结合 facet_grid (ggplot2) 和 denscomp (fitdistrplus)
迈克尔·拜尔斯
首先，我是R新手。我正在尝试将密度图应用于我的数据中的各个组。使用 fitdistrplus，我为我的所有数据创建了一个单一的分布密度图。
plot(my_data, pch=20)
plotdist(my_data$Capture_Rate, histo = TRUE, demp = TRUE)
fit_w <- fitdist(my_data$Capture_Rate, "weibull")
fit_g <- fitdist(my_data$Capture_Rate, "gamma")
fit_ln <- fitdist(my_data$Capture_Rate, "lnorm")
par(mfrow=c(2,2))
plot.legend <- c("Weibull", "lognormal", "gamma")
denscomp(list(fit_w, fit_ln, fit_g), legendtext = plot.legend)
在 ggplot 中使用 facet_grid，我为我的数据的每个分组创建了一个直方图网格。
df_data <- data.frame(my_data)
cdat <- ddply(df_data, c("sYear", "Season"), summarise, Capture_Rate.mean=mean(Capture_Rate))
ggplot(df_data, aes(x=Capture_Rate, fill=sYear))+
geom_histogram(binwidth = .025,
alpha = .5,
position = "identity")+
#geom_density(alpha=.2, fill="#FF6666")+
geom_vline(data=cdat, aes(xintercept=Capture_Rate.mean),
color="red", linetype="dashed", size=1)+
facet_grid(Season ~ sYear)
我正在寻找的是将两个结果组合起来，在那里我得到分组网格中每个直方图的密度图。感谢您的帮助。
样本数据：
a <- dput(my_data)
structure(list(Schedule_Name = 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, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Actuals ", class = "factor"),
Sub_Fleet = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = "38K", class = "factor"), sDate = structure(c(17664,
17665, 17666, 17667, 17668, 17669, 17670, 17672, 17674, 17675,
17676, 17677, 17678, 17679, 17680, 17681, 17682, 17683, 17684,
17685, 17686, 17687, 17688, 17689, 17690, 17691, 17692, 17693,
17694, 17696, 17697, 17698, 17699, 17700, 17701, 17702, 17703,
17704, 17705, 17706, 17707, 17708, 17710, 17711, 17712, 17713,
17714, 17715, 17716, 17717, 17718, 17719, 17720, 17721, 17722,
17723, 17724, 17725, 17728, 17729, 17730, 17731, 17732, 17733,
17734, 17735, 17736, 17737, 17738, 17739, 17740, 17741, 17742,
17743, 17744, 17745, 17746, 17747, 17748, 17749, 17750, 17751,
17753, 17754, 17755, 17758, 17759, 17761, 17762, 17763, 17764,
17765, 17766, 17767, 17768, 17769, 17770, 17771, 17772, 17773,
17774, 17775, 17776, 17777, 17778, 17779, 17781, 17782, 17783,
17784, 17785, 17786, 17787, 17788, 17789, 17790, 17791, 17792,
17793, 17794, 17795, 17796, 17797, 17798, 17799, 17800, 17801,
17802, 17803, 17804, 17805, 17806, 17807, 17808, 17809, 17810,
17811, 17812, 17813, 17814, 17815, 17816, 17817, 17818, 17819,
17820, 17821, 17822, 17823, 17824, 17825, 17826, 17827, 17828,
17829, 17830, 17831, 17832, 17833, 17834, 17835, 17836, 17837,
17838, 17839, 17840, 17841, 17842, 17843, 17844, 17845, 17846,
17847, 17848, 17849, 17850, 17851, 17852, 17853, 17854, 17855,
17856, 17857, 17858, 17859, 17860, 17861, 17862, 17863, 17864,
17865, 17866, 17867, 17868, 17869, 17870, 17871, 17872, 17873,
17874, 17875, 17876, 17877, 17878, 17879, 17880, 17881, 17882,
17883, 17884, 17885, 17886, 17887, 17888, 17889, 17890, 17891,
17892, 17893, 17894, 17895, 17896, 17897, 17898, 17899, 17900,
17901, 17902, 17903, 17904, 17905, 17906, 17907, 17908, 17909,
17910, 17911, 17912, 17913, 17914, 17915, 17916, 17917, 17918,
17919, 17920, 17921, 17922, 17923, 17924, 17925, 17926, 17927,
