suppressWarnings(RNGversion("3.5.2"))

set.seed(290875)

datLB <-
structure(list(Site = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L), ID = c(1.1, 2.1, 3.1, 4.1, 5.1, 
6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 3.2, 4.2, 5.2, 
6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 1.3, 2.3, 3.3, 
4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 1.4, 2.4, 3.4, 
4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 1.6, 2.6, 
3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 14.6, 
15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 10.7, 11.7, 
12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8, 
12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 1.9, 2.9, 3.9, 
4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 2.1, 3.1, 4.1, 
5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 3.2, 4.2, 
5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 1.3, 2.3, 
3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 1.4, 2.4, 
3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 1.6, 
2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 
14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 10.7, 
11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 
11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 1.9, 2.9, 
3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 2.1, 3.1, 
4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 3.2, 
4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 1.3, 
2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 1.4, 
2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 
1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 
13.6, 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 
10.7, 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 
10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 1.9, 
2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 2.1, 
3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 
3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 
1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 
1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 
6.5, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 
12.6, 13.6, 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 
9.7, 10.7, 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 
9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 
1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 
2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 
2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 
14.2, 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 
12.3, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 
4.5, 5.5, 6.5, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 
11.6, 12.6, 13.6, 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 
8.7, 9.7, 10.7, 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 
8.8, 9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 
19.8, 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9
), Treat = structure(c(2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 
1L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 
1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 
3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 
2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 
3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 
2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 
1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 
2L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 
3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 
1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 
3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 
3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 
1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 
2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 
1L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 
1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 
2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 
1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 
1L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 
3L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 
3L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 
3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 
1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 
3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 
2L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 
1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 
3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 
2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 
1L, 2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 
