Я пытаюсь проанализировать вероятность обнаружения птицы, используя пакет «Дистанция» в R. Я разделил обнаружение по классу размера птицы: маленький и средний. Большой по весу, потому что обнаружение отдельных видов было низким. Ниже приведен класс маленьких птиц на 26 различных участках, которые варьируются в 4 различных типах среды обитания: сельское хозяйство, лес, кустарник, луг. У меня есть более высокие, но приличные значения AI C с некоторыми из моих выходов (Пример 1), а более низкие формулы значений AI C дают NA и нулевые выходы (Пример 2). Попытка устранить неполадки и выяснить, что не так. Тип овощей = категория, среднегодовое количество осадков = непрерывно. Я также включил свой вывод без каких-либо переменных (Пример 3). Разве нельзя использовать непрерывную переменную как фактор? Потому что выходы имеют низкий AIC с NA и нулями (Пример 4). Я ценю любые рекомендации или рекомендации. Заранее спасибо!
> head(det_small3)
Region.Label Sample.Label Effort distance size Species vegheight canopy weight meanannualrainfall elevation ground avgtempf vegtype
1 agriculture 5 1 35 15 small 0.35 0 17.9 409.9 31.09 80 78 agriculture
2 agriculture 5 1 40 6 small 0.35 0 17.9 409.9 31.09 80 78 agriculture
3 agriculture 5 1 45 4 small 0.35 0 17.9 409.9 31.09 80 78 agriculture
4 agriculture 5 1 40 5 small 0.35 0 17.9 409.9 31.09 80 78 agriculture
5 agriculture 5 1 45 10 small 0.35 0 17.9 409.9 31.09 80 78 agriculture
6 agriculture 5 1 30 2 small 0.35 0 17.9 409.9 31.09 80 78 agriculture
> tail(det_small3)
Region.Label Sample.Label Effort distance size Species vegheight canopy weight meanannualrainfall elevation ground avgtempf vegtype
237 grassland 26 1 60 30 small 0.7 0 17.9 1249.7 341 70 73.0 grassland
238 grassland 26 1 40 3 small 0.7 0 17.9 1249.7 341 70 73.0 grassland
239 grassland 26 1 70 3 small 0.7 0 17.9 1249.7 341 70 73.0 grassland
240 grassland 26 1 80 2 small 0.7 0 17.9 1249.7 341 70 73.0 grassland
241 grassland 26 1 30 3 small 0.7 0 17.9 1249.7 341 70 73.0 grassland
242 shrubland 15 1 NA NA small 0.5 0 na 1177.5 2673 50 52.9 shrubland
dput(det_small3)structure(list(Region.Label = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 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, 3L), .Label = c("agriculture",
"forest", "shrubland", "grassland"), class = "factor"), Area = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Sample.Label = structure(c(5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 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, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 22L, 22L, 22L, 22L, 22L, 22L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 14L, 14L, 14L, 14L, 17L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 15L), .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26"), class = "factor"), Effort = c(1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1), distance = c(35, 40, 45, 40, 45, 30, 45, 75,
55, 65, 80, 50, 50, 10, 30, 20, 30, 20, 30, 40, 150, 30, 40,
10, 10, 15, 20, 20, 30, 50, 20, 40, 25, 30, 30, 50, 50, 20, 20,
20, 40, 5, 20, 20, 20, 20, 30, 5, 10, 30, 20, 10, 30, 20, 10,