17928, 17929, 17930, 17931, 17932, 17933, 17934, 17935, 17936,
17937, 17938, 17939, 17940, 17941, 17942, 17943, 17944, 17945,
17946, 17947, 17948, 17949, 17950, 17951, 17952, 17953, 17954,
17955, 17956, 17957, 17958, 17959, 17960, 17961, 17962, 17963,
17964, 17965, 17966, 17967, 17968, 17969, 17970, 17971, 17972,
17973, 17974, 17975, 17976, 17977, 17978, 17979, 17980, 17981,
17982, 17983, 17984, 17985, 17986, 17987, 17988, 17989, 17990,
17991, 17992, 17993, 17994, 17995, 17996, 17997, 17998, 17999,
18000, 18001, 18002, 18003, 18004, 18005, 18006, 18007, 18008,
18009, 18010, 18011, 18012, 18013, 18014, 18015, 18016, 18017,
18018, 18019, 18020, 18021, 18022, 18023, 18024, 18025, 18026,
18027, 18028, 18029, 18030, 18031, 18032, 18033, 18034, 18035,
18036, 18037, 18038, 18039, 18040, 18041, 18042, 18043, 18044,
18045, 18046, 18047, 18048, 18049, 18050, 18051, 18052, 18053,
18054, 18055, 18056, 18057, 18058, 18059, 18060, 18061, 18062,
18063, 18064, 18065, 18066, 18067, 18068, 18069, 18070, 18071,
18072, 18073, 18074, 18075, 18076, 18077, 18078, 18079, 18080,
18081, 18082, 18083, 18084, 18085, 18086, 18087, 18088, 18089,
18090, 18091, 18092), class = "Date"), Active_Tails = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 8L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 17L, 18L, 18L, 19L, 19L, 19L, 20L, 21L, 21L,
21L, 22L, 22L, 23L, 24L, 25L, 26L, 26L, 26L, 26L, 25L, 26L,
26L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 29L, 30L, 30L, 31L,
32L, 33L, 33L, 34L, 34L, 34L, 35L, 35L, 36L, 36L, 36L, 37L,
37L, 37L, 37L, 38L, 40L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 45L, 46L,
46L, 46L, 46L, 46L, 46L, 47L, 48L, 48L, 49L, 49L, 49L, 49L,
50L, 51L, 51L, 52L, 52L, 52L, 52L, 53L, 53L, 54L, 55L, 55L,
55L, 55L, 56L, 56L, 56L, 58L, 58L, 58L, 58L, 60L, 59L, 59L,
60L, 60L, 60L, 60L, 61L, 62L, 63L, 63L, 63L, 63L, 65L, 65L,
65L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 67L, 67L, 67L, 67L,
67L, 68L, 68L, 68L, 68L, 69L, 69L, 69L, 69L, 69L, 69L, 69L,
69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L,
69L, 69L, 69L, 69L, 69L, 69L, 69L, 70L, 70L, 70L, 69L, 70L,
70L, 71L, 71L, 70L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 70L, 70L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L), MX_Credits = 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, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 3L, 4L, 3L, 2L, 4L, 4L, 1L, 3L, 2L, 4L,
4L, 3L, 3L, 4L, 2L, 5L, 5L, 4L, 4L, 6L, 7L, 2L, 4L, 6L, 4L,
7L, 9L, 6L, 4L, 7L, 3L, 9L, 6L, 9L, 7L, 7L, 8L, 7L, 5L, 8L,
10L, 11L, 9L, 6L, 8L, 5L, 7L, 6L, 9L, 10L, 8L, 10L, 7L, 9L,
11L, 9L, 10L, 11L, 8L, 10L, 11L, 11L, 9L, 8L, 9L, 13L, 13L,
16L, 15L, 10L, 13L, 16L, 12L, 10L, 14L, 17L, 12L, 12L, 13L,
15L, 18L, 14L, 24L, 15L, 20L, 17L, 17L, 14L, 22L, 19L, 21L,
23L, 16L, 19L, 23L, 16L, 22L, 17L, 17L, 15L, 22L, 21L, 16L,
19L, 19L, 18L, 14L, 23L, 23L, 25L, 17L, 15L, 22L, 21L, 17L,
19L, 17L, 20L, 23L, 22L, 22L, 22L, 19L, 19L, 25L, 22L, 25L,
25L, 21L, 22L, 24L, 24L, 22L, 20L, 26L, 22L, 22L, 26L, 25L,
24L, 27L, 27L, 26L, 24L, 28L, 23L, 27L, 25L, 25L, 27L, 27L,
