[U] User's Guide

284 [ U ] 20 Estimation and postestimation commandsExample 18Continuing our previous example: suppose that we wanted the ratio of the coefficients (and standarderrors, Wald test, confidence interval, etc.) of blacks and races other than white and black:. nlcom _b[2.race]/_b[3.race]_nl_1: _b[2.race]/_b[3.race]low Coef. Std. Err. z P>|z| [95% Conf. Interval]_nl_1 1.316531 .7359262 1.79 0.074 -.1258574 2.75892The Wald test given is that of the null hypothesis that the nonlinear combination is zero versus thetwo-sided alternative—this is probably not informative for a ratio. If we would instead like to testwhether this ratio is one, we can rerun nlcom, this time subtracting one from our ratio estimate.. nlcom _b[2.race]/_b[3.race] - 1_nl_1: _b[2.race]/_b[3.race] - 1low Coef. Std. Err. z P>|z| [95% Conf. Interval]_nl_1 .3165314 .7359262 0.43 0.667 -1.125857 1.75892We can interpret this as not much evidence that the “ratio minus 1” is different from zero, meaningthat we cannot reject the null hypothesis that the ratio equals one.When using nlcom, we needed to refer to the model coefficients by their “proper” names, e.g.,b[2.race], and not by the shorthand 2.race, such as when using lincom. If we had typed. nlcom 2.race/3.raceStata would have reported an error.If you have difficulty determining what to type for a coefficient when using lincom or nlcomreplay your results using the coeflegend option. Here are the results for our current estimates,. logit, coeflegendLogistic regression Number of obs = 189LR chi2(8) = 37.12Prob > chi2 = 0.0000Log likelihood = -98.777998 Pseudo R2 = 0.1582low Coef. Legendage -.0464796 _b[low:age]lwd .8420615 _b[low:lwd]race2 1.073456 _b[low:2.race]3 .815367 _b[low:3.race]smoke .8071996 _b[low:smoke]ptd 1.281678 _b[low:ptd]ht 1.435227 _b[low:ht]ui .6576256 _b[low:ui]_cons -1.216781 _b[low:_cons]