30, 15, 20, 25, 10, 20, 40, 15, 20, 30, 40, 20, 30, 30, 30, 30,
30, 100, 30, 30, 30, 10, 40, 30, 60, 90, 40, 80, 40, 30, 45,
15, 20, 70, 80, 60, 30, 100, 80, 50, 40, 60, 30, 30, 40, 60,
70, 55, 35, 40, 50, 45, 40, 45, 35, 35, 30, 40, 10, 6, 10, 5,
15, 20, 30, 15, 40, 20, 25, 20, 30, 40, 20, 25, 15, 30, 25, 30,
30, 25, 20, 20, 25, 50, 40, 50, 30, 50, 50, 50, 30, 60, 60, 80,
60, 80, 30, 100, 60, 25, 50, 30, 20, 30, 40, 20, 30, 20, 30,
50, 40, 30, 40, 30, 20, 30, 60, 50, 20, 50, 55, 35, 55, 15, 30,
10, 50, 60, 80, 40, 35, 35, 50, 40, 30, 25, 30, 50, 50, 40, 30,
50, 50, 30, 200, 200, 100, 100, 100, 150, 100, 30, 40, 30, 20,
70, 40, 40, 60, 60, 30, 50, 50, 30, 50, 60, 80, 90, 30, 60, 80,
40, 100, 50, 40, 35, 20, 60, 90, 40, 50, 60, 40, 70, 80, 30,
NA), size = c(15, 6, 4, 5, 10, 2, 2, 4, 2, 10, 3, 3, 3, 3, 11,
2, 2, 1, 2, 6, 4, 2, 1, 6, 2, 8, 2, 2, 1, 3, 2, 3, 2, 5, 2, 6,
6, 14, 6, 2, 4, 2, 2, 2, 5, 4, 5, 2, 4, 12, 1, 8, 3, 3, 12, 4,
2, 6, 6, 6, 2, 7, 2, 8, 10, 1, 14, 2, 3, 1, 2, 2, 3, 2, 2, 4,
3, 4, 2, 8, 1, 4, 1, 2, 8, 15, 8, 5, 9, 8, 2, 2, 3, 2, 8, 2,
1, 3, 2, 2, 2, 5, 4, 3, 7, 6, 3, 1, 6, 4, 1, 3, 1, 5, 4, 3, 4,
3, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 5, 2, 1, 2, 3, 3, 9,
8, 5, 2, 2, 2, 2, 2, 3, 4, 6, 4, 2, 2, 3, 2, 2, 6, 1, 1, 1, 1,
1, 4, 2, 6, 1, 1, 4, 4, 2, 3, 3, 3, 8, 2, 3, 4, 2, 4, 1, 1, 2,
1, 1, 1, 1, 2, 10, 4, 4, 2, 10, 3, 3, 2, 5, 6, 2, 2, 2, 1, 5,
2, 1, 2, 1, 1, 1, 6, 4, 4, 4, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 8,
2, 1, 2, 2, 1, 2, 2, 3, 6, 1, 8, 3, 12, 10, 15, 30, 3, 3, 2,
3, NA), Species = structure(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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("medium.large",
"small", "raptor"), class = "factor"), vegheight = c(0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25, 0.25, 0.725, 0.725, 0.725, 0.725, 0.725,
0.725, 0.725, 0.725, 0.725, 0.725, 0.45, 0.45, 0.45, 0.45, 0.45,
0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,
0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 2.93, 2.93, 2.93, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 5, 5, 5, 5, 5, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 6, 6,
6, 0.5, 0.5, 0.5, 0.5, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3,
8.3, 8.3, 8.3, 8.3, 8.3, 0.3, 0.3, 0.43, 0.43, 0.43, 0.43, 0.43,
0.55, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55,
0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.11, 0.3, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.7, 0.7, 0.7,
0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.5), canopy = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 85, 85, 90,
90, 90, 90, 90, 90, 90, 90, 90, 90, 60, 60, 60, 60, 60, 75, 75,
75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75,
75, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 65, 65, 65, 0,
0, 0, 0, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weight = 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, 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, 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), .Label = c("17.9",
"na"), class = "factor"), meanannualrainfall = c(409.9, 409.9,
