如何将 predict.gam 与具有偏移量的 GAM 一起使用？

Question

I have a dataset that looks like this:

structure(list(effort = c(2633, 7871, 10273, 
5202, 8550, 4698, 7357, 3670, 8933, 8301, 4416, 5355, 443, 8946, 
11168, 14572, 15552, 13947, 7969, 7541, 27478, 8698, 9044, 10803, 
29567, 9261, 1892, 8258, 9744, 5937, 11277, 7260, 6600, 1385, 
6959, 13788, 11792, 10363, 27837, 12622, 20954, 11912, 14986, 
14331, 14612, 7230, 25266, 25518, 8293, 6637, 9049, 6053, 6195, 
9957, 5039, 4840, 9757, 7760, 5836, 5741, 203, 5857, 4584, 5022, 
17794, 3499, 17010, 14025, 12059, 21645, 7174, 16150, 11445, 
12035, 24534, 6379, 11183, 6072, 10104, 6675, 14265, 9222, 9099, 
14397, 14097, 15684, 19315, 8753, 13876, 22169, 15724, 4688, 
21923, 16051, 8415, 6117, 11456, 10134, 5044, 19750, 10624, 9225, 
3935, 5995, 26458, 15806, 10188, 1641, 11402, 54, 7203, 9196, 
22643, 13905, 561, 7675, 6913, 7765, 11046, 9639, 10833, 16405, 
26188, 14262, 10092, 9834, 33753, 28133, 7095, 12020, 14248, 
10619, 8587, 11951, 8739, 10862, 4872, 6351, 2243, 5272, 2870, 
963, 18789, 20216, 17339, 20585, 16121, 8203, 11968, 7082, 12494, 
4731, 9975, 8863, 14946, 7321, 11694, 3228, 3375, 5607, 6223, 
10922, 5594, 604, 13512, 715, 16321, 5429, 15807, 17313, 3273, 
18884, 22627, 21474, 7898, 11273, 10482, 15778, 9962, 10997, 
12926, 8386, 11580, 10621, 3296, 8579, 14194, 9817, 7873, 8868, 
8093, 9366, 11594, 6801, 15844, 3426, 342, 13291, 7239, 6943, 
11958, 20140, 11373, 36384, 9897, 12543, 4293, 6691, 3176, 9847, 
1750, 794, 554, 6591, 14309, 2740, 6856, 8444, 3242, 2640, 8481, 
3197, 2332, 9287, 15318, 6410, 20876, 23016, 6741, 16704, 15311, 
7531, 8648, 2784, 7355, 8113, 13470, 11159, 14903, 8367, 7075, 
7312, 7496, 14094, 15349, 7191, 12474, 11323, 6793, 21977, 11888, 
17712, 4310, 6308, 16487, 19514, 9420, 6320, 7026, 1655, 7041, 
3070, 3533, 11043, 3843, 7483, 7150, 4463, 4319, 10384, 7579, 
8298, 2502, 4803, 8676, 16523, 10248, 5342, 4780, 3936, 17412, 
31632, 10323, 19263, 12757, 13171, 11301, 4273, 8657, 7512, 9319, 
9483, 3695, 4496, 7407, 26571, 5176, 2454, 9207, 9075, 16222, 
14280, 9963, 9426, 10864, 10627, 6665, 17141, 18597, 6093, 8094, 
4238), 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),  month = c(1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 
8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
11L, 12L, 12L, 12L, 12L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 
6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 
8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 
11L, 12L, 12L, 12L, 12L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 
6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 
8L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
12L, 12L, 12L, 12L, 12L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 
6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 
10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L), 
    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
    ))

I am running a gam that looks like this:

CSA1.offset.gam.week<-gam(landings~ s(Date, bs = "tp") + s(month, bs = "cc", k=12) + offset(log(effort)),
                           data = CSA1.effort.land.week2, family = nb, method="REML")

I am looking to use predict.gam() to plot my data in ggplot but am having issues due the presence of an offset.

When I use predict.gam() like this to get add a fit and SE to my dataset it looks like this:

cbind(CSA1.effort.land.week2,
                          predict.gam(CSA1.offset.gam.week, 
                                  se.fit=TRUE,
                                  type="response",
                                  terms="s(Date)"))

When I plot this fit it shows up as an extremely jagged linear model.

When I remove the offset, I see a GAM that alligns with what I expect a my GAM to look like but I need to include this offset in my data.

This is the GAM without the offset:

CSA1.offset.gam.week<-gam(landings~ s(Date, bs = "tp")+s(month, bs = "cc", k=12), data = CSA1.effort.land.week2,family = nb, method="REML")

This is what that GAM looks like with the same predict.gam function as the previous example

How should I be using the predict.gam function with this GAM if I intend to keep the offset???

Answer 1

The jaggedness is coming from the predictions using different (the observed) effort values. The data arose from different efforts so if you want to compare the model output with the data then you need to provide the observed offsets.

It you want to show the model predictions for the same amount of effort per observation, generate some new data over the range of covariates and provide a constant offset:

newd <- with(CSA1.effort.land.week2,
             data.frame(Date = seq(min(Date), max(Date), length = 1000),
                        effort = 1))
newd <- transform(newd, month = as.numeric(format(Date, format = "%m")))

(not tested; am away from a computer with R right now.)

But don't expect this to go anywhere near the data because that would be an apples-to-oranges comparison for the same reason you modelled the data with the offset in the first place.

With effort = 1 , the units on the values given by predict() will be landings per unit effort. If you set effort = 10 it would be landings per 10 efforts. Whatever effort is measured in would give you the final units. If effort was in hours, then for effort = 10 the units would be landings per 10 hours.

When you include an offset in a count model like this, the model becomes a rate per unit effort (in your case). Hence you can get predicted counts out of the model model for any amount of effort, but you do need to supply it as a constant value if you want to compare predicted counts on a common basis.

I don't know how you created the plot shown, but don't use the standard error when type = "response" ; it's not wrong, it's just not useful for creating a confidence interval when on the response scale. If you didn't create the confidence this way — on the link scale and then back transform to the response scale using the inverse of the link function — then it is likely wrong.