23L, 28L, 23L, 23L, 29L, 32L, 23L, 19L, 30L, 27L, 30L, 29L,
25L, 29L, 26L, 24L, 30L, 30L, 33L, 24L, 31L, 30L, 28L, 28L,
29L, 35L, 33L, 30L, 33L, 35L, 37L, 32L, 32L, 36L, 30L, 31L,
33L, 33L, 31L, 33L, 33L, 37L, 33L, 33L, 38L, 37L, 37L, 38L,
34L, 36L, 38L, 28L, 35L, 30L, 33L, 38L, 39L, 30L, 34L, 32L,
28L, 37L, 33L, 36L, 39L, 33L, 36L, 34L, 39L, 28L, 39L, 39L,
32L, 30L, 35L, 33L, 37L, 25L, 32L, 30L, 28L, 39L, 36L, 33L,
38L, 40L, 37L, 33L, 35L, 43L, 30L, 32L, 40L, 36L, 30L, 31L,
41L, 29L, 31L, 38L, 41L, 34L, 35L, 42L, 34L, 33L, 40L, 33L,
31L, 38L, 37L, 29L, 33L, 35L, 38L, 34L, 33L, 36L, 39L, 33L,
33L, 31L, 33L, 36L, 33L, 38L, 33L, 30L, 28L, 30L, 28L, 37L,
34L, 33L, 33L, 34L, 35L, 31L, 38L, 30L, 35L, 30L, 45L, 35L,
31L, 30L, 26L, 26L, 35L, 34L, 26L, 34L, 36L, 31L, 31L), Capture_Rate = 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, 0.5, 0.5,
1, 1, 0.5, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5, 1,
0.33, 1, 1, 0.75, 0.5, 1, 1, 0.25, 0.6, 0.4, 0.8, 0.8, 0.6,
0.6, 0.8, 0.4, 1, 1, 0.67, 0.67, 1, 1, 0.25, 0.4, 0.6, 0.4,
0.64, 0.82, 0.55, 0.36, 0.64, 0.25, 0.75, 0.5, 0.75, 0.58,
0.58, 0.62, 0.54, 0.36, 0.57, 0.71, 0.79, 0.6, 0.38, 0.5,
0.31, 0.44, 0.38, 0.56, 0.63, 0.47, 0.56, 0.39, 0.47, 0.58,
0.47, 0.5, 0.52, 0.38, 0.48, 0.5, 0.5, 0.39, 0.33, 0.36,
0.5, 0.5, 0.62, 0.58, 0.4, 0.5, 0.62, 0.44, 0.37, 0.5, 0.61,
0.43, 0.43, 0.46, 0.52, 0.6, 0.47, 0.77, 0.47, 0.61, 0.52,
0.5, 0.41, 0.65, 0.54, 0.6, 0.64, 0.44, 0.53, 0.62, 0.43,
0.59, 0.46, 0.45, 0.38, 0.54, 0.51, 0.39, 0.46, 0.46, 0.44,
0.34, 0.56, 0.53, 0.58, 0.4, 0.35, 0.51, 0.49, 0.4, 0.44,
0.4, 0.44, 0.5, 0.48, 0.48, 0.48, 0.41, 0.41, 0.53, 0.46,
0.52, 0.51, 0.43, 0.45, 0.49, 0.48, 0.43, 0.39, 0.5, 0.42,
0.42, 0.5, 0.47, 0.45, 0.5, 0.49, 0.47, 0.44, 0.51, 0.41,
0.48, 0.45, 0.43, 0.47, 0.47, 0.4, 0.47, 0.39, 0.39, 0.48,
0.53, 0.38, 0.32, 0.49, 0.44, 0.48, 0.46, 0.4, 0.46, 0.4,
0.37, 0.46, 0.45, 0.5, 0.36, 0.47, 0.45, 0.42, 0.42, 0.43,
0.52, 0.49, 0.45, 0.49, 0.51, 0.54, 0.47, 0.47, 0.52, 0.43,
0.45, 0.48, 0.48, 0.45, 0.48, 0.48, 0.54, 0.48, 0.48, 0.55,
0.54, 0.54, 0.55, 0.49, 0.52, 0.55, 0.41, 0.51, 0.43, 0.48,
0.55, 0.57, 0.43, 0.49, 0.46, 0.4, 0.53, 0.48, 0.51, 0.56,
0.46, 0.51, 0.49, 0.55, 0.39, 0.55, 0.55, 0.45, 0.42, 0.49,
0.46, 0.52, 0.35, 0.46, 0.43, 0.39, 0.55, 0.51, 0.46, 0.54,
0.56, 0.52, 0.46, 0.49, 0.61, 0.42, 0.45, 0.56, 0.51, 0.42,
0.44, 0.58, 0.41, 0.44, 0.54, 0.58, 0.48, 0.49, 0.59, 0.48,
0.46, 0.56, 0.46, 0.44, 0.54, 0.52, 0.41, 0.46, 0.49, 0.54,
0.48, 0.46, 0.51, 0.55, 0.46, 0.46, 0.44, 0.46, 0.51, 0.46,
0.54, 0.46, 0.42, 0.39, 0.42, 0.39, 0.52, 0.48, 0.46, 0.46,
0.48, 0.49, 0.44, 0.54, 0.42, 0.49, 0.42, 0.63, 0.49, 0.44,
0.42, 0.37, 0.37, 0.49, 0.48, 0.37, 0.48, 0.51, 0.44, 0.44
), Total_SPR_IML = c(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, 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, 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, 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, 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, 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,
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, 0L,
