Search
   >> Home >> Products >> Features >> Overview >> Margins plots

Margins plots

The marginsplot command makes it easy to graph statistics from fitted models. marginsplot graphs the results from margins, and margins itself can compute functions of fitted values after almost any estimation command, linear or nonlinear.

db

Suppose we’ve just fit a two-way ANOVA of systolic blood pressure on age group, sex, and their interaction. We can estimate the response for each cell with margins:

. margins agegrp#sex Adjusted predictions Number of obs = 10351 Model VCE : OLS Expression : Linear prediction, predict()
Delta-method
Margin Std. Err. t P|>t| [95% Conf. Interval]
agegrp#sex
20-29 Male 123.8862 .6052954 204.67 0.000 122.6997 125.0727
20-29 Female 111.2849 .5827553 190.96 0.000 110.1426 112.4272
30-39 Male 124.6818 .728709 171.10 0.000 123.2534 126.1102
30-39 Female 116.2207 .692755 167.77 0.000 114.8627 117.5786
40-49 Male 129.0033 .8187185 157.57 0.000 127.3984 130.6081
40-49 Female 125.0468 .7859058 159.11 0.000 123.5063 126.5874
50-59 Male 136.0864 .8241405 165.13 0.000 134.4709 137.7019
50-59 Female 135.3164 .7703532 175.66 0.000 133.8064 136.8264
60-69 Male 140.7451 .5465096 257.53 0.000 139.6738 141.8163
60-69 Female 142.2368 .5236736 271.61 0.000 141.2103 143.2633
70+ Male 146.3951 .9553456 153.24 0.000 144.5224 148.2678
70+ Female 149.6599 .8717829 171.67 0.000 147.951 151.3687

To study the interaction it would be nice to see a graph. We use marginsplot:

. marginsplot Variables that uniquely identify margins: agegrp sex

marginsplot automatically chooses the y-variable and x-variable and adds confidence intervals. We don’t have to stick with the defaults, though: marginsplot includes a rich set of options for changing axis definitions, labels, curves, confidence intervals, and more.

marginsplot also supports the new features of the margins command, including the contrast operators. Let’s contrast women with men in each age group and plot the contrasts:

. margins r.sex@agegrp, contrast(nowald) Contrasts of adjusted predictions Model VCE : OLS Expression : Linear prediction, predict()
Delta-method
Contrast Std. Err. [95% Conf. Interval]
sex@agegrp
(Female vs Male) 20-29 -12.60132 .8402299 -14.24833 -10.9543
(Female vs Male) 30-39 -8.461161 1.005448 -10.43203 -6.490288
(Female vs Male) 40-49 -3.956451 1.134878 -6.181031 -1.731871
(Female vs Male) 50-59 -.7699782 1.128119 -2.981309 1.441353
(Female vs Male) 60-69 1.491684 .756906 .0080022 2.975367
(Female vs Male) 70+ 3.264762 1.293325 .729594 5.79993
. marginsplot, yline(0) Variables that uniquely identify margins: agegrp

We see that systolic blood pressure is lower in younger women than in younger men. But among the old, the relationship is reversed, and women have higher blood pressure than men.

marginsplot works with nonlinear models, too. Suppose we've fit a logistic regression, modeling the probability of high blood pressure as a function of sex, age group, body mass index, and their interactions. marginsplot can contrast women with men on the probability scale:

. quietly margins r.sex, at(bmi=(10(1)65)) . marginsplot, xlabel(10(10)60) recast(line) recastci(rarea) Variables that uniquely identify margins: bmi

We’ve only scratched the surface—anything that margins can compute, marginsplot can graph.

The Stata Blog: Not Elsewhere Classified Find us on Facebook Follow us on Twitter LinkedIn Google+ Watch us on YouTube