Hi
I was wondering how to obtain appropriate lincom results after using transform_margins.
I am using svy:logistic to analyze a survey database. Margins after the svy:logistic model gave confidence intervals with negative values, so I used margins with the linear predictor, and then used transform_margins discussed by Jeff Pitblado here :
https://stats.stackexchange.com/ques...rgins-in-stata
This provided estimates with confidence intervals that were not negative.
I would like to assess the difference between two levels of a variable for the linear predictor margins, and ensure that the confidence intervals are appropriate. lincom does not seem to be appropriate for the linear predictor margins. I have tried nlcom with the invlogit of each of the estimates, and this seems to provide a reasonable estimate. However, does anyone know if this provides the appropriate estimate and confidence interval for the difference?
an example is below:
I was wondering how to obtain appropriate lincom results after using transform_margins.
I am using svy:logistic to analyze a survey database. Margins after the svy:logistic model gave confidence intervals with negative values, so I used margins with the linear predictor, and then used transform_margins discussed by Jeff Pitblado here :
https://stats.stackexchange.com/ques...rgins-in-stata
This provided estimates with confidence intervals that were not negative.
I would like to assess the difference between two levels of a variable for the linear predictor margins, and ensure that the confidence intervals are appropriate. lincom does not seem to be appropriate for the linear predictor margins. I have tried nlcom with the invlogit of each of the estimates, and this seems to provide a reasonable estimate. However, does anyone know if this provides the appropriate estimate and confidence interval for the difference?
an example is below:
Code:
. webuse nhanes2
. quietly svy, subpop(if agegrp>3):logit diabetes bmi black##female
*using the probabilities
**********************************************************************
. margins female, at(black=(0 1)) subpop(if agegrp>3) vce(unconditional) post
------------------------------------------------------------------------------
| Linearized
| Margin Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#female |
1 0 | .0672466 .0073744 9.12 0.000 .0522064 .0822868
1 1 | .066051 .004407 14.99 0.000 .0570628 .0750391
2 0 | .1287978 .0245295 5.25 0.000 .0787696 .1788259
2 1 | .0915743 .0170822 5.36 0.000 .0567349 .1264138
------------------------------------------------------------------------------
. lincom _b[2._at#1.female]-_b[1bn._at#1.female]
------------------------------------------------------------------------------
| Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .0255234 .0176777 1.44 0.159 -.0105305 .0615773
------------------------------------------------------------------------------
.
*using the linear predictor
**********************************************************************
. margins female, at(black=(0 1)) subpop(if agegrp>3) vce(unconditional) exp(predict(xb)) post
------------------------------------------------------------------------------
| Linearized
| Margin Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#female |
1 0 | -2.676449 .1199975 -22.30 0.000 -2.921185 -2.431712
1 1 | -2.695843 .0717618 -37.57 0.000 -2.842202 -2.549484
2 0 | -1.949814 .2179706 -8.95 0.000 -2.394368 -1.50526
2 1 | -2.337782 .211021 -11.08 0.000 -2.768162 -1.907402
------------------------------------------------------------------------------
. transform_margins invlogit(@)
----------------------------------------------
| b ll ul
-------------+--------------------------------
_at#female |
1 0 | .0643774 .0511162 .0807862
1 1 | .0632191 .0550858 .0724611
2 0 | .1245737 .0836032 .1816423
2 1 | .0880418 .0590691 .129273
----------------------------------------------
. lincom _b[2._at#1.female]-_b[1bn._at#1.female]
------------------------------------------------------------------------------
| Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .358061 .2201364 1.63 0.114 -.0909101 .8070322
------------------------------------------------------------------------------
. nlcom invlogit(_b[2._at#1.female])-invlogit(_b[1bn._at#1.female])
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | .0248227 .0173011 1.43 0.151 -.0090867 .0587322
------------------------------------------------------------------------------