0L, 0L, 0L, 0L), Capture_Rate_w_SPR_IML = 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, 0.5, 0.5, 1, 1, 0.5,
1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5, 1, 0.33, 1, 1,
0.75, 0.5, 1, 1, 0.25, 0.6, 0.4, 0.8, 0.8, 0.6, 0.6, 0.8,
0.4, 1, 1, 0.67, 0.67, 1, 1, 0.25, 0.4, 0.6, 0.4, 0.64, 0.82,
0.55, 0.36, 0.64, 0.25, 0.75, 0.5, 0.75, 0.58, 0.58, 0.62,
0.54, 0.36, 0.57, 0.71, 0.79, 0.6, 0.38, 0.5, 0.31, 0.44,
0.38, 0.56, 0.63, 0.47, 0.56, 0.39, 0.47, 0.58, 0.47, 0.5,
0.52, 0.38, 0.48, 0.5, 0.5, 0.39, 0.33, 0.36, 0.5, 0.5, 0.62,
0.58, 0.4, 0.5, 0.62, 0.44, 0.37, 0.5, 0.61, 0.43, 0.43,
0.46, 0.52, 0.6, 0.47, 0.77, 0.47, 0.61, 0.52, 0.5, 0.41,
0.65, 0.54, 0.6, 0.64, 0.44, 0.53, 0.62, 0.43, 0.59, 0.46,
0.45, 0.38, 0.54, 0.51, 0.39, 0.46, 0.46, 0.44, 0.34, 0.56,
0.53, 0.58, 0.4, 0.35, 0.51, 0.49, 0.4, 0.44, 0.4, 0.44,
0.5, 0.48, 0.48, 0.48, 0.41, 0.41, 0.53, 0.46, 0.52, 0.51,
0.43, 0.45, 0.49, 0.48, 0.43, 0.39, 0.5, 0.42, 0.42, 0.5,
0.47, 0.45, 0.5, 0.49, 0.47, 0.44, 0.51, 0.41, 0.48, 0.45,
0.43, 0.47, 0.47, 0.4, 0.47, 0.39, 0.39, 0.48, 0.53, 0.38,
0.32, 0.49, 0.44, 0.48, 0.46, 0.4, 0.46, 0.4, 0.37, 0.46,
0.45, 0.5, 0.36, 0.47, 0.45, 0.42, 0.42, 0.43, 0.52, 0.49,
0.45, 0.49, 0.51, 0.54, 0.47, 0.47, 0.52, 0.43, 0.45, 0.48,
0.48, 0.45, 0.48, 0.48, 0.54, 0.48, 0.48, 0.55, 0.54, 0.54,
0.55, 0.49, 0.52, 0.55, 0.41, 0.51, 0.43, 0.48, 0.55, 0.57,
0.43, 0.49, 0.46, 0.4, 0.53, 0.48, 0.51, 0.56, 0.46, 0.51,
0.49, 0.55, 0.39, 0.55, 0.55, 0.45, 0.42, 0.49, 0.46, 0.52,
0.35, 0.46, 0.43, 0.39, 0.55, 0.51, 0.46, 0.54, 0.56, 0.52,
0.46, 0.49, 0.61, 0.42, 0.45, 0.56, 0.51, 0.42, 0.44, 0.58,
0.41, 0.44, 0.54, 0.58, 0.48, 0.49, 0.59, 0.48, 0.46, 0.56,
0.46, 0.44, 0.54, 0.52, 0.41, 0.46, 0.49, 0.54, 0.48, 0.46,
0.51, 0.55, 0.46, 0.46, 0.44, 0.46, 0.51, 0.46, 0.54, 0.46,
0.42, 0.39, 0.42, 0.39, 0.52, 0.48, 0.46, 0.46, 0.48, 0.49,
0.44, 0.54, 0.42, 0.49, 0.42, 0.63, 0.49, 0.44, 0.42, 0.37,
0.37, 0.49, 0.48, 0.37, 0.48, 0.51, 0.44, 0.44), sYear = 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, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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("2018 -",
"2019 -"), class = "factor"), sYear_Month = structure(c(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, 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, 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, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 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, 7L, 7L, 7L, 7L, 7L, 7L, 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, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 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, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L), .Label = c("2018-05",
"2018-06", "2018-07", "2018-08", "2018-09", "2018-10", "2018-11",
"2018-12", "2019-01", "2019-02", "2019-03", "2019-04", "2019-05",
"2019-06", "2019-07"), class = "factor"), Season = structure(c(3L,
