carla@gmail.com>

**Re: st: Median and CI with predict***From:*Nick Cox <njcoxstata@gmail.com>

- Prev by Date:
**st: Regression with clustered robust standard errors** - Next by Date:
**st: error calculating powers** - Previous by thread:
**Re: st: Median and CI with predict** - Next by thread:
**Re: st: Median and CI with predict** - Index(es):