3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 
2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 
3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 
2L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 
2L, 1L, 3L, 2L, 1L, 1L, 2L), .Label = c("10000U", "5000U", "Placebo"
), class = "factor"), Age = c(65L, 70L, 64L, 59L, 76L, 59L, 72L, 
40L, 52L, 47L, 57L, 47L, 70L, 49L, 59L, 64L, 45L, 66L, 49L, 54L, 
47L, 31L, 53L, 61L, 40L, 67L, 54L, 41L, 66L, 68L, 41L, 77L, 41L, 
56L, 46L, 46L, 47L, 35L, 58L, 62L, 73L, 52L, 53L, 69L, 55L, 52L, 
51L, 56L, 65L, 35L, 43L, 61L, 43L, 64L, 57L, 60L, 44L, 41L, 51L, 
57L, 42L, 48L, 57L, 39L, 67L, 39L, 69L, 54L, 67L, 58L, 72L, 65L, 
68L, 75L, 26L, 36L, 72L, 54L, 64L, 39L, 54L, 48L, 83L, 74L, 41L, 
65L, 79L, 63L, 63L, 34L, 42L, 57L, 68L, 51L, 51L, 61L, 42L, 73L, 
57L, 59L, 57L, 68L, 55L, 46L, 79L, 43L, 50L, 39L, 57L, 65L, 70L, 
64L, 59L, 76L, 59L, 72L, 40L, 52L, 47L, 57L, 47L, 70L, 49L, 59L, 
64L, 45L, 66L, 49L, 54L, 47L, 31L, 53L, 61L, 40L, 67L, 54L, 41L, 
66L, 68L, 41L, 77L, 41L, 56L, 46L, 46L, 47L, 35L, 58L, 62L, 73L, 
52L, 53L, 69L, 55L, 52L, 51L, 56L, 65L, 35L, 43L, 61L, 43L, 64L, 
57L, 60L, 44L, 41L, 51L, 57L, 42L, 48L, 57L, 39L, 67L, 39L, 69L, 
54L, 67L, 58L, 72L, 65L, 68L, 75L, 26L, 36L, 72L, 54L, 64L, 39L, 
54L, 48L, 83L, 74L, 41L, 65L, 79L, 63L, 63L, 34L, 42L, 57L, 68L, 
51L, 51L, 61L, 42L, 73L, 57L, 59L, 57L, 68L, 55L, 46L, 79L, 43L, 
50L, 39L, 57L, 65L, 70L, 64L, 59L, 76L, 59L, 72L, 40L, 52L, 47L, 
57L, 47L, 70L, 49L, 59L, 64L, 45L, 66L, 49L, 54L, 47L, 31L, 53L, 
61L, 40L, 67L, 54L, 41L, 66L, 68L, 41L, 77L, 41L, 56L, 46L, 46L, 
47L, 35L, 58L, 62L, 73L, 52L, 53L, 69L, 55L, 52L, 51L, 56L, 65L, 
35L, 43L, 61L, 43L, 64L, 57L, 60L, 44L, 41L, 51L, 57L, 42L, 48L, 
57L, 39L, 67L, 39L, 69L, 54L, 67L, 58L, 72L, 65L, 68L, 75L, 26L, 
36L, 72L, 54L, 64L, 39L, 54L, 48L, 83L, 74L, 41L, 65L, 79L, 63L, 
63L, 34L, 42L, 57L, 68L, 51L, 51L, 61L, 42L, 73L, 57L, 59L, 57L, 
68L, 55L, 46L, 79L, 43L, 50L, 39L, 57L, 65L, 70L, 64L, 59L, 76L, 
59L, 72L, 40L, 52L, 47L, 57L, 47L, 70L, 49L, 59L, 64L, 45L, 66L, 
49L, 54L, 47L, 31L, 53L, 61L, 40L, 67L, 54L, 41L, 66L, 68L, 41L, 
77L, 41L, 56L, 46L, 46L, 47L, 35L, 58L, 62L, 73L, 52L, 53L, 69L, 
55L, 52L, 51L, 56L, 65L, 35L, 43L, 61L, 43L, 64L, 57L, 60L, 44L, 
41L, 51L, 57L, 42L, 48L, 57L, 39L, 67L, 39L, 69L, 54L, 67L, 58L, 
72L, 65L, 68L, 75L, 26L, 36L, 72L, 54L, 64L, 39L, 54L, 48L, 83L, 
74L, 41L, 65L, 79L, 63L, 63L, 34L, 42L, 57L, 68L, 51L, 51L, 61L, 
42L, 73L, 57L, 59L, 57L, 68L, 55L, 46L, 79L, 43L, 50L, 39L, 57L, 
65L, 70L, 64L, 59L, 76L, 59L, 72L, 40L, 52L, 47L, 57L, 47L, 70L, 
49L, 59L, 64L, 45L, 66L, 49L, 54L, 47L, 31L, 53L, 61L, 40L, 67L, 
54L, 41L, 66L, 68L, 41L, 77L, 41L, 56L, 46L, 46L, 47L, 35L, 58L, 
62L, 73L, 52L, 53L, 69L, 55L, 52L, 51L, 56L, 65L, 35L, 43L, 61L, 
43L, 64L, 57L, 60L, 44L, 41L, 51L, 57L, 42L, 48L, 57L, 39L, 67L, 
39L, 69L, 54L, 67L, 58L, 72L, 65L, 68L, 75L, 26L, 36L, 72L, 54L, 
64L, 39L, 54L, 48L, 83L, 74L, 41L, 65L, 79L, 63L, 63L, 34L, 42L, 
57L, 68L, 51L, 51L, 61L, 42L, 73L, 57L, 59L, 57L, 68L, 55L, 46L, 
79L, 43L, 50L, 39L, 57L), W0 = c(32L, 60L, 44L, 53L, 53L, 49L, 
42L, 34L, 41L, 27L, 48L, 34L, 49L, 46L, 56L, 59L, 62L, 50L, 42L, 
53L, 67L, 44L, 65L, 56L, 30L, 47L, 50L, 34L, 39L, 43L, 46L, 52L, 
38L, 33L, 28L, 34L, 39L, 29L, 52L, 52L, 54L, 52L, 47L, 44L, 42L, 
42L, 44L, 60L, 60L, 50L, 38L, 44L, 54L, 54L, 56L, 51L, 53L, 36L, 
59L, 49L, 50L, 46L, 55L, 46L, 34L, 57L, 41L, 49L, 42L, 31L, 50L, 
35L, 38L, 53L, 42L, 53L, 46L, 50L, 43L, 46L, 41L, 33L, 36L, 33L, 
37L, 24L, 42L, 30L, 42L, 49L, 58L, 26L, 37L, 40L, 33L, 41L, 46L, 
40L, 40L, 61L, 35L, 58L, 49L, 52L, 45L, 67L, 57L, 63L, 53L, 32L, 
60L, 44L, 53L, 53L, 49L, 42L, 34L, 41L, 27L, 48L, 34L, 49L, 46L, 
56L, 59L, 62L, 50L, 42L, 53L, 67L, 44L, 65L, 56L, 30L, 47L, 50L, 
34L, 39L, 43L, 46L, 52L, 38L, 33L, 28L, 34L, 39L, 29L, 52L, 52L, 
54L, 52L, 47L, 44L, 42L, 42L, 44L, 60L, 60L, 50L, 38L, 44L, 54L, 
54L, 56L, 51L, 53L, 36L, 59L, 49L, 50L, 46L, 55L, 46L, 34L, 57L, 
41L, 49L, 42L, 31L, 50L, 35L, 38L, 53L, 42L, 53L, 46L, 50L, 43L, 
46L, 41L, 33L, 36L, 33L, 37L, 24L, 42L, 30L, 42L, 49L, 58L, 26L, 
37L, 40L, 33L, 41L, 46L, 40L, 40L, 61L, 35L, 58L, 49L, 52L, 45L, 
67L, 57L, 63L, 53L, 32L, 60L, 44L, 53L, 53L, 49L, 42L, 34L, 41L, 
27L, 48L, 34L, 49L, 46L, 56L, 59L, 62L, 50L, 42L, 53L, 67L, 44L, 
65L, 56L, 30L, 47L, 50L, 34L, 39L, 43L, 46L, 52L, 38L, 33L, 28L, 
34L, 39L, 29L, 52L, 52L, 54L, 52L, 47L, 44L, 42L, 42L, 44L, 60L, 
60L, 50L, 38L, 44L, 54L, 54L, 56L, 51L, 53L, 36L, 59L, 49L, 50L, 
46L, 55L, 46L, 34L, 57L, 41L, 49L, 42L, 31L, 50L, 35L, 38L, 53L, 
42L, 53L, 46L, 50L, 43L, 46L, 41L, 33L, 36L, 33L, 37L, 24L, 42L, 
30L, 42L, 49L, 58L, 26L, 37L, 40L, 33L, 41L, 46L, 40L, 40L, 61L, 