3L, 3L, 3L, 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, 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, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 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, 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, 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, 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, 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), .Label = c("0.Winter 1H",
"1.Winter 2H", "2.Spring", "3.Summer", "4.Fall"), class = "factor"),
Year_Season = structure(c(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, 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, 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, 3L, 3L, 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,
4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 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, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L), .Label = c("2018-0.Winter 1H", "2018-2.Spring",
"2018-3.Summer", "2018-4.Fall", "2019-1.Winter 2H", "2019-2.Spring",
"2019-3.Summer"), class = "factor")), row.names = c(NA, 418L
), class = "data.frame")
特恩布兰德
因此，经验密度的解决方案比理论分布要容易一些。首先，让我们设置一些虚拟数据，因为我们没有您的任何数据可供使用。
set.seed(123)
# Setup some facets
idx <- expand.grid(c("A", "B"), c("C", "D"))
# For each facet, generate some numbers
df <- apply(idx, 1, function(x){
data.frame(row = x[[1]],
col = x[[2]],
# chose 10 as mean, since Weibull can't be negative
x = rnorm(100, 10))
})
df <- do.call(rbind, df)
Now for the empirical case, we can simply take the density in each facet. We can do this, because ggplot has included kernel density estimates as a stat function.
ggplot(df, aes(x)) +
geom_histogram(binwidth = 0.1) +
# To line up the histogram with KDE, we multiply y-values by binwidth
geom_line(aes(y = ..count..*0.1, colour = "empirical"), stat = "density") +
facet_grid(row ~ col)
Which looks like this:
Because we don't have any ggplot stat functions for the theoretical densities -at least not ones that are panel specific- we would have to pre-compute the xy-coordinates for the theoretical distributions in a separate data.frame:
# Loop over facets
dists <- apply(idx, 1, function(i){
# Grab data belonging to facet
dat <- df$x[df$row == i[[1]] & df$col == i[[2]]]
# Setup x-values
xseq <- seq(min(dat), max(dat), length.out = 100)
# Specify distributions of interest
dists <- c("weibull", "lnorm", "gamma")
# Loop over distributions
fits <- lapply(setNames(dists, dists), function(dist) {
# Estimate parameters
ests <- fitdist(dat, dist)$estimate
# Get y-values
y <- do.call(paste0("d", dist), c(list(x = xseq), as.list(ests)))
# Multiplied by length(dat) to match absolute counts
y * length(dat)
})
# Format everything neatly in a data.frame
out <- lapply(dists, function(j) {
data.frame(row = i[[1]],
col = i[[2]],
x = xseq,
y = fits[[j]],
distr = j)
})
# Combine all distributions
do.call(rbind, out)
})
# Combine all facets
dists <- do.call(rbind, dists)
Now that we've done that tedious work, we can finally plot it:
ggplot(df, aes(x)) +
geom_histogram(binwidth = 0.1) +
geom_line(data = dists, aes(y = y * 0.1, colour = distr)) +
facet_grid(row ~ col)
Adapt as necessary for your own data. Good luck!
