在gratia draw()函数中使用的默认值是什么不确定度？

Question

我有一个数据集，看起来像这样。

structure(list(landings = c(116, 31, 0, 0, 0, 
    0, 0, 0, 0, 120, 0, 241, 9, 0, 64, 326, 142, 605, 139, 410, 
    212, 470, 416, 309, 1269, 474, 22, 135, 395, 464, 451, 32, 
    2537, 210, 299, 1522, 184, 550, 666, 429, 1372, 184, 147, 
    1208, 159, 951, 1000, 1100, 301, 144, 244, 0, 0, 281, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 42, 594, 26, 747, 436, 0, 914, 182, 
    8, 275, 175, 766, 130, 930, 31, 177, 123, 895, 88, 107, 0, 
    4, 481, 909, 511, 877, 402, 295, 336, 645, 310, 301, 398, 
    411, 0, 205, 293, 49, 454, 162, 138, 1171, 0, 138, 0, 111, 
    0, 0, 36, 78, 114, 0, 0, 134, 44, 549, 0, 378, 716, 739, 
    393, 203, 839, 70, 454, 132, 651, 63, 1850, 217, 403, 55, 
    0, 408, 43, 17, 12, 26, 2, 811, 581, 1216, 154, 1059, 89, 
    1862, 1310, 297, 29, 680, 0, 0, 29, 0, 0, 0, 0, 0, 0, 17, 
    6, 0, 0, 0, 44, 909, 0, 0, 0, 194, 0, 212, 18, 46, 44, 56, 
    365, 37, 0, 73, 11, 16, 19, 0, 0, 0, 23, 0, 92, 0, 216, 0, 
    16, 0, 80, 319, 59, 35, 929, 47, 0, 0, 356, 0, 0, 33, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 91, 362, 0, 
    0, 0, 0, 0, 29, 0, 0, 392, 105, 0, 94, 15, 222, 34, 44, 178, 
    1867, 0, 224, 241, 23, 1502, 492, 168, 0, 234, 299, 453, 
    0, 406, 149, 0, 39, 57, 86, 0, 28, 23, 265, 0, 0, 0, 168, 
    31, 20, 0, 28, 78, 244, 13, 0, 99, 168, 861, 52, 649, 0, 
    174, 0, 0, 2462, 64, 178, 0, 61, 0, 321, 391, 33, 17, 227, 
    241, 248, 294, 1119, 37, 90, 0, 85, 37, 89, 0, 0, 0), Date = c(2014, 
    2014.01916495551, 2014.03832991102, 2014.05749486653, 2014.07665982204, 
    2014.09582477755, 2014.11498973306, 2014.13415468857, 2014.15331964408, 
    2014.17248459959, 2014.1916495551, 2014.21081451061, 2014.22997946612, 
    2014.24914442163, 2014.26830937714, 2014.28747433265, 2014.30663928816, 
    2014.32580424367, 2014.34496919918, 2014.36413415469, 2014.3832991102, 
    2014.40246406571, 2014.42162902122, 2014.44079397673, 2014.45995893224, 
    2014.47912388775, 2014.49828884326, 2014.51745379877, 2014.53661875428, 
    2014.55578370979, 2014.5749486653, 2014.59411362081, 2014.61327857632, 
    2014.63244353183, 2014.65160848734, 2014.67077344285, 2014.68993839836, 
    2014.70910335387, 2014.72826830938, 2014.74743326489, 2014.7665982204, 
    2014.78576317591, 2014.80492813142, 2014.82409308693, 2014.84325804244, 
    2014.86242299795, 2014.88158795346, 2014.90075290897, 2014.91991786448, 
    2014.93908281999, 2014.9582477755, 2014.97741273101, 2014.99657768652, 
    2015.01574264203, 2015.03490759754, 2015.05407255305, 2015.07323750856, 
    2015.09240246407, 2015.11156741958, 2015.13073237509, 2015.1498973306, 
    2015.16906228611, 2015.18822724162, 2015.20739219713, 2015.22655715264, 
    2015.24572210815, 2015.26488706366, 2015.28405201916, 2015.30321697467, 
    2015.32238193018, 2015.34154688569, 2015.3607118412, 2015.37987679671, 
    2015.39904175222, 2015.41820670773, 2015.43737166324, 2015.45653661875, 
    2015.47570157426, 2015.49486652977, 2015.51403148528, 2015.53319644079, 
    2015.5523613963, 2015.57152635181, 2015.59069130732, 2015.60985626283, 
    2015.62902121834, 2015.64818617385, 2015.66735112936, 2015.68651608487, 
    2015.70568104038, 2015.72484599589, 2015.7440109514, 2015.76317590691, 
    2015.78234086242, 2015.80150581793, 2015.82067077344, 2015.83983572895, 
    2015.85900068446, 2015.87816563997, 2015.89733059548, 2015.91649555099, 
    2015.9356605065, 2015.95482546201, 2015.97399041752, 2015.99315537303, 
    2016.01232032854, 2016.03148528405, 2016.05065023956, 2016.06981519507, 
    2016.08898015058, 2016.10814510609, 2016.1273100616, 2016.14647501711, 
    2016.16563997262, 2016.18480492813, 2016.20396988364, 2016.22313483915, 
    2016.24229979466, 2016.26146475017, 2016.28062970568, 2016.29979466119, 