35L, 58L, 49L, 52L, 45L, 67L, 57L, 63L, 53L, 32L, 60L, 44L, 53L, 
53L, 49L, 42L, 34L, 41L, 27L, 48L, 34L, 49L, 46L, 56L, 59L, 62L, 
50L, 42L, 53L, 67L, 44L, 65L, 56L, 30L, 47L, 50L, 34L, 39L, 43L, 
46L, 52L, 38L, 33L, 28L, 34L, 39L, 29L, 52L, 52L, 54L, 52L, 47L, 
44L, 42L, 42L, 44L, 60L, 60L, 50L, 38L, 44L, 54L, 54L, 56L, 51L, 
53L, 36L, 59L, 49L, 50L, 46L, 55L, 46L, 34L, 57L, 41L, 49L, 42L, 
31L, 50L, 35L, 38L, 53L, 42L, 53L, 46L, 50L, 43L, 46L, 41L, 33L, 
36L, 33L, 37L, 24L, 42L, 30L, 42L, 49L, 58L, 26L, 37L, 40L, 33L, 
41L, 46L, 40L, 40L, 61L, 35L, 58L, 49L, 52L, 45L, 67L, 57L, 63L, 
53L, 32L, 60L, 44L, 53L, 53L, 49L, 42L, 34L, 41L, 27L, 48L, 34L, 
49L, 46L, 56L, 59L, 62L, 50L, 42L, 53L, 67L, 44L, 65L, 56L, 30L, 
47L, 50L, 34L, 39L, 43L, 46L, 52L, 38L, 33L, 28L, 34L, 39L, 29L, 
52L, 52L, 54L, 52L, 47L, 44L, 42L, 42L, 44L, 60L, 60L, 50L, 38L, 
44L, 54L, 54L, 56L, 51L, 53L, 36L, 59L, 49L, 50L, 46L, 55L, 46L, 
34L, 57L, 41L, 49L, 42L, 31L, 50L, 35L, 38L, 53L, 42L, 53L, 46L, 
50L, 43L, 46L, 41L, 33L, 36L, 33L, 37L, 24L, 42L, 30L, 42L, 49L, 
58L, 26L, 37L, 40L, 33L, 41L, 46L, 40L, 40L, 61L, 35L, 58L, 49L, 
52L, 45L, 67L, 57L, 63L, 53L), Fem = c(1L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 
1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 
0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 
1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 
0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 
0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 
1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 
1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 
1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 
0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L), Week = c(2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L), Total = c(30L, 26L, 20L, 61L, 35L, 34L, 32L, 33L, 32L, 
10L, 41L, 19L, 47L, 35L, 44L, 48L, 60L, 53L, 42L, 56L, 64L, 40L, 
58L, 54L, 33L, NA, 43L, 29L, 41L, 31L, 26L, 44L, 19L, 38L, 16L, 
23L, 37L, 42L, 55L, 30L, 52L, 44L, 45L, 34L, 39L, 14L, 34L, 57L, 
53L, 50L, 27L, NA, 53L, 32L, 55L, 50L, 56L, 29L, 53L, 50L, 38L, 
48L, 34L, 44L, 31L, 48L, 40L, 25L, 30L, 18L, 27L, 24L, 25L, 40L, 
48L, 45L, 47L, 42L, 24L, 39L, 30L, 27L, 15L, 32L, NA, 29L, 23L, 
22L, 46L, 25L, 46L, 26L, NA, 24L, 10L, 50L, NA, 28L, 16L, 52L, 
21L, 38L, 45L, 46L, 46L, 63L, NA, 51L, 38L, 24L, 27L, 23L, 64L, 
48L, 43L, 32L, 21L, 34L, 31L, 32L, 21L, 44L, 45L, 48L, 56L, 60L, 
52L, 43L, 52L, 65L, 32L, 55L, 52L, 25L, 54L, 51L, 27L, 33L, 29L, 
29L, 47L, 20L, 40L, 11L, 16L, 39L, 35L, 51L, 43L, 52L, 33L, 41L, 
29L, 38L, 9L, 32L, 53L, 55L, NA, 16L, 46L, 51L, 40L, 44L, 50L, 
47L, 24L, 45L, 48L, 42L, 46L, 26L, 47L, 25L, 50L, 42L, 30L, 40L, 
23L, 43L, 34L, 21L, 38L, 26L, 52L, 45L, 52L, 17L, 25L, 44L, 25L, 
16L, 31L, NA, 18L, 30L, 21L, 41L, 30L, 46L, 27L, 23L, 25L, 13L, 
22L, 41L, 29L, 18L, 61L, 29L, 50L, 36L, 36L, 33L, 71L, 36L, 46L, 
NA, 37L, 41L, 26L, 62L, 49L, 48L, 43L, 27L, 35L, 32L, 35L, 24L, 
48L, 49L, 54L, 55L, 64L, 57L, 33L, 54L, 64L, 36L, NA, 48L, 29L, 
43L, 46L, 21L, 39L, 28L, 33L, 50L, 27L, 48L, 7L, 15L, 39L, 24L, 
52L, 45L, 54L, 54L, 45L, 28L, 47L, 9L, 35L, 52L, 62L, 46L, 19L, 
26L, 56L, 52L, 50L, 56L, 53L, 32L, 44L, 56L, 43L, 57L, 40L, 50L, 
NA, 50L, 38L, 41L, 43L, 26L, 32L, 28L, 33L, 44L, 37L, 51L, 45L, 
60L, 37L, 15L, 46L, 30L, 17L, 27L, NA, 20L, 36L, 25L, 43L, 49L, 
50L, 22L, 18L, 37L, 16L, 28L, 41L, 30L, 25L, 68L, 30L, 53L, NA, 
NA, 44L, 66L, 23L, 50L, 33L, 39L, 65L, 35L, NA, 41L, 48L, 42L, 
32L, 37L, 6L, 57L, 28L, 44L, 53L, 49L, 57L, 67L, 61L, 37L, 55L, 
62L, 42L, 56L, 52L, 32L, 46L, 49L, 22L, 37L, 33L, 45L, 50L, 29L, 
49L, 13L, 17L, 45L, 29L, 54L, 47L, 51L, 46L, 43L, 35L, 39L, 16L, 
54L, 53L, 67L, 50L, 23L, 30L, 39L, 42L, 53L, 59L, 51L, 45L, 50L, 
49L, 42L, 57L, 49L, 46L, NA, 50L, 50L, 41L, 36L, 33L, 40L, 34L, 
42L, 47L, 37L, 52L, 50L, 54L, 36L, 21L, 46L, 28L, 22L, 49L, NA, 
25L, 41L, 26L, 49L, 55L, 56L, 38L, 34L, NA, 32L, 34L, 58L, 37L, 
33L, 59L, 35L, 47L, 40L, 45L, 46L, 68L, NA, 50L, 36L, 36L, 67L, 
35L, NA, 51L, 51L, 46L, 38L, 36L, 14L, 51L, 28L, 44L, 56L, 60L, 
58L, 66L, 54L, 43L, 51L, 64L, 43L, 60L, 53L, 32L, 50L, 53L, 22L, 
37L, 38L, 56L, 49L, 32L, 44L, 21L, 29L, 43L, 42L, 57L, 46L, 57L, 
47L, 41L, 41L, 39L, 33L, 53L, 58L, NA, 57L, 26L, 34L, 9L, 47L, 
52L, 53L, 51L, 36L, 48L, 57L, 46L, 49L, 47L, 51L, NA, 49L, 56L, 
31L, 45L, 41L, 47L, 28L, 53L, 53L, 43L, 53L, 52L, 59L, 38L, 25L, 
44L, 30L, 41L, 60L, NA, 41L, 43L, 33L, 54L, 58L, 60L, 35L, 36L, 
38L, 16L, 36L, 53L, 44L, 48L, 71L, 48L, 59L, 52L, 54L, 48L, 71L, 
52L, 54L, 51L)), .Names = c("Site", "ID", "Treat", "Age", "W0", 
"Fem", "Week", "Total"), class = "data.frame", row.names = c(NA, 
-545L))


