Hi Jeff and other Stata lister,
Despite of some valuable comments of you, I couldn't solve the problem. At the last reply, Jeff explained about how to get CI with "SVYMEAN" statements. I need to calculate CIs with "SVYPROP" statements, however. Below is the table explaining how I calculate 8 Est. Prop.
**** BEGIN:
svyprop cat1Rr cat2Rr cat3Rr
pweight: wtdper98 Number of obs = 364
Strata: <one> Number of strata = 1
PSU: <observations> Number of PSUs = 364
Population size = 3657115.5
Survey proportions estimation
+---------------------------------------------------------+
| cat1Rr cat2Rr cat3Rr Obs Est. Prop. Std. Err. |
|---------------------------------------------------------|
| 0 0 0 65 0.202852 0.026513 |
| 0 0 1 29 0.078938 0.020018 |
| 0 1 0 13 0.028383 0.009759 |
| 0 1 1 2 0.003007 0.002141 |
| 1 0 0 5 0.005753 0.003897 |
|---------------------------------------------------------|
| 1 0 1 1 0.003676 0.003676 |
| 1 1 0 2 0.002165 0.001566 |
| 1 1 1 1 0.002011 0.002014 |
| 2 2 2 246 0.673215 0.031302 |
+---------------------------------------------------------+
*** END:
My questions are:
1) To get CIs for the 8 Est. Prop. separately, I first need to create a new variable with 8 categories? If so, how?
2) And then, how can I calculate CI of Est. Prop in this case?
Sorry for the repeated question and many thanks for your time and generous help in advance.
Hong
----- Original Message -----
From: [email protected] (Jeff Pitblado, StataCorp LP)
Date: Friday, August 27, 2004 11:01 am
Subject: Re: st: calculrating confidence Intervals in svyprop statements
> Hong <[email protected]> asks about producing confidence intervals for
> categories of some category variables:
>
> > By following Stas' suggestion, I checked the common_options and
> tried it.
> > Unfortunately, svyprop doesn't allow "ci " as a common_option.
> Below is the
> > caution message popped up on the STATA Results window.
> >
> > svyprop cat1Rr cat2Rr cat3Rr, ci
> > option ci not allowed
> > r(198);
> >
> > Is there anyone who has any other suggestion? Thanks for
> considering of it.
> > FYI, the all three combined variables -cat1Rr, catRr, and cat3Rr-
> are binary
> > variables(0,1).
>
> You can get normal based confidence intervals already by using -
> svymean-.
> Here is a simple example:
>
> . sysuse auto, clear
> . svyset, srs
> . svymean foreign
>
> Here are the results from -svymean- above:
>
> ***** BEGIN:
> Survey mean estimation
>
> pweight: <none> Number of obs
> = 74
> Strata: <one> Number of strata
> = 1
> PSU: <observations> Number of PSUs
> = 74
> Population size
> = 74
>
> -------------------------------------------------------------------
> -----------
> Mean | Estimate Std. Err. [95% Conf. Interval] Deff
> ---------+---------------------------------------------------------
> -----------
> foreign | .2972973 .0534958 .1906803 .4039143
> 1
> -------------------------------------------------------------------
> -----------
> ***** END:
>
> -foreign- is an indicator variable for foreign made cars.
> However, if you
> wanted a point and interval estimate for each category, first
> generate an
> indicator variable for each using -tabulate, generate()-:
>
> . sysuse auto, clear
> . svyset, srs
> . tabulate rep78, generate(repcat)
> . svymean repcat*
>
> Here are the results from -svymean- above:
>
> ***** BEGIN:
> . svymean repcat*
>
> Survey mean estimation
>
> pweight: <none> Number of obs(*)
> = 74
> Strata: <one> Number of strata
> = 1
> PSU: <observations> Number of PSUs
> = 74
> Population size
> = 74
>
> -------------------------------------------------------------------
> -----------
> Mean | Estimate Std. Err. [95% Conf. Interval] Deff
> ---------+---------------------------------------------------------
> -----------
> repcat1 | .0289855 .0203446 -.0116115 .0695825
> 1
> repcat2 | .115942 .0388245 .0384689 .1934152
> 1
> repcat3 | .4347826 .0601159 .3148232 .554742
> 1
> repcat4 | .2608696 .0532498 .1546113 .3671278
> 1
> repcat5 | .1594203 .0443922 .070837 .2480036
> 1
> -------------------------------------------------------------------
> -----------
> (*) Some variables contain missing values.
> ***** END:
>
> Note that you need to look at the label of the newly generated
> variable to
> determine which category the new variable -tabulate- generated
> belongs to. In
> the above case the assignment is pretty straight forward: repcat1
> identifiesrep78 == 1, ..., and repcat5 identifies rep78==5.
>
> The Ci for -repcat1- is not entirely contained in [0,1]. In a
> previous email
> to Statalist, "Nichols, Austin" <[email protected]> indicated
> using the
> inverse logit transform of the confidence interval limits from -
> svylogit-.
> Here is an example of how this can be done:
>
> . sysuse auto, clear
> . svyset, srs
> . tabulate rep78, generate(repcat)
> . svylogit repcat1
> . scalar lcb = invlogit(_b[_cons]-invttail(e(df_r),.025)*_se[_cons])
> . scalar ucb = invlogit(_b[_cons]+invttail(e(df_r),.025)*_se[_cons])
> . di "CI for repcat1 is (" scalar(lcb) ", " scalar(ucb) ")"
>
> Here are the results from above:
>
> ***** BEGIN:
> . svylogit repcat1
>
> Survey logistic regression
>
> pweight: <none> Number of obs
> = 69
> Strata: <one> Number of strata
> = 1
> PSU: <observations> Number of PSUs
> = 69
> Population size
> = 69
> F( 0, 69)
> = .
> Prob > F
> = .
>
> -------------------------------------------------------------------
> -----------
> repcat1 | Coef. Std. Err. t P>|t| [95%
> Conf. Interval]
> -------------+-----------------------------------------------------
> -----------
> _cons | -3.511545 .7228401 -4.86 0.000 -4.95395
> -2.069141
> -------------------------------------------------------------------
> -----------
>
> . scalar lcb = invlogit(_b[_cons]-invttail(e(df_r),.025)*_se[_cons])
>
> . scalar ucb = invlogit(_b[_cons]+invttail(e(df_r),.025)*_se[_cons])
>
> . di "CI for repcat1 is (" scalar(lcb) ", " scalar(ucb) ")"
> CI for repcat1 is (.00700605, .11213258)
> ***** END:
>
> --Jeff
> [email protected]
> *
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>
*
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