使用 mgcv gam 的 Gsummary 输出
Gtsummary output with mgcv gam
我有以下数据集：
structure(list(Age = c(83L, 26L, 26L, 20L, 20L, 77L, 32L, 21L,
15L, 75L, 27L, 81L, 81L, 15L, 24L, 16L, 35L, 27L, 30L, 31L, 24L,
24L, 31L, 79L, 30L, 19L, 20L, 42L, 62L, 83L, 79L, 18L, 26L, 66L,
23L, 83L, 77L, 80L, 57L, 42L, 32L, 76L, 85L, 29L, 65L, 79L, 9L,
34L, 20L, 16L, 34L, 22L, 19L, 23L, 25L, 14L, 53L, 28L, 79L, 22L,
22L, 21L, 82L, 81L, 16L, 19L, 77L, 15L, 18L, 15L, 78L, 24L, 16L,
14L, 29L, 18L, 50L, 17L, 43L, 8L, 14L, 85L, 31L, 20L, 30L, 23L,
78L, 29L, 6L, 61L, 14L, 22L, 10L, 83L, 15L, 13L, 15L, 15L, 29L,
8L, 9L, 15L, 8L, 9L, 15L, 9L, 34L, 8L, 9L, 9L, 16L, 8L, 25L,
21L, 23L, 13L, 56L, 10L, 7L, 27L, 8L, 8L, 8L, 8L, 80L, 80L, 6L,
15L, 42L, 25L, 23L, 21L, 8L, 11L, 43L, 69L, 34L, 34L, 14L, 12L,
10L, 22L, 78L, 16L, 76L, 12L, 10L, 16L, 6L, 13L, 66L, 11L, 26L,
12L, 16L, 13L, 24L, 76L, 10L, 65L, 20L, 13L, 25L, 14L, 12L, 15L,
43L, 51L, 27L, 15L, 24L, 34L, 63L, 17L, 15L, 9L, 12L, 17L, 82L,
75L, 24L, 44L, 69L, 11L, 10L, 12L, 10L, 10L, 70L, 54L, 45L, 42L,
84L, 54L, 23L, 23L, 14L, 81L, 17L, 42L, 44L, 16L, 15L, 43L, 45L,
50L, 53L, 23L, 53L, 49L, 13L, 69L, 14L, 65L, 14L, 13L, 22L, 67L,
59L, 52L, 54L, 44L, 78L, 62L, 69L, 10L, 63L, 57L, 22L, 12L, 62L,
9L, 82L, 53L, 54L, 66L, 49L, 63L, 51L, 9L, 45L, 49L, 77L, 49L,
61L, 62L, 57L, 67L, 16L, 65L, 75L, 45L, 16L, 55L, 17L, 64L, 67L,
56L, 52L, 63L, 10L, 62L, 14L, 66L, 68L, 15L, 13L, 43L, 47L, 55L,
69L, 21L, 67L, 34L, 52L, 15L, 31L, 64L, 55L, 13L, 48L, 71L, 64L,
13L, 25L, 34L, 50L, 61L, 70L, 33L, 57L, 51L, 46L, 57L, 69L, 46L,
8L, 11L, 46L, 71L, 33L, 38L, 56L, 17L, 29L, 28L, 6L, 8L), Sex = structure(c(1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L), .Label = c("Male", "Female"), class = "factor"),
mean_AD_scaled = c(3.15891332561581, -0.0551328105526693,
0.582747640515478, 1.94179165777054, 1.7064645993306, 2.37250948563045,
1.015775832203, 1.36189033704266, -1.05640048650493, 0.184814975542474,
-0.143366705302007, 1.81560178585347, 2.06325078470728, -0.473088628698217,
0.414641167726219, 0.199887349084444, -0.60620959209809,
-0.17879228399189, -1.03483709078065, -1.43497010225613,
-0.958595084469815, 1.0203965598582, -1.44731404613503, -1.17191867788498,
-2.02547709312595, -1.22395687266857, -1.09952727795348,
-1.0830246791849, 1.21072653232248, 1.69997357714829, 1.53648783201423,
0.208688735094353, 0.0862394522314924, 1.08662698958276,
-0.731299290763917, 2.29307697689102, -0.660008064083659,
-1.21425334459264, 1.10191939777498, -2.0957781638801, -1.14947514355972,
0.248845058764562, 2.6526135953958, 0.197907037232212, -0.222469162066061,
1.92880961340592, 1.23328008397287, -1.17288683034607, -0.308282675662673,
-1.02603570477074, -1.32647101621898, -1.58316343919798,
-0.0440210607151585, -0.388375288352846, -0.935491446193807,
-0.63789458173376, 0.454577456746182, -1.77391147749773,
0.709267564407921, 0.125735671950958, -0.821073428064989,
-0.126534054558056, 0.519597695894384, 0.188005477971066,
0.212319306823438, -1.45807374053215, 1.5856655763446, -1.25641198358011,
-0.910847565366061, -1.1191763722206, 0.25300371365424, -0.750772357310844,
0.37932560636146, -0.871791414947088, -1.92771569802088,
-1.1752191976387, 0.210449012296334, -0.347778895382139,
-0.132254955464496, 0.953616043508016, -0.0862677135627232,
0.838977990728951, -1.8993092246739, -0.0254281327692267,
0.298022803094927, -1.21559555595915, 0.0134079829994995,