library("partykit")
library("rpart")

fac <- c(1,3,6)
for(j in 1:length(fac)) datLB[,fac[j]] <- as.factor(datLB[,fac[j]])
dat <- subset(datLB,Week==16)
dat <- na.omit(dat)
fit <- rpart(Total ~ Site + Treat + Age + W0, 
             method = "anova", data = dat)
f <- as.party(fit)
plot(f,tp_args = list(id = FALSE))
f[10]$node$split

### factors with empty levels in learning sample
if (require("mlbench")) {
    data("Vowel", package = "mlbench")
    ct <- ctree(V2 ~ V1, data = Vowel[1:200,]) ### only levels 1:4 in V1
    try(p1 <- predict(ct, newdata = Vowel)) ### 14 levels in V1
}

### deal with empty levels for teststat = "quad" by
### removing elements of the teststatistic with zero variance
### reported by Wei-Yin Loh <loh@stat.wisc.edu>
tdata <-
structure(list(ytrain = structure(c(3L, 7L, 3L, 2L, 1L, 6L, 2L, 
1L, 1L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 2L, 6L, 2L, 4L, 6L, 1L, 2L, 
3L, 7L, 6L, 4L, 6L, 2L, 2L, 1L, 2L, 6L, 1L, 7L, 1L, 3L, 6L, 2L, 
1L, 7L, 2L, 7L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 6L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 6L, 6L, 7L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 6L, 5L, 1L, 
1L, 4L, 7L, 2L, 3L, 3L, 3L, 1L, 8L, 1L, 6L, 2L, 8L, 3L, 4L, 6L, 
2L, 7L, 3L, 6L, 6L, 1L, 1L, 2L, 6L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 
7L, 2L, 3L, 6L, 2L, 5L, 2L, 2L, 2L, 1L, 3L, 3L, 7L, 3L, 2L, 3L, 
3L, 1L, 6L, 1L, 1L, 1L, 7L, 1L, 3L, 7L, 6L, 1L, 3L, 3L, 6L, 4L, 
2L, 3L, 2L, 8L, 3L, 4L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 4L, 6L, 
4L, 8L, 2L, 2L, 3L, 3L, 2L, 3L, 6L, 2L, 1L, 2L, 2L, 7L, 2L, 1L, 
1L, 7L, 2L, 7L, 6L, 6L, 6L), .Label = c("0", "1", "2", "3", "4", 
"5", "6", "7"), class = "factor"), landmass = c(5L, 3L, 4L, 6L, 
3L, 4L, 1L, 2L, 2L, 6L, 3L, 1L, 5L, 5L, 1L, 3L, 1L, 4L, 1L, 5L, 
4L, 2L, 1L, 5L, 3L, 4L, 5L, 4L, 4L, 1L, 4L, 1L, 4L, 2L, 5L, 2L, 
4L, 4L, 6L, 1L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 2L, 4L, 1L, 4L, 4L, 
3L, 2L, 6L, 3L, 3L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 1L, 6L, 1L, 4L, 
4L, 2L, 1L, 1L, 5L, 3L, 3L, 6L, 5L, 5L, 3L, 5L, 3L, 4L, 1L, 5L, 
5L, 5L, 4L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 4L, 4L, 5L, 5L, 3L, 6L, 
4L, 1L, 6L, 5L, 1L, 4L, 4L, 6L, 5L, 3L, 1L, 6L, 1L, 4L, 4L, 5L, 
5L, 3L, 5L, 5L, 2L, 6L, 2L, 2L, 6L, 3L, 1L, 5L, 3L, 4L, 4L, 5L, 
4L, 4L, 5L, 6L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L, 4L, 2L, 3L, 3L, 
5L, 5L, 4L, 5L, 4L, 6L, 2L, 4L, 5L, 1L, 5L, 4L, 3L, 2L, 1L, 1L, 
5L, 6L, 3L, 2L, 5L, 6L, 3L, 4L, 4L, 4L), zone = c(1L, 1L, 1L, 
3L, 1L, 2L, 4L, 3L, 3L, 2L, 1L, 4L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 
1L, 2L, 3L, 4L, 1L, 1L, 4L, 1L, 2L, 1L, 4L, 4L, 4L, 1L, 3L, 1L, 
4L, 2L, 2L, 3L, 4L, 4L, 1L, 1L, 1L, 1L, 4L, 4L, 3L, 1L, 4L, 1L, 
1L, 4L, 3L, 2L, 1L, 1L, 4L, 2L, 4L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 
4L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 2L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 
1L, 4L, 4L, 1L, 1L, 4L, 4L, 2L, 2L, 1L, 1L, 4L, 2L, 4L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 4L, 2L, 3L, 3L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 
1L, 4L, 4L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 1L, 4L, 1L, 
1L, 1L, 1L, 2L, 1L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 3L, 4L, 
4L, 1L, 2L, 1L, 4L, 1L, 3L, 1L, 2L, 2L, 2L), area = c(648L, 29L, 
2388L, 0L, 0L, 1247L, 0L, 2777L, 2777L, 7690L, 84L, 19L, 1L, 
143L, 0L, 31L, 23L, 113L, 0L, 47L, 600L, 8512L, 0L, 6L, 111L, 
274L, 678L, 28L, 474L, 9976L, 4L, 0L, 623L, 757L, 9561L, 1139L, 
2L, 342L, 0L, 51L, 115L, 9L, 128L, 43L, 22L, 0L, 49L, 284L, 1001L, 
21L, 28L, 1222L, 1L, 12L, 18L, 337L, 547L, 91L, 268L, 10L, 108L, 
249L, 0L, 132L, 0L, 0L, 109L, 246L, 36L, 215L, 28L, 112L, 1L, 
93L, 103L, 1904L, 1648L, 435L, 70L, 21L, 301L, 323L, 11L, 372L, 
98L, 181L, 583L, 0L, 236L, 10L, 30L, 111L, 0L, 3L, 587L, 118L, 
333L, 0L, 0L, 0L, 1031L, 1973L, 1L, 1566L, 0L, 447L, 783L, 0L, 
140L, 41L, 0L, 268L, 128L, 1267L, 925L, 121L, 195L, 324L, 212L, 
804L, 76L, 463L, 407L, 1285L, 300L, 313L, 9L, 11L, 237L, 26L, 
0L, 2150L, 196L, 72L, 1L, 30L, 637L, 1221L, 99L, 288L, 66L, 0L, 
0L, 0L, 2506L, 63L, 450L, 41L, 185L, 36L, 945L, 514L, 57L, 1L, 
5L, 164L, 781L, 0L, 84L, 236L, 245L, 178L, 0L, 9363L, 22402L, 
15L, 0L, 912L, 333L, 3L, 256L, 905L, 753L, 391L), population = c(16L, 
3L, 20L, 0L, 0L, 7L, 0L, 28L, 28L, 15L, 8L, 0L, 0L, 90L, 0L, 
10L, 0L, 3L, 0L, 1L, 1L, 119L, 0L, 0L, 9L, 7L, 35L, 4L, 8L, 24L, 
0L, 0L, 2L, 11L, 1008L, 28L, 0L, 2L, 0L, 2L, 10L, 1L, 15L, 5L, 
0L, 0L, 6L, 8L, 47L, 5L, 0L, 31L, 0L, 0L, 1L, 5L, 54L, 0L, 1L, 
1L, 17L, 61L, 0L, 10L, 0L, 0L, 8L, 6L, 1L, 1L, 6L, 4L, 5L, 11L, 
0L, 157L, 39L, 14L, 3L, 4L, 57L, 7L, 2L, 118L, 2L, 6L, 17L, 0L, 
3L, 3L, 1L, 1L, 0L, 0L, 9L, 6L, 13L, 0L, 0L, 0L, 2L, 77L, 0L, 
