tests/gen_data.R
In SVA-SE/freedom: Demonstration of Disease Freedom (DDF)
library(freedom)
df1_expected <- data.frame(
ppn = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 8L, 8L,
9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 15L, 15L,
17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L,
22L, 23L, 23L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L,
29L, 29L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L,
34L, 35L, 35L, 36L, 36L, 37L, 37L, 38L, 38L, 39L, 39L,
40L, 40L, 41L, 41L, 42L, 42L, 43L, 43L, 44L, 44L, 45L,
45L, 46L, 46L, 48L, 48L, 49L, 49L, 50L, 50L, 7L, 7L, 14L,
14L, 16L, 16L, 24L, 24L, 47L, 47L),
herd_urg = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("1", "2"),
class = "factor"),
animal_urg = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L),
N_animal_urg = c(59L, 426L, 55L, 474L, 56L, 472L, 46L, 463L, 49L,
416L, 53L, 457L, 61L, 451L, 57L, 436L, 52L, 430L,
48L, 438L, 52L, 441L, 61L, 437L, 46L, 475L, 61L,
452L, 46L, 474L, 47L, 470L, 53L, 448L, 54L, 401L,
45L, 466L, 57L, 467L, 45L, 444L, 62L, 432L, 55L,
442L, 43L, 442L, 55L, 414L, 52L, 470L, 43L, 447L,
39L, 485L, 52L, 465L, 73L, 423L, 44L, 445L, 48L,
464L, 42L, 442L, 43L, 422L, 53L, 464L, 60L, 437L,
55L, 450L, 54L, 437L, 42L, 465L, 41L, 433L, 48L,
457L, 53L, 460L, 49L, 463L, 48L, 448L, 53L, 446L,
58L, 458L, 51L, 448L, 58L, 460L, 39L, 445L, 54L,
442L),
n_animal_urg = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
61L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 61L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 57L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 42L, 7L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 54L, 9L, 0L, 0L, 41L, 6L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 53L, 10L, 58L, 12L, 51L, 5L, 58L,
2L, 39L, 10L, 37L, 2L))
df1_observed <- sample_data(nherds = 50,
mean_herd_size = 500,
n_herd_urg = 2,
herd_dist = c(0.8, 0.2),
herd_samp_frac = 0.15,
herd_samp_dist = c(0.1, 0.9),
n_animal_urg = 2,
animal_dist = c(0.1, 0.9),
animal_samp_frac = 0.15,
animal_samp_dist = c(0.9, 0.1),
seed = 1)
stopifnot(identical(df1_observed, df1_expected))
df2_expected <- data.frame(
ppn = c(2L, 3L, 4L, 6L, 8L, 10L, 1L, 5L, 7L, 9L),
herd_urg = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
.Label = c("1", "2"),
class = "factor"),
animal_urg = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
N_animal_urg =
c(478L, 474L, 502L, 494L, 509L, 491L, 479L, 484L, 483L, 521L),
n_animal_urg = c(0L, 0L, 0L, 0L, 0L, 0L, 62L, 87L, 111L, 93L))
df2_observed <- sample_data(nherds = 10,
mean_herd_size = 500,
n_herd_urg = 2,
herd_dist = c(0.8, 0.2),
herd_samp_frac = 0.15,
herd_samp_dist = c(0.1, 0.9),
n_animal_urg = 1,
animal_dist = c(1),
animal_samp_frac = 0.15,
animal_samp_dist = c(1),
seed = 2)
stopifnot(identical(df2_observed, df2_expected))
df3_expected <- data.frame(
ppn = c(3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 6L, 6L, 6L, 6L),
herd_urg = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L),
.Label = c("1", "2", "3", "4"),
class = "factor"),
animal_urg = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L),
N_animal_urg = c(31L, 27L, 47L, 423L, 23L, 32L, 45L, 409L, 25L,
24L, 62L, 374L, 31L, 16L, 47L, 435L, 25L, 24L,
48L, 368L, 27L, 29L, 51L, 409L, 30L, 29L, 64L,
389L, 32L, 22L, 47L, 392L, 26L, 28L, 52L, 376L,
24L, 29L, 58L, 399L),
n_animal_urg = c(31L, 3L, 6L, 8L, 23L, 5L, 5L, 5L, 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))
df3_observed <- sample_data(nherds = 10,
mean_herd_size = 500,
n_herd_urg = 4,
herd_dist = c(0.1, 0.2, 0.6, 0.1),
herd_samp_frac = 1,
herd_samp_dist = c(0.1, 0.8, 0.05, 0.05),
n_animal_urg = 4,
animal_dist = c(0.05, 0.05, 0.1, 0.8),
animal_samp_frac = 0.15,
animal_samp_dist = c(0.8, 0.05, 0.05, 0.1),
seed = 1)
stopifnot(identical(df3_observed, df3_expected))
SVA-SE/freedom documentation built on Feb. 1, 2023, 5:50 p.m.