-0.763094297724715, 0.334768589686298, -1.12568939786794,
-2.11786964276497, -0.0434709740895377, 0.388237009696492,
1.30050066962355, -0.260645173884043, -0.60620959209809,
1.05945271027717, -0.275717547426008, -0.0238878902174922,
0.496604074943496, 0.534009965485611, -0.692903244295693,
-0.566933407028871, 0.125625654625835, -0.518305749324122,
1.79381835547894, -0.790708646330802, -0.227860010997131,
0.347420582075538, 0.784189362817269, -0.660118081408782,
1.29962053102256, -0.561652575422924, -0.710395998990384,
-1.29315777017148, -0.457356151205503, -1.01756437073621,
0.146528946399368, -1.07136284272178, -1.42968927065019,
0.798601632408495, -0.799730066990963, -0.431348055546223,
0.569545561500617, 2.32168148142323, 0.472070211440872, 1.65145593676866,
-0.814142336582189, -0.544489872703603, -0.315433801795725,
0.382626126115175, -0.623812364117908, 0.216279930527897,
-0.606099574772967, -0.367207954999011, 0.719829227619811,
-0.749122097433987, 0.934693063586709, -0.79026857703031,
-0.371872689584264, 0.0769979969210905, -0.793899148759394,
1.50414273842782, 0.730280873506577, -0.290569886317732,
0.303743704001367, 0.390877425499463, -1.00359217044547,
-0.534918365417827, 0.325967203676389, 0.129036191704673,
0.34434009697207, -0.141386393449775, -0.363401355549725,
-0.395416397160769, -0.0235578382421178, -1.13583299524436,
1.16781977552417, -1.31890182425046, 0.139377820266317, 0.0160483988024708,
0.481311666751279, -1.05475022662807, 0.839858129329941,
0.652498624644007, -0.350199276534864, -0.262075399110649,
0.178543988010412, -1.13198238886502, -0.05117218684821,
-1.29678834190056, 0.429603523943066, 1.05098137624263, -0.956504755292464,
0.502765045150433, -0.81678275238516, -1.50263075720731,
-0.826684311646306, 2.40100397283753, 2.06633126981075, -0.470558230220369,
0.484942238480364, 0.822035322659877, 0.143888530596397,
0.384056351341786, -0.63580425255641, 0.358422314587926,
-0.372422776209885, 0.0607154328027556, -0.113221958218067,
1.02710761669075, -0.349649189909243, 2.27195365046724, -0.507634068787109,
-0.326105482332738, -1.0396778530861, 1.06484355920824, 1.32151397872221,
-0.185173288849074, -0.651888785489516, -0.171311105883464,
-0.104200537557911, -0.693673365571561, -1.26609350819101,
0.411230630647381, -0.929770545287362, -0.481009876107135,
0.386146680519137, 0.0482834750637615, -0.198265350538812,
0.790020281048832, 0.926001694901924, -1.08918564939184,
0.50298507980068, -0.0694350628187722, 1.04966116834114,
0.00878725534429612, 1.48742010500899, 0.750194009353997,
0.423772605711498, -0.596418050162068, -0.652636903300361,
-0.308942779613417, 0.314437388003408, 0.679562886624478,
-1.24312189070515, -0.432712270377761, 0.00427654501421597,
-0.197935298563442, 0.228821905592019, 1.06957430418856,
-1.61612462980509, 1.9499329398297, -0.263285589687014, 0.156430505660519,
-0.322254875953402, -0.451085163673446, -0.35526007349056,
0.10780284795577, 0.408700232169533, -0.957604928543701,
-1.05662052115517, 1.00345389178912, -0.238751726184391,
0.300003114947154, -0.397946795638617, -0.0802167606809086,
0.943714484246865, 1.10973062785877, 1.76279346979401, 1.62087112038423,
0.25533608094687, 0.226841593739787, 0.869672824438507, -1.44960240649761,
-0.450315042397579, -0.199629565370345, 0.29813282042005,
0.760425620590513, 1.87391096816911, -0.454275666102039,
-0.0559029318285365, -0.343048150401812, -1.01371376435687,