2L, 0L, 20L, 12L, 0L, 16L, 14L, 0L, 2L, 3L, 5L, 56L, 18L, 9L, 
4L, 1L, 84L, 2L, 3L, 3L, 14L, 48L, 36L, 3L, 0L, 22L, 5L, 0L, 
9L, 6L, 3L, 3L, 0L, 5L, 29L, 39L, 2L, 15L, 0L, 0L, 0L, 20L, 0L, 
8L, 6L, 10L, 18L, 18L, 49L, 2L, 0L, 1L, 7L, 45L, 0L, 1L, 13L, 
56L, 3L, 0L, 231L, 274L, 0L, 0L, 15L, 60L, 0L, 22L, 28L, 6L, 
8L), language = structure(c(10L, 6L, 8L, 1L, 6L, 10L, 1L, 2L, 
2L, 1L, 4L, 1L, 8L, 6L, 1L, 6L, 1L, 3L, 1L, 10L, 10L, 6L, 1L, 
10L, 5L, 3L, 10L, 10L, 3L, 1L, 6L, 1L, 10L, 2L, 7L, 2L, 3L, 10L, 
1L, 2L, 2L, 6L, 5L, 6L, 3L, 1L, 2L, 2L, 8L, 2L, 10L, 10L, 6L, 
1L, 1L, 9L, 3L, 3L, 10L, 1L, 4L, 4L, 1L, 6L, 1L, 1L, 2L, 3L, 
6L, 1L, 3L, 2L, 7L, 9L, 6L, 10L, 6L, 8L, 1L, 10L, 6L, 3L, 1L, 
9L, 8L, 10L, 10L, 1L, 10L, 8L, 10L, 10L, 4L, 4L, 10L, 10L, 10L, 
10L, 10L, 10L, 8L, 2L, 10L, 10L, 1L, 8L, 10L, 10L, 10L, 6L, 6L, 
1L, 2L, 3L, 10L, 10L, 8L, 6L, 8L, 6L, 2L, 1L, 2L, 2L, 10L, 5L, 
2L, 8L, 6L, 10L, 6L, 8L, 3L, 1L, 7L, 1L, 10L, 6L, 10L, 8L, 10L, 
1L, 1L, 1L, 8L, 6L, 6L, 4L, 8L, 7L, 10L, 10L, 3L, 10L, 1L, 8L, 
9L, 1L, 8L, 10L, 1L, 2L, 1L, 1L, 5L, 6L, 6L, 2L, 10L, 1L, 6L, 
10L, 10L, 10L), .Label = c("1", "2", "3", "4", "5", "6", "7", 
"8", "9", "10"), class = "factor"), bars = c(0L, 0L, 2L, 0L, 
3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 2L, 1L, 0L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 
1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 3L, 3L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 
0L, 3L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 
3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 3L, 0L), stripes = c(3L, 0L, 
0L, 0L, 0L, 2L, 1L, 3L, 3L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 2L, 0L, 
0L, 0L, 5L, 0L, 0L, 0L, 3L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 
0L, 3L, 0L, 0L, 0L, 5L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 3L, 
3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 5L, 3L, 3L, 1L, 9L, 0L, 0L, 
0L, 0L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 2L, 3L, 3L, 0L, 2L, 0L, 0L, 
0L, 0L, 3L, 0L, 5L, 0L, 3L, 2L, 0L, 11L, 2L, 3L, 2L, 3L, 14L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 3L, 0L, 3L, 1L, 0L, 3L, 
3L, 0L, 5L, 3L, 0L, 2L, 0L, 0L, 0L, 3L, 0L, 0L, 2L, 5L, 0L, 0L, 
0L, 3L, 0L, 0L, 3L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 
5L, 0L, 0L, 3L, 0L, 0L, 5L, 5L, 0L, 0L, 0L, 0L, 0L, 3L, 6L, 0L, 
9L, 0L, 13L, 0L, 0L, 0L, 3L, 0L, 0L, 3L, 0L, 0L, 7L), colours = c(5L, 
3L, 3L, 5L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 8L, 
2L, 6L, 4L, 3L, 4L, 6L, 4L, 5L, 3L, 3L, 3L, 3L, 2L, 5L, 6L, 5L, 
3L, 2L, 3L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 4L, 6L, 3L, 3L, 4L, 
2L, 4L, 3L, 3L, 6L, 7L, 2L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 
7L, 2L, 3L, 4L, 5L, 2L, 2L, 6L, 3L, 3L, 2L, 3L, 4L, 3L, 2L, 3L, 
3L, 3L, 2L, 4L, 2L, 4L, 4L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 4L, 
3L, 3L, 3L, 2L, 4L, 2L, 3L, 7L, 2L, 5L, 3L, 3L, 3L, 3L, 3L, 2L, 
3L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 4L, 6L, 2L, 4L, 2L, 3L, 2L, 7L, 
4L, 4L, 2L, 3L, 3L, 2L, 4L, 2L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 
4L, 2L, 2L, 4L, 3L, 4L, 3L, 4L, 2L, 3L, 2L, 2L, 6L, 4L, 5L, 3L, 
3L, 6L, 3L, 2L, 4L, 4L, 7L, 2L, 3L, 4L, 4L, 4L, 5L), red = c(1L, 
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 
1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 
0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 
1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), green = c(1L, 
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 
1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 
1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 
0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L), blue = c(0L, 
0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 
0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 
1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 
1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 
0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L), gold = c(1L, 
1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 
0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 
0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 
1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 
1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L), white = c(1L, 
0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 
1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 
1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L), black = c(1L, 
1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 
0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 
0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L), orange = c(0L, 
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L), mainhue = structure(c(5L, 