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library(freedom)
df1_expected <- data.frame(
ppn = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 8L, 8L,
9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 15L, 15L,
17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L,
22L, 23L, 23L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L,
29L, 29L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L,
34L, 35L, 35L, 36L, 36L, 37L, 37L, 38L, 38L, 39L, 39L,
40L, 40L, 41L, 41L, 42L, 42L, 43L, 43L, 44L, 44L, 45L,
45L, 46L, 46L, 48L, 48L, 49L, 49L, 50L, 50L, 7L, 7L, 14L,
14L, 16L, 16L, 24L, 24L, 47L, 47L),
herd_urg = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("1", "2"),
class = "factor"),
animal_urg = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L),
N_animal_urg = c(59L, 426L, 55L, 474L, 56L, 472L, 46L, 463L, 49L,
416L, 53L, 457L, 61L, 451L, 57L, 436L, 52L, 430L,
48L, 438L, 52L, 441L, 61L, 437L, 46L, 475L, 61L,
452L, 46L, 474L, 47L, 470L, 53L, 448L, 54L, 401L,
45L, 466L, 57L, 467L, 45L, 444L, 62L, 432L, 55L,
442L, 43L, 442L, 55L, 414L, 52L, 470L, 43L, 447L,
39L, 485L, 52L, 465L, 73L, 423L, 44L, 445L, 48L,
464L, 42L, 442L, 43L, 422L, 53L, 464L, 60L, 437L,
55L, 450L, 54L, 437L, 42L, 465L, 41L, 433L, 48L,
457L, 53L, 460L, 49L, 463L, 48L, 448L, 53L, 446L,
58L, 458L, 51L, 448L, 58L, 460L, 39L, 445L, 54L,
442L),
n_animal_urg = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
61L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 61L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 57L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 42L, 7L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 54L, 9L, 0L, 0L, 41L, 6L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 53L, 10L, 58L, 12L, 51L, 5L, 58L,
2L, 39L, 10L, 37L, 2L))
df1_observed <- sample_data(nherds = 50,
mean_herd_size = 500,
n_herd_urg = 2,
herd_dist = c(0.8, 0.2),
herd_samp_frac = 0.15,
herd_samp_dist = c(0.1, 0.9),
n_animal_urg = 2,
animal_dist = c(0.1, 0.9),
animal_samp_frac = 0.15,
animal_samp_dist = c(0.9, 0.1),
seed = 1)
stopifnot(identical(df1_observed, df1_expected))
df2_expected <- data.frame(
ppn = c(2L, 3L, 4L, 6L, 8L, 10L, 1L, 5L, 7L, 9L),
herd_urg = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
.Label = c("1", "2"),
class = "factor"),
animal_urg = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
N_animal_urg =
c(478L, 474L, 502L, 494L, 509L, 491L, 479L, 484L, 483L, 521L),
n_animal_urg = c(0L, 0L, 0L, 0L, 0L, 0L, 62L, 87L, 111L, 93L))
df2_observed <- sample_data(nherds = 10,
mean_herd_size = 500,
n_herd_urg = 2,
herd_dist = c(0.8, 0.2),
herd_samp_frac = 0.15,
herd_samp_dist = c(0.1, 0.9),
n_animal_urg = 1,
animal_dist = c(1),
animal_samp_frac = 0.15,
animal_samp_dist = c(1),
seed = 2)
stopifnot(identical(df2_observed, df2_expected))
df3_expected <- data.frame(
ppn = c(3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 6L, 6L, 6L, 6L),
herd_urg = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L),
.Label = c("1", "2", "3", "4"),
class = "factor"),
animal_urg = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L,
4L),
N_animal_urg = c(31L, 27L, 47L, 423L, 23L, 32L, 45L, 409L, 25L,
24L, 62L, 374L, 31L, 16L, 47L, 435L, 25L, 24L,
48L, 368L, 27L, 29L, 51L, 409L, 30L, 29L, 64L,
389L, 32L, 22L, 47L, 392L, 26L, 28L, 52L, 376L,
24L, 29L, 58L, 399L),
n_animal_urg = c(31L, 3L, 6L, 8L, 23L, 5L, 5L, 5L, 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))
df3_observed <- sample_data(nherds = 10,
mean_herd_size = 500,
n_herd_urg = 4,
herd_dist = c(0.1, 0.2, 0.6, 0.1),
herd_samp_frac = 1,
herd_samp_dist = c(0.1, 0.8, 0.05, 0.05),
n_animal_urg = 4,
animal_dist = c(0.05, 0.05, 0.1, 0.8),
animal_samp_frac = 0.15,
animal_samp_dist = c(0.8, 0.05, 0.05, 0.1),
seed = 1)
stopifnot(identical(df3_observed, df3_expected))
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