Hongsoo <[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 identifies
rep78 == 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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