7L, 5L, 2L, 4L, 7L, 8L, 2L, 2L, 2L, 7L, 2L, 7L, 5L, 2L, 4L, 2L, 
5L, 7L, 6L, 2L, 5L, 2L, 4L, 7L, 7L, 7L, 7L, 4L, 7L, 4L, 2L, 4L, 
7L, 7L, 4L, 5L, 7L, 2L, 2L, 2L, 8L, 8L, 7L, 2L, 5L, 2L, 4L, 1L, 
2L, 5L, 5L, 8L, 2L, 2L, 8L, 8L, 8L, 5L, 7L, 4L, 1L, 8L, 2L, 4L, 
2L, 2L, 4L, 4L, 5L, 1L, 2L, 2L, 7L, 2L, 7L, 7L, 7L, 8L, 8L, 8L, 
8L, 5L, 8L, 1L, 7L, 7L, 7L, 7L, 7L, 2L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 2L, 5L, 5L, 2L, 7L, 2L, 7L, 4L, 2L, 3L, 7L, 8L, 2L, 2L, 
6L, 5L, 2L, 7L, 7L, 7L, 5L, 7L, 1L, 7L, 7L, 2L, 8L, 7L, 3L, 7L, 
7L, 5L, 5L, 5L, 5L, 8L, 5L, 2L, 6L, 8L, 7L, 4L, 5L, 2L, 5L, 7L, 
7L, 2L, 7L, 7L, 7L, 5L, 7L, 5L, 7L, 7L, 7L, 7L, 2L, 5L, 4L, 7L, 
8L, 8L, 8L, 7L, 7L, 4L, 7L, 7L, 7L, 7L, 5L, 5L, 5L), .Label = c("black", 
"blue", "brown", "gold", "green", "orange", "red", "white"), class = "factor"), 
    circles = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 1L, 0L, 1L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), crosses = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), saltires = c(0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L), quarters = c(0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 
    0L, 0L, 0L, 0L), sunstars = c(1L, 1L, 1L, 0L, 0L, 1L, 0L, 
    0L, 1L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 22L, 
    0L, 0L, 1L, 1L, 14L, 3L, 1L, 0L, 1L, 4L, 1L, 1L, 5L, 0L, 
    4L, 1L, 15L, 0L, 1L, 0L, 0L, 0L, 1L, 10L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 7L, 
    0L, 0L, 0L, 1L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 1L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 1L, 0L, 4L, 1L, 0L, 1L, 1L, 1L, 2L, 0L, 
    6L, 4L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 2L, 5L, 1L, 0L, 4L, 
    0L, 1L, 0L, 2L, 0L, 2L, 0L, 1L, 0L, 5L, 5L, 1L, 0L, 0L, 1L, 
    0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 1L, 0L, 0L, 1L, 0L, 0L, 
    1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 50L, 1L, 0L, 0L, 7L, 1L, 
    5L, 1L, 0L, 0L, 1L), crescent = c(0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L), triangle = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L), icon = c(1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 
    1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
    0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
    0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L), animate = c(0L, 
    1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
    0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L), text = c(0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L), topleft = structure(c(1L, 6L, 4L, 2L, 
    2L, 6L, 7L, 2L, 2L, 7L, 6L, 2L, 7L, 4L, 2L, 1L, 6L, 4L, 7L, 
    5L, 2L, 4L, 7L, 7L, 7L, 6L, 2L, 7L, 4L, 6L, 6L, 7L, 2L, 2L, 
    6L, 3L, 4L, 6L, 7L, 2L, 2L, 7L, 7L, 6L, 7L, 4L, 2L, 3L, 6L, 
    2L, 4L, 4L, 7L, 7L, 7L, 7L, 2L, 2L, 4L, 6L, 1L, 1L, 7L, 2L, 
    6L, 6L, 2L, 6L, 6L, 1L, 1L, 2L, 7L, 6L, 2L, 6L, 4L, 6L, 4L, 
    2L, 4L, 6L, 3L, 7L, 1L, 6L, 1L, 6L, 6L, 6L, 4L, 2L, 2L, 6L, 
    7L, 1L, 2L, 6L, 7L, 2L, 4L, 4L, 2L, 6L, 7L, 6L, 4L, 2L, 2L, 
    6L, 7L, 7L, 2L, 5L, 4L, 2L, 6L, 6L, 6L, 7L, 7L, 6L, 6L, 6L, 
    2L, 7L, 6L, 7L, 2L, 6L, 4L, 4L, 4L, 4L, 6L, 2L, 2L, 5L, 7L, 
    6L, 3L, 4L, 2L, 2L, 6L, 4L, 2L, 6L, 6L, 2L, 4L, 6L, 6L, 7L, 
    7L, 6L, 6L, 7L, 6L, 1L, 7L, 7L, 7L, 2L, 6L, 1L, 3L, 3L, 6L, 
    2L, 2L, 4L, 4L, 4L), .Label = c("black", "blue", "gold", 
    "green", "orange", "red", "white"), class = "factor"), botright = structure(c(5L, 
    7L, 8L, 7L, 7L, 1L, 2L, 2L, 2L, 2L, 7L, 2L, 7L, 5L, 2L, 7L, 
    7L, 5L, 7L, 7L, 2L, 5L, 2L, 4L, 7L, 5L, 7L, 8L, 4L, 7L, 5L, 
    2L, 4L, 7L, 7L, 7L, 5L, 7L, 2L, 2L, 2L, 8L, 7L, 7L, 5L, 5L, 
    2L, 7L, 1L, 2L, 7L, 7L, 8L, 2L, 2L, 8L, 7L, 7L, 2L, 5L, 4L, 
    4L, 7L, 2L, 7L, 7L, 2L, 5L, 5L, 5L, 7L, 2L, 2L, 5L, 2L, 8L, 
    7L, 1L, 6L, 2L, 7L, 5L, 4L, 8L, 5L, 7L, 5L, 2L, 7L, 7L, 2L, 
    7L, 7L, 2L, 5L, 5L, 8L, 7L, 7L, 2L, 5L, 7L, 2L, 7L, 2L, 7L, 
    4L, 2L, 2L, 2L, 8L, 2L, 2L, 5L, 5L, 2L, 1L, 7L, 5L, 5L, 8L, 
    1L, 2L, 7L, 7L, 7L, 7L, 3L, 7L, 5L, 5L, 5L, 7L, 2L, 8L, 5L, 
    2L, 2L, 8L, 1L, 4L, 7L, 2L, 5L, 1L, 5L, 2L, 7L, 1L, 7L, 2L, 
    7L, 5L, 7L, 8L, 7L, 7L, 2L, 1L, 7L, 7L, 8L, 8L, 7L, 7L, 5L, 
    8L, 7L, 7L, 7L, 7L, 5L, 3L, 5L), .Label = c("black", "blue", 
    "brown", "gold", "green", "orange", "red", "white"), class = "factor")), .Names = c("ytrain", 
"landmass", "zone", "area", "population", "language", "bars", 
"stripes", "colours", "red", "green", "blue", "gold", "white", 
"black", "orange", "mainhue", "circles", "crosses", "saltires", 
"quarters", "sunstars", "crescent", "triangle", "icon", "animate", 
"text", "topleft", "botright"), row.names = c(NA, -174L), class = "data.frame")
tdata$language <-  factor(tdata$language)
tdata$ytrain <- factor(tdata$ytrain)

