I am unfortunately unfamiliar with postgr3 and -clarify-, but I have had a similar problem before and ended up employing a solution used by Andreas Wimmer & Brian Min (2006) (article content unrelated).
Basically it boils down to four steps:
- create copies of your control variables.
- regress with the copies.
- fix the value of the control variable copies for all cases using whatever assumptions you deem appropriate.
- use the regression equation to compute predicted values for all cases.
With sorted data this produces a nice distribution of predicted probabilities estimates which can be easily plotted. You can also easily get summary data or run additional statistics on the predicted probabilities.
/*logistic regression, c1, c2, and c3 are controls*/
logit y xlin xsq c1 c2 c3
/*generate graphing copies*/
gen c1g = c1
gen c2g = c2
gen c3g = c3
/*regress on copies, resultis will be idential to above regression*/
logit y xlin xsq c1g c2g c3g
/*replace copies with fixed values, in this case median values*/
quietly sum c1, det
replace c1g = r(p50)
quietly sum c2, det
replace c2g = r(p50)
quietly sum c3, det
replace c3g = r(p50)
/*generate predicted values*/
predict pr, p /*probability*/
predict logodds, xb
predict stderr, stdp
generate lodds_lb = logodds - 1.96*stderr
generate lodds_ub = logodds + 1.96*stderr
generate ub_p = exp(lodds_ub)/(1+exp(lodds_ub)) /*upper confidence band*/
generate lb_p = exp(lodds_lb)/(1+exp(lodds_lb)) /*lower confidence band*/
/* PLOT RESULTS */
twoway (rarea lb_p ub_p xlin, bcolor(gs14)) ///
(line pr xlin, clcolor(black) clwidth(medthick)), ///
xline(0, lp(dash) lc(gs14) lw(thin)) ///
ylabel(#8, labsize(small)) xlabel(#20, labsize(small)) ///
ytitle(Predicted probability of y, size(small)) xtitle(x, size(small)) ///
legend(order(2 "Probability of y" 1 "95% confidence interval") size(small) rows(1)) ///