409.9, 409.9, 409.9, 409.9, 409.9, 409.9, 409.9, 409.9, 409.9,
467.5, 467.5, 467.5, 467.5, 467.5, 467.5, 467.5, 467.5, 467.5,
467.5, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8,
306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8,
306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 306.8,
306.8, 306.8, 306.8, 306.8, 306.8, 306.8, 320.7, 320.7, 320.7,
320.7, 320.7, 320.7, 320.7, 320.7, 320.7, 320.7, 320.7, 320.7,
320.7, 320.7, 320.7, 409.9, 409.9, 409.9, 409.9, 409.9, 409.9,
409.9, 409.9, 409.9, 409.9, 376.6, 376.6, 376.6, 376.6, 376.6,
376.6, 376.6, 376.6, 352.4, 352.4, 352.4, 352.4, 352.4, 352.4,
352.4, 352.4, 352.4, 352.4, 352.4, 352.4, 352.4, 352.4, 2044.1,
2044.1, 2044.1, 918.3, 918.3, 918.3, 918.3, 918.3, 918.3, 918.3,
918.3, 918.3, 918.3, 2004.5, 2004.5, 2004.5, 2004.5, 2004.5,
1842.8, 1842.8, 1842.8, 1842.8, 1842.8, 1842.8, 1842.8, 1842.8,
1842.8, 1842.8, 1842.8, 1842.8, 1842.8, 1842.8, 1842.8, 1842.8,
1842.8, 1842.8, 1842.8, 1842.8, 808, 808, 808, 808, 808, 808,
808, 808, 808, 808, 808, 808, 808, 3550.6, 3550.6, 3550.6, 797.7,
797.7, 797.7, 797.7, 1352.1, 1352.1, 1352.1, 1352.1, 1352.1,
1352.1, 1352.1, 1352.1, 1352.1, 1352.1, 1352.1, 1352.1, 1352.1,
1008, 1008, 1008, 1008, 1008, 1008, 1008, 1012.9, 1012.9, 1012.9,
1012.9, 1012.9, 1012.9, 634.4, 634.4, 634.4, 634.4, 634.4, 1371,
1371, 1371, 734.2, 734.2, 734.2, 734.2, 734.2, 734.2, 734.2,
734.2, 734.2, 734.2, 734.2, 734.2, 734.2, 734.2, 525.8, 525.8,
525.8, 525.8, 718.9, 677.8, 677.8, 677.8, 677.8, 677.8, 677.8,
677.8, 677.8, 677.8, 677.8, 677.8, 677.8, 677.8, 677.8, 677.8,
677.8, 677.8, 677.8, 677.8, 677.8, 677.8, 1249.7, 1249.7, 1249.7,
1249.7, 1249.7, 1249.7, 1249.7, 1249.7, 1249.7, 1177.5), elevation = c(31.09,
31.09, 31.09, 31.09, 31.09, 31.09, 31.09, 31.09, 31.09, 31.09,
31.09, 335.28, 335.28, 335.28, 335.28, 335.28, 335.28, 335.28,
335.28, 335.28, 335.28, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 78.02, 78.02, 78.02, 78.02, 78.02, 78.02,
78.02, 78.02, 78.02, 78.02, 78.02, 78.02, 78.02, 78.02, 78.02,
52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 182, 182, 182, 182, 182,
182, 182, 182, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44,
44, 44, 27.13, 27.13, 27.13, 1981.2, 1981.2, 1981.2, 1981.2,
1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 746.76, 746.76,
746.76, 746.76, 746.76, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2,
1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2,
1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1981.2, 1433,
1433, 1433, 1433, 1433, 1433, 1433, 1433, 1433, 1433, 1433, 1433,
1433, 73, 73, 73, 1343, 1343, 1343, 1343, 396, 396, 396, 396,
396, 396, 396, 396, 396, 396, 396, 396, 396, 1828.8, 1828.8,
2194.56, 2194.56, 2194.56, 2194.56, 2194.56, 2569, 2569, 2569,
2569, 2569, 2569, 502.92, 502.92, 502.92, 502.92, 502.92, 944.88,
944.88, 944.88, 667.5, 667.5, 667.5, 667.5, 667.5, 667.5, 667.5,
667.5, 667.5, 667.5, 667.5, 667.5, 667.5, 667.5, 359.05, 359.05,
359.05, 359.05, 986, 690, 690, 690, 690, 690, 690, 690, 690,
690, 690, 690, 690, 690, 690, 690, 690, 690, 690, 690, 690, 690,
341, 341, 341, 341, 341, 341, 341, 341, 341, 2673), ground = c(80,