    2016.3189596167, 2016.33812457221, 2016.35728952772, 2016.37645448323, 
    2016.39561943874, 2016.41478439425, 2016.43394934976, 2016.45311430527, 
    2016.47227926078, 2016.49144421629, 2016.5106091718, 2016.52977412731, 
    2016.54893908282, 2016.56810403833, 2016.58726899384, 2016.60643394935, 
    2016.62559890486, 2016.64476386037, 2016.66392881588, 2016.68309377139, 
    2016.7022587269, 2016.72142368241, 2016.74058863792, 2016.75975359343, 
    2016.77891854894, 2016.79808350445, 2016.81724845996, 2016.83641341547, 
    2016.85557837098, 2016.87474332649, 2016.893908282, 2016.91307323751, 
    2016.93223819302, 2016.95140314853, 2016.97056810404, 2016.98973305955, 
    2017.00889801506, 2017.02806297057, 2017.04722792608, 2017.06639288159, 
    2017.0855578371, 2017.10472279261, 2017.12388774812, 2017.14305270363, 
    2017.16221765914, 2017.18138261465, 2017.20054757016, 2017.21971252567, 
    2017.23887748118, 2017.25804243669, 2017.2772073922, 2017.29637234771, 
    2017.31553730322, 2017.33470225873, 2017.35386721424, 2017.37303216975, 
    2017.39219712526, 2017.41136208077, 2017.43052703628, 2017.44969199179, 
    2017.4688569473, 2017.48802190281, 2017.50718685832, 2017.52635181383, 
    2017.54551676934, 2017.56468172485, 2017.58384668036, 2017.60301163587, 
    2017.62217659138, 2017.64134154689, 2017.6605065024, 2017.67967145791, 
    2017.69883641342, 2017.71800136893, 2017.73716632444, 2017.75633127995, 
    2017.77549623546, 2017.79466119097, 2017.81382614648, 2017.83299110199, 
    2017.85215605749, 2017.871321013, 2017.89048596851, 2017.90965092402, 
    2017.92881587953, 2017.94798083504, 2017.96714579055, 2017.98631074606, 
    2018.00547570157, 2018.02464065708, 2018.04380561259, 2018.0629705681, 
    2018.08213552361, 2018.12046543463, 2018.13963039014, 2018.15879534565, 
    2018.17796030116, 2018.19712525667, 2018.21629021218, 2018.23545516769, 
    2018.2546201232, 2018.27378507871, 2018.29295003422, 2018.31211498973, 
    2018.33127994524, 2018.35044490075, 2018.36960985626, 2018.38877481177, 
    2018.40793976728, 2018.42710472279, 2018.4462696783, 2018.46543463381, 
    2018.48459958932, 2018.50376454483, 2018.52292950034, 2018.54209445585, 
    2018.56125941136, 2018.58042436687, 2018.59958932238, 2018.61875427789, 
    2018.6379192334, 2018.65708418891, 2018.67624914442, 2018.69541409993, 
    2018.71457905544, 2018.73374401095, 2018.75290896646, 2018.77207392197, 
    2018.79123887748, 2018.81040383299, 2018.8295687885, 2018.84873374401, 
    2018.86789869952, 2018.88706365503, 2018.90622861054, 2018.92539356605, 
    2018.94455852156, 2018.96372347707, 2018.98288843258, 2019.00205338809, 
    2019.0212183436, 2019.04038329911, 2019.05954825462, 2019.07871321013, 
    2019.09787816564, 2019.11704312115, 2019.13620807666, 2019.15537303217, 
    2019.17453798768, 2019.19370294319, 2019.2128678987, 2019.23203285421, 
    2019.25119780972, 2019.27036276523, 2019.28952772074, 2019.30869267625, 
    2019.32785763176, 2019.34702258727, 2019.36618754278, 2019.38535249829, 
    2019.4045174538, 2019.42368240931, 2019.44284736482, 2019.46201232033, 
    2019.48117727584, 2019.50034223135, 2019.51950718686, 2019.53867214237, 
    2019.55783709788, 2019.57700205339, 2019.5961670089, 2019.61533196441, 
    2019.63449691992, 2019.65366187543, 2019.67282683094, 2019.69199178645, 
    2019.71115674196, 2019.73032169747, 2019.74948665298, 2019.76865160849, 
    2019.787816564, 2019.80698151951, 2019.82614647502, 2019.84531143053, 
    2019.86447638604, 2019.88364134155, 2019.90280629706, 2019.92197125257, 
    2019.94113620808, 2019.96030116359, 2019.9794661191))

我运行的GAM是这样的

gam1<-gam(landings~s(Date))

我使用draw绘制数据

draw(gam1)

我一直在寻找draw()函数中不确定度的测量方法，但没有成功。这个图中的不确定度是用95%置信区间还是标准误差来绘制的？

Answer 1

这是一个近似于95%的可信区间（按2*平稳的标准误差画出），和你从以下方面得到的结果一样 mgcv:::plot.gam().

我应该把这一点说得更清楚，并允许用户在包中控制他们想要的间隔的覆盖范围。