EDIT: Now with example data
假设df是您发布dput()输出的 data.frame 。我已经包含了一个条件，用于检查方面数据的长度是否大于 2 以及方差是否为非零，以便跳过我们无论如何都无法进行任何估计的数据。此外，我已将变量名称转换为与您在 data.frame 中命名它们的方式兼容。
idx <- expand.grid(levels(df$Season), levels(df$sYear))
# Loop over facets
dists <- apply(idx, 1, function(i){
dat <- df$Capture_Rate[df$Season == i[[1]] & df$sYear == i[[2]]]
print(length(dat))
if (length(dat) < 2 | var(dat) == 0) {
return(NULL)
}
xseq <- seq(min(dat), max(dat), length.out = 100)
dists <- c("weibull", "lnorm", "gamma")
fits <- lapply(setNames(dists, dists), function(dist) {
ests <- fitdist(dat, dist)$estimate
y <- do.call(paste0("d", dist), c(list(x = xseq), as.list(ests)))
y * length(dat)
})
out <- lapply(dists, function(j) {
data.frame(Season = i[[1]],
sYear = i[[2]],
x = xseq,
y = fits[[j]],
distr = j)
})
do.call(rbind, out)
})
dists <- do.call(rbind, dists)
ggplot(df, aes(x=Capture_Rate, fill=sYear))+
geom_histogram(binwidth = .025,
alpha = .5,
position = "identity") +
geom_line(data = dists, aes(x, y * .025, colour = distr), inherit.aes = FALSE) +
facet_grid(Season ~ sYear)
本文收集自互联网，转载请注明来源。
如有侵权，请联系[email protected] 删除。
编辑于
相关文章
Related 相关文章
- 1
geom_abline似乎不尊重facet_grid中的组[ggplot2]
- 2
ggplot2中的facet_wrap（）和facet_grid（）有什么区别？
- 3
ggplot2中facet_grid的图层无效
- 4
将字符串传递给facet_grid：ggplot2
- 5
改变ggplot2中的facet_grid输出
- 6
在ggplot2中使用facet_grid的自定义散点图矩阵
- 7
ggplot2：如何获取facet_grid（）的labeller = label_both和facet_wrap（）的ncol选项的合并功能？
- 8
ggplot2中facet_grid的label_parsed与空格和表达式混合
- 9
使用ggplot2中的facet_grid更改中断次数
- 10
如何在ggplot2中使用facet_grid制作甜甜圈图？
- 11
使用facet_grid / ggplot2将时间序列数据偏移一天
- 12
ggplot facet_grid标签上标
- 13
将参数传递给ggplot和facet_grid
- 14
ggplot2：更改facet_grid图中的strip.text位置
- 15
使用ggplot（）和facet_grid（）绘制几个图
- 16
使用facet_grid时使用..count ..的R ggplot2
- 17
绘制多列+ facet_grid（ggplot）
- 18
如何使用ggplot2 facet_grid注释没有数据的构面？
- 19
ggplot facet_grid x轴步进
- 20
R // ggplot2：结合facet_wrap和for循环时的动态标题
- 21
如何在ggplot2 :: facet_grid中格式化网格标题和条目
- 22
ggplot2 facet_grid，带有方面标题
- 23
将ggplot的facet_grid与bmrs conditional_effects结合
- 24
将ggplot2和facet_grid一起用于连续变量和分类变量（R）
- 25
ggplot2中的facet_grid错误
- 26
ggplot facet_grid 现在切换左条带和刻度线
- 27
`ggplot2` 中的 `annotate` 与 `facet_grid` 结合时会报告错误
- 28
控制facet_grid、ggplot中的间距和标签
- 29
R ggplot facet_grid 多箱线图
我来说两句