80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75,
75, 75, 75, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 75, 75, 75,
75, 75, 75, 75, 75, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95,
90, 90, 90, 90, 90, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80,
80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80,
80, 80, 80, 80, 80, 80, 95, 95, 95, 73, 73, 73, 73, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 80, 80,
60, 60, 60, 60, 60, 40, 40, 40, 40, 40, 40, 85, 85, 85, 85, 85,
95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95,
95, 35, 35, 35, 35, 75, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 70, 70, 70, 70, 70,
70, 70, 70, 70, 50), avgtempf = c(78, 78, 78, 78, 78, 78, 78,
78, 78, 78, 78, 77.5, 77.5, 77.5, 77.5, 77.5, 77.5, 77.5, 77.5,
77.5, 77.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5,
80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5,
80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5, 80.5,
80.5, 79.5, 79.5, 79.5, 79.5, 79.5, 79.5, 79.5, 79.5, 79.5, 79.5,
79.5, 79.5, 79.5, 79.5, 79.5, 80.6, 80.6, 80.6, 80.6, 80.6, 80.6,
80.6, 80.6, 80.6, 80.6, 78.4, 78.4, 78.4, 78.4, 78.4, 78.4, 78.4,
78.4, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77,
76, 76, 76, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 68.5, 68.5,
68.5, 68.5, 68.5, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3,
54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3, 54.3,
54.3, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5, 65.5,
65.5, 65.5, 65.5, 74.5, 74.5, 74.5, 65, 65, 65, 65, 78, 78, 78,
78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 65, 65, 65, 65, 65, 65,
65, 50, 50, 50, 50, 50, 50, 69, 69, 69, 69, 69, 66, 66, 66, 65.8,
65.8, 65.8, 65.8, 65.8, 65.8, 65.8, 65.8, 65.8, 65.8, 65.8, 65.8,
65.8, 65.8, 74.3, 74.3, 74.3, 74.3, 67.4, 75.5, 75.5, 75.5, 75.5,
75.5, 75.5, 75.5, 75.5, 75.5, 75.5, 75.5, 75.5, 75.5, 75.5, 75.5,
75.5, 75.5, 75.5, 75.5, 75.5, 75.5, 73, 73, 73, 73, 73, 73, 73,
73, 73, 52.9), vegtype = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 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, 3L), .Label = c("agriculture",
"forest", "shrubland", "grassland"), class = "factor")), class = "data.frame", row.names = c(NA,
-242L))
Пример 1:
detect_hrvegtype $ dht $ индивидуальный $ резюме
Region Area CoveredArea Effort n ER se.ER cv.ER mean.size se.mean
1 agriculture 198470.1 198470.1 7 428 61.14286 13.698155 0.2240352 4.412371 0.3501204
2 forest 198470.1 226823.0 8 206 25.75000 5.936419 0.2305405 2.942857 0.2162026
3 shrubland 198470.1 113411.5 4 31 7.75000 2.954516 0.3812279 2.384615 0.5608883
4 grassland 198470.1 198470.1 7 216 30.85714 10.479978 0.3396289 4.408163 0.6912465
5 Total 793880.5 737174.7 26 881 33.88462 5.961241 0.1759276 3.847162 0.2262063
Пример 2:
detect_hrcosmeanannualrainfall $ dht $ индивидуумов $ итоговый NULL
сводка (detect_hrcosmeanannualrainfall)
Summary for distance analysis
Number of observations : 229
Distance range : 0 - 95
Model : Hazard-rate key function
AIC : 6
Detection function parameters
Scale coefficient(s):
estimate se