### was: error
model <- ctree(ytrain ~ ., data = tdata, 
    control = ctree_control(testtype = "Univariate", splitstat = "maximum"))

if (require("coin")) {
  ### check against coin (independence_test automatically
  ### removes empty levels)
  p <- info_node(node_party(model))$criterion["p.value",]
  p[is.na(p)] <- 0
  p2 <- sapply(names(p), function(n)
    pvalue(independence_test(ytrain ~ .,
    data = tdata[, c("ytrain", n)], teststat = "quad")))
  stopifnot(max(abs(p - p2)) < sqrt(.Machine$double.eps))

  p <- info_node(node_party(model[2]))$criterion["p.value",]
  p[is.na(p)] <- 0
  p2 <- sapply(names(p), function(n)
    pvalue(independence_test(ytrain ~ .,
    data = tdata[tdata$language != "8", c("ytrain", n)], 
    teststat = "quad")))
  stopifnot(max(abs(p - p2)) < sqrt(.Machine$double.eps))

  p <- info_node(node_party(model[3]))$criterion["p.value",]
  p[is.na(p)] <- 0
  p2 <- sapply(names(p), function(n)
    pvalue(independence_test(ytrain ~ .,
    data = tdata[!(tdata$language %in% c("2", "4", "8")), 
                 c("ytrain", n)], 
    teststat = "quad")))
  stopifnot(max(abs(p - p2)) < sqrt(.Machine$double.eps))
}

### check coersion of constparties to simpleparties
### containing terminal nodes without corresponding observations
## create party
data("WeatherPlay", package = "partykit")
py <- party(
  partynode(1L,
    split = partysplit(1L, index = 1:3),
    kids = list(
      partynode(2L,
        split = partysplit(3L, breaks = 75),
        kids = list(
          partynode(3L, info = "yes"),
          partynode(4L, info = "no"))),
      partynode(5L, 
        split = partysplit(3L, breaks = 20),
        kids = list(
          partynode(6L, info = "no"),
          partynode(7L, info = "yes"))),
      partynode(8L,
        split = partysplit(4L, index = 1:2),
        kids = list(
          partynode(9L, info = "yes"),
          partynode(10L, info = "no"))))),
  WeatherPlay)
names(py) <- LETTERS[nodeids(py)]

pn <- node_party(py)
cp <- party(pn,
  data = WeatherPlay,
  fitted = data.frame(
    "(fitted)" = fitted_node(pn, data = WeatherPlay),
    "(response)" = WeatherPlay$play,
    check.names = FALSE),
  terms = terms(play ~ ., data = WeatherPlay),
)
print(cp)
cp <- as.constparty(cp)

nd <- data.frame(outlook = factor("overcast", levels = levels(WeatherPlay$outlook)), 
                 humidity = 10, temperature = 10, windy = "yes")
try(predict(cp, type = "node", newdata = nd))
try(predict(cp, type = "response", newdata = nd))
as.simpleparty(cp)
print(cp)

