We could extract the tt
data frame directly from the object returned by truthTable
. We will, however, need to remove the rows where n == 0
to obtain what you're after.
library(QCA)
tT <- truthTable(LC, "SURV")$tt # Extract data frame from object
tT[tT$n > 0, ] # Show the relevant rows
Output:
DEV URB LIT IND STB OUT n incl PRI cases
1 0 0 0 0 0 0 3 0 0 GR,PT,ES
2 0 0 0 0 1 0 2 0 0 IT,RO
5 0 0 1 0 0 0 2 0 0 HU,PL
6 0 0 1 0 1 0 1 0 0 EE
22 1 0 1 0 1 1 2 1 1 FI,IE
23 1 0 1 1 0 0 1 0 0 AU
24 1 0 1 1 1 1 2 1 1 FR,SE
31 1 1 1 1 0 0 1 0 0 DE
32 1 1 1 1 1 1 4 1 1 BE,CZ,NL,UK
Data:
The data used here is the Lipset binary crisp data
used in the first example in the documentation of QCA::truthTable
. I cannot find the SCHF
-dataset.
Update: I've found the dataset (and updated OP). Same approach, new filtering :-)
library(SetMethods)
library(QCA)
data(SCHF)
tT <- TT_y$tt
tT[order(tT$incl, decreasing = TRUE), 1:10]
Output:
EMP BARGAIN UNI OCCUP STOCK MA OUT n incl PRI
19 0 1 0 0 1 0 1 1 0.981958762886598 0.911392405063291
28 0 1 1 0 1 1 1 2 0.965765765765766 0.898666666666667
16 0 0 1 1 1 1 1 1 0.963696369636964 0.865853658536585
43 1 0 1 0 1 0 1 4 0.950653120464441 0.875457875457875
63 1 1 1 1 1 0 1 2 0.941176470588236 0.755000000000001
64 1 1 1 1 1 1 1 7 0.940740740740741 0.859649122807018
27 0 1 1 0 1 0 1 1 0.930354796320631 0.713513513513514
8 0 0 0 1 1 1 1 3 0.930348258706468 0.776
60 1 1 1 0 1 1 1 4 0.928881179531657 0.796019900497512
11 0 0 1 0 1 0 1 6 0.9201244813278 0.849019607843137
32 0 1 1 1 1 1 1 4 0.910460992907802 0.764568764568765
29 0 1 1 1 0 0 1 1 0.9004329004329 0.333333333333332
56 1 1 0 1 1 1 0 5 0.894366197183099 0.652777777777778
62 1 1 1 1 0 1 0 1 0.88268156424581 0.267441860465115
12 0 0 1 0 1 1 0 10 0.879601226993865 0.789261744966443
61 1 1 1 1 0 0 0 2 0.84375 0.227642276422765
55 1 1 0 1 1 0 0 2 0.8348623853211 0.296874999999999
57 1 1 1 0 0 0 0 1 0.823931623931624 0.213740458015268
2 0 0 0 0 0 1 0 1 0.78688524590164 0.31578947368421
49 1 1 0 0 0 0 0 4 0.742339832869081 0.114832535885168
10 0 0 1 0 0 1 0 2 0.661538461538462 0.158469945355191
53 1 1 0 1 0 0 0 12 0.606844741235392 0.0907335907335905
1 0 0 0 0 0 0 ? 0 - -
3 0 0 0 0 1 0 ? 0 - -
4 0 0 0 0 1 1 ? 0 - -
5 0 0 0 1 0 0 ? 0 - -
6 0 0 0 1 0 1 ? 0 - -
7 0 0 0 1 1 0 ? 0 - -
9 0 0 1 0 0 0 ? 0 - -
13 0 0 1 1 0 0 ? 0 - -
14 0 0 1 1 0 1 ? 0 - -
15 0 0 1 1 1 0 ? 0 - -
17 0 1 0 0 0 0 ? 0 - -
18 0 1 0 0 0 1 ? 0 - -
20 0 1 0 0 1 1 ? 0 - -
21 0 1 0 1 0 0 ? 0 - -
22 0 1 0 1 0 1 ? 0 - -
23 0 1 0 1 1 0 ? 0 - -
24 0 1 0 1 1 1 ? 0 - -
25 0 1 1 0 0 0 ? 0 - -
26 0 1 1 0 0 1 ? 0 - -
30 0 1 1 1 0 1 ? 0 - -
31 0 1 1 1 1 0 ? 0 - -
33 1 0 0 0 0 0 ? 0 - -
34 1 0 0 0 0 1 ? 0 - -
35 1 0 0 0 1 0 ? 0 - -
36 1 0 0 0 1 1 ? 0 - -
37 1 0 0 1 0 0 ? 0 - -
38 1 0 0 1 0 1 ? 0 - -
39 1 0 0 1 1 0 ? 0 - -
40 1 0 0 1 1 1 ? 0 - -
41 1 0 1 0 0 0 ? 0 - -
42 1 0 1 0 0 1 ? 0 - -
44 1 0 1 0 1 1 ? 0 - -
45 1 0 1 1 0 0 ? 0 - -
46 1 0 1 1 0 1 ? 0 - -
47 1 0 1 1 1 0 ? 0 - -
48 1 0 1 1 1 1 ? 0 - -
50 1 1 0 0 0 1 ? 0 - -
51 1 1 0 0 1 0 ? 0 - -
52 1 1 0 0 1 1 ? 0 - -
54 1 1 0 1 0 1 ? 0 - -
58 1 1 1 0 0 1 ? 0 - -
59 1 1 1 0 1 0 ? 0 - -