(Intercept) 3.108856 NA
meanannualrainfall -1.000038 NA
Shape coefficient(s):
estimate se
(Intercept) 0.8765737 NA
Estimate SE CV
Average p 0 NA NA
N in covered region Inf NA NA
Пример 3:
резюме (detect_hrcos)
Summary for distance analysis
Number of observations : 229
Distance range : 0 - 95
Model : Hazard-rate key function
AIC : 1954.596
Detection function parameters
Scale coefficient(s):
estimate se
(Intercept) 3.645371 0.06451175
Shape coefficient(s):
estimate se
(Intercept) 1.461591 0.1104191
Estimate SE CV
Average p 0.2519536 0.02109612 0.0837302
N in covered region 908.8975475 92.14143419 0.1013771
Summary for clusters
Summary statistics:
Region Area CoveredArea Effort n k ER se.ER cv.ER
1 agriculture 198470.1 198470.1 7 97 7 13.857143 3.165503 0.2284383
2 forest 198470.1 226823.0 8 70 8 8.750000 2.234071 0.2553224
3 shrubland 198470.1 113411.5 4 13 4 3.250000 1.376893 0.4236593
4 grassland 198470.1 198470.1 7 49 7 7.000000 2.380476 0.3400680
5 Total 793880.5 737174.7 26 229 26 8.807692 1.406599 0.1597012
Density:
Label Estimate se cv lcl ucl df
1 agriculture 0.0019397960 0.0004719521 0.2432999 0.0011118509 0.003384274 7.716770
2 forest 0.0012248712 0.0003291242 0.2687010 0.0006710625 0.002235722 8.583516
3 shrubland 0.0004549522 0.0001964729 0.4318541 0.0001286891 0.001608384 3.238871
4 grassland 0.0009798970 0.0003431836 0.3502242 0.0004357316 0.002203646 6.748863
5 Total 0.0011498791 0.0001921382 0.1670942 0.0008205686 0.001611348 33.532371
Summary for individuals
Summary statistics:
Region Area CoveredArea Effort n ER se.ER cv.ER mean.size se.mean
1 agriculture 198470.1 198470.1 7 428 61.14286 13.698155 0.2240352 4.412371 0.3501204
2 forest 198470.1 226823.0 8 206 25.75000 5.936419 0.2305405 2.942857 0.2162026
3 shrubland 198470.1 113411.5 4 31 7.75000 2.954516 0.3812279 2.384615 0.5608883
4 grassland 198470.1 198470.1 7 216 30.85714 10.479978 0.3396289 4.408163 0.6912465
5 Total 793880.5 737174.7 26 881 33.88462 5.961241 0.1759276 3.847162 0.2262063
Density:
Label Estimate se cv lcl ucl df
1 agriculture 0.008559100 0.0020470844 0.2391705 0.0049557369 0.014782502 7.789194
2 forest 0.003604621 0.0008841224 0.2452747 0.0020857164 0.006229654 8.963693
3 shrubland 0.001084886 0.0004234468 0.3903146 0.0003470734 0.003391149 3.296311
4 grassland 0.004319546 0.0015109678 0.3497979 0.0019226637 0.009704492 6.750856
5 Total 0.004392038 0.0007439172 0.1693786 0.0031064619 0.006209636 25.098525
Expected cluster size
Region Expected.S se.Expected.S cv.Expected.S
1 agriculture 4.412371 0.3659490 0.08293704
2 forest 2.942857 0.2285997 0.07767953
3 shrubland 2.384615 0.8192792 0.34356868
4 grassland 4.408163 1.1686187 0.26510331
5 Total 3.819565 0.3229394 0.08454874
Пример 4:
detect_hrcoselevation<- ds(det_small3, truncation = 95, transect = "point", key = "hr", formula =
~elevation) #AIC=-382
detect_hrcosmeanannualrainfall<- ds(det_small3, truncation = 95, transect = "point", key = "hr", formula = ~meanannualrainfall) #AIC=6
detect_hrcosmeanannualrainfallvegheight<- ds(det_small3, truncation = 95, transect = "point", key = "hr", formula = ~meanannualrainfall+vegheight) #AIC=8