### scores
y <- gl(3, 10, ordered = TRUE)
x <- rnorm(length(y))  
x <- ordered(cut(x, 3))
d <- data.frame(y = y, x = x)

### partykit with scores
ct11 <- partykit::ctree(y ~ x, data = d) 
ct12 <- partykit::ctree(y ~ x, data = d, 
                        scores = list(y = c(1, 4, 5)))
ct13 <- partykit::ctree(y ~ x, data = d, 
                        scores = list(y = c(1, 4, 5), x = c(1, 5, 6)))

### party with scores
ct21 <- party::ctree(y ~ x, data = d) 
ct22 <- party::ctree(y ~ x, data = d, 
                     scores = list(y = c(1, 4, 5)))
ct23 <- party::ctree(y ~ x, data = d, 
                     scores = list(y = c(1, 4, 5), x = c(1, 5, 6)))

stopifnot(all.equal(ct11$node$info$p.value, 
          1 - ct21@tree$criterion$criterion, check.attr = FALSE))
stopifnot(all.equal(ct12$node$info$p.value, 
          1 - ct22@tree$criterion$criterion, check.attr = FALSE))
stopifnot(all.equal(ct13$node$info$p.value, 
          1 - ct23@tree$criterion$criterion, check.attr = FALSE))

### ytrafo
y <- runif(100, max = 3)
x <- rnorm(length(y))
d <- data.frame(y = y, x = x)

### partykit with scores
ct11 <- partykit::ctree(y ~ x, data = d)
ct12 <- partykit::ctree(y ~ x, data = d,
                        ytrafo = list(y = sqrt))

### party with scores
ct21 <- party::ctree(y ~ x, data = d)
f <- function(data) coin::trafo(data, numeric_trafo = sqrt)
ct22 <- party::ctree(y ~ x, data = d,
                     ytrafo = f)

stopifnot(all.equal(ct11$node$info$p.value,
          1 - ct21@tree$criterion$criterion, check.attr = FALSE))
stopifnot(all.equal(ct12$node$info$p.value,
          1 - ct22@tree$criterion$criterion, check.attr = FALSE))


### spotted by Peter Philip Stephensen (DREAM) <PSP@dreammodel.dk>
### splits x >= max(x) where possible in partykit::ctree
nAge <- 30
d <- data.frame(Age=rep(1:nAge,2),y=c(rep(1,nAge),rep(0,nAge)), 
                n = rep(0,2*nAge))
ntot <- 100
alpha <- .5
d[d$y==1,]$n = floor(ntot * alpha * d[d$y==1,]$Age / nAge)
d[d$y==0,]$n = ntot - d[d$y==1,]$n
d$n <- as.integer(d$n)
ctrl <- partykit::ctree_control(maxdepth=3, minbucket = min(d$n) + 1)
tree <- partykit::ctree(y ~ Age, weights=n, data=d, control=ctrl)
## IGNORE_RDIFF_BEGIN
tree
## IGNORE_RDIFF_END

(w1 <- predict(tree, type = "node"))

(ct <- ctree(dist + I(dist^2) ~ speed, data = cars))
predict(ct)

### nodeapply was not the same for permutations of ids
### spotted by Heidi Seibold
airq <- subset(airquality, !is.na(Ozone))
airct <- ctree(Ozone ~ ., data = airq)
n1 <- nodeapply(airct, ids = c(3, 5, 6), function(x) x$info$nobs)
n2 <- nodeapply(airct, ids = c(6, 3, 5), function(x) x$info$nobs)
stopifnot(all.equal(n1[names(n2)], n2))

### pruning got "fitted" wrong, spotted by Jason Parker
data("Titanic")
titan <- as.data.frame(Titanic)
(tree <- ctree(Survived ~ Class + Sex + Age, data = titan, weights = Freq))
### prune off nodes 5-12 and check if the other nodes are not affected
nodeprune(tree, 4)

### this gave a warning "ME is not a factor"
if (require("TH.data")) {
    data("mammoexp", package = "TH.data")
    a <- cforest(ME ~ PB + SYMPT, data = mammoexp, ntree = 5)
    print(predict(a, newdata=mammoexp[1:3,]))
}

### pruning didn't work properly
mt <- lmtree(dist ~ speed, data = cars)
mt2 <- nodeprune(mt, 2)
stopifnot(all(mt2$fitted[["(fitted)"]] %in% c(2, 3)))

###
a <- rep('N',87)
a[77] <- 'Y'
b <- rep(FALSE, 87)
b[c(7,10,11,33,56,77)] <- TRUE
d <- rep(1,87)
d[c(29,38,40,42,65,77)] <- 0
dfb <- data.frame(a = as.factor(a), b = as.factor(b), d = as.factor(d))
tr <- ctree(a ~ ., data = dfb, control = ctree_control(minsplit = 10,minbucket = 5,
                                                       maxsurrogate = 2, alpha = 0.05))
tNodes <- node_party(tr)
### this creates a tie on purpose and "d" should be selected
### this check fails on M1mac
nodeInfo <- info_node(tNodes)
nodeInfo$criterion
#stopifnot(names(nodeInfo$p.value) == "d")
#stopifnot(split_node(tNodes)$varid == 3)

### reported by John Ogawa, 2020-12-11
class(dfb$a) <- c("Hansi", "factor")
tr2 <- ctree(a ~ ., data = dfb, control = ctree_control(minsplit = 10,minbucket = 5,
                                                       maxsurrogate = 2, alpha = 0.05))
stopifnot(isTRUE(all.equal(tr, tr2, check.attributes = FALSE)))