dtable
Stata 18’s new
dtable is
terrific for creating a "Table 1", but by default if provided a categorical
variable and asked to test for differences by group, it will provide a single
Chi-sq test by default.
. sysuse auto, clear
(1978 automobile data)
. dtable i.rep78, by(foreign, tests)
note: using test pearson across levels of foreign for rep78.
-----------------------------------------------------------
Car origin
Domestic Foreign Total Test
-----------------------------------------------------------
N 52 (70.3%) 22 (29.7%) 74 (100.0%)
Repair record 1978
1 2 (4.2%) 0 (0.0%) 2 (2.9%) <0.001
2 8 (16.7%) 0 (0.0%) 8 (11.6%)
3 27 (56.2%) 3 (14.3%) 30 (43.5%)
4 9 (18.8%) 9 (42.9%) 18 (26.1%)
5 2 (4.2%) 9 (42.9%) 11 (15.9%)
-----------------------------------------------------------Instead you may want a category-by-category comparison of proportions. We’ll need to create a series of binary variables first.
. quietly tab rep78, gen(repcat)
. label var repcat1 "Repair Record Category 1"
. label var repcat2 "Repair Record Category 2"
. label var repcat3 "Repair Record Category 3"
. label var repcat4 "Repair Record Category 4"
. label var repcat5 "Repair Record Category 5"
Next, we can add each of the repcat* to dtable, but
preface with 1. to only include the "yes" for each binary.
. dtable 1.repcat*, by(foreign, tests)
note: using test pearson across levels of foreign for repcat1, repcat2, repcat3, repcat4, and repcat5.
-----------------------------------------------------------------
Car origin
Domestic Foreign Total Test
-----------------------------------------------------------------
N 52 (70.3%) 22 (29.7%) 74 (100.0%)
Repair Record Category 1
1 2 (4.2%) 0 (0.0%) 2 (2.9%) 0.342
Repair Record Category 2
1 8 (16.7%) 0 (0.0%) 8 (11.6%) 0.047
Repair Record Category 3
1 27 (56.2%) 3 (14.3%) 30 (43.5%) 0.001
Repair Record Category 4
1 9 (18.8%) 9 (42.9%) 18 (26.1%) 0.036
Repair Record Category 5
1 2 (4.2%) 9 (42.9%) 11 (15.9%) <0.001
-----------------------------------------------------------------
So we have the tests we want (at least, if you’re OK with a Chi-sq test for
each binary against the group variable), but the extra "1" entries is
redundant. We can use
collect
to modify this.
. collect style header repcat1 repcat2 repcat3 repcat4 repcat5, level(hide)
. collect preview
-----------------------------------------------------------------
Car origin
Domestic Foreign Total Test
-----------------------------------------------------------------
N 52 (70.3%) 22 (29.7%) 74 (100.0%)
Repair Record Category 1 2 (4.2%) 0 (0.0%) 2 (2.9%) 0.342
Repair Record Category 2 8 (16.7%) 0 (0.0%) 8 (11.6%) 0.047
Repair Record Category 3 27 (56.2%) 3 (14.3%) 30 (43.5%) 0.001
Repair Record Category 4 9 (18.8%) 9 (42.9%) 18 (26.1%) 0.036
Repair Record Category 5 2 (4.2%) 9 (42.9%) 11 (15.9%) <0.001
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