dtable
By default, if you use dtable
to compare groups and include a “Total” column, the “Total” comes last.
Here’s how to re-order the columns.
. 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%)
-----------------------------------------------------------
The collect query autolevels command can be used to examine
the current ordering of columns:
. collect query autolevels foreign
Automatic dimension levels
Collection: DTable
Dimension: foreign
Levels: 0 1 .m _dtable_test
Look at the “Levels:” line. We can use collect label
list to check what those three values are:
. collect label list foreign
Collection: DTable
Dimension: foreign
Label: Car origin
Level labels:
.m Total
0 Domestic
1 Foreign
_dtable_test Test
So “.m” is the “Total” column and
“_dtable_test” is the p-value. We can re-arrange it with
collect style autolevels. We’ll use the
s(levels) macro, which currently contains the string
“0 1 .m _dtable_test” from the “Levels:” line
of the collect query autolevels foreign” output to avoid
having to rewrite everything (duplicate entries, as .m will
be, are apparently ignored), but we could of course manually order all
columns.
. collect style autolevels foreign .m `s(levels)', clear
. collect query autolevels foreign
Automatic dimension levels
Collection: DTable
Dimension: foreign
Levels: .m 0 1 _dtable_test
. collect preview
-----------------------------------------------------------
Car origin
Total Domestic Foreign Test
-----------------------------------------------------------
N 74 (100.0%) 52 (70.3%) 22 (29.7%)
Repair record 1978
1 2 (2.9%) 2 (4.2%) 0 (0.0%) <0.001
2 8 (11.6%) 8 (16.7%) 0 (0.0%)
3 30 (43.5%) 27 (56.2%) 3 (14.3%)
4 18 (26.1%) 9 (18.8%) 9 (42.9%)
5 11 (15.9%) 2 (4.2%) 9 (42.9%)
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