0.68880434193488, -0.29222014619459, 1.16132875334186, -1.95715633422403,
-0.534368278792206, -0.560112332871189, 1.84508642898666,
-1.19150176175703, -0.772203732244971, -0.3443683583033,
-1.45684154649076, -0.633823940704178, -1.77454957798344,
0.279539892474118, -0.875532004001301, 1.26001429397797,
-0.536590628759707, 2.1869102581465, 0.211109116247078, 0.130246382281038,
-0.355810160116181, -0.898085555651692, -0.429741802599415,
1.13360438741065, 1.61338994227581, 0.588688576072169, 0.454137387445685,
0.747113524250528, 0.460848444278238, -0.38177424884541,
-0.169990897981981, -0.747361820232001, -0.760123829946369,
0.208028631143609, -1.28748087619509, 2.33950428809329, -0.973029357526068,
-1.06091119683501, 0.917530360867389, -0.35041931118511,
-1.90613029883158, -1.15057531681095, 0.65348878057012, 0.43147381847017
)), row.names = c(NA, -308L), class = c("tbl_df", "tbl",
"data.frame"))
我正在使用这个 gam 模型：
m1 <- gam(mean_AD_scaled ~ s(Age, bs = 'ad', k = -1) + Sex + ti(Age, by = Sex, bs ='fs'),
data = DF,
method = 'REML',
family = gaussian)
输出：
Family: gaussian
Link function: identity
Formula:
mean_AD_scaled ~ s(Age, bs = "ad", k = -1) + Sex + ti(Age,
by = Sex, bs = "fs")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04691 0.06976 0.672 0.502
SexFemale -0.12950 0.09428 -1.374 0.171
Approximate significance of smooth terms:
edf Ref.df F p-value
s(Age) 2.980 3.959 8.72 2.24e-06 ***
ti(Age):SexMale 2.391 2.873 23.47 < 2e-16 ***
ti(Age):SexFemale 1.000 1.000 43.40 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Rank: 48/49
R-sq.(adj) = 0.34 Deviance explained = 35.6%
-REML = 375.4 Scale est. = 0.63867 n = 308
但是当我使用 gtsummary 时，我得到了每个性别的重复值 'interaction':
tbl_regression(m1, tidy_fun = tidy_gam)
我在一份出版物中看到以下内容，我正试图复制它的性别和年龄：
我不知道如何解决这个问题。我的目标是为手稿打印 table，以便可以添加任何其他与 gam 相关的信息，例如 edf 和 R^2。
我认为您在处理这些类型的交互时发现了一个错误。在我们修复错误的同时，这段代码应该可以满足您的需求。谢谢
library(gtsummary)
#> #BlackLivesMatter
library(mgcv)
packageVersion("gtsummary")
#> [1] ‘1.5.2’
m1 <- gam(marker ~ s(age, bs = 'ad', k = -1) + grade + ti(age, by = grade, bs ='fs'),
data = gtsummary::trial,
method = 'REML',
family = gaussian)
tbl_regression(m1, tidy_fun = gtsummary::tidy_gam) %>%
modify_table_body(
~ .x %>%
dplyr::select(-n_obs) %>%
dplyr::distinct()
) %>%
as_kable() # convert to kable to display on SO
Characteristic
Beta
95% CI
p-value
Grade
I
—
—
II
-0.39
-0.70, -0.08
0.014
III
-0.13
-0.43, 0.18
0.4
s(age)
>0.9
ti(age):gradeI
0.6
ti(age):gradeII
>0.9
ti(age):gradeIII
0.6
由 reprex package (v2.0.1)
创建于 2022-02-21
我有以下数据集：
structure(list(Age = c(83L, 26L, 26L, 20L, 20L, 77L, 32L, 21L,
15L, 75L, 27L, 81L, 81L, 15L, 24L, 16L, 35L, 27L, 30L, 31L, 24L,
24L, 31L, 79L, 30L, 19L, 20L, 42L, 62L, 83L, 79L, 18L, 26L, 66L,
23L, 83L, 77L, 80L, 57L, 42L, 32L, 76L, 85L, 29L, 65L, 79L, 9L,
34L, 20L, 16L, 34L, 22L, 19L, 23L, 25L, 14L, 53L, 28L, 79L, 22L,
22L, 21L, 82L, 81L, 16L, 19L, 77L, 15L, 18L, 15L, 78L, 24L, 16L,
14L, 29L, 18L, 50L, 17L, 43L, 8L, 14L, 85L, 31L, 20L, 30L, 23L,
78L, 29L, 6L, 61L, 14L, 22L, 10L, 83L, 15L, 13L, 15L, 15L, 29L,
8L, 9L, 15L, 8L, 9L, 15L, 9L, 34L, 8L, 9L, 9L, 16L, 8L, 25L,
21L, 23L, 13L, 56L, 10L, 7L, 27L, 8L, 8L, 8L, 8L, 80L, 80L, 6L,
15L, 42L, 25L, 23L, 21L, 8L, 11L, 43L, 69L, 34L, 34L, 14L, 12L,
10L, 22L, 78L, 16L, 76L, 12L, 10L, 16L, 6L, 13L, 66L, 11L, 26L,
12L, 16L, 13L, 24L, 76L, 10L, 65L, 20L, 13L, 25L, 14L, 12L, 15L,
43L, 51L, 27L, 15L, 24L, 34L, 63L, 17L, 15L, 9L, 12L, 17L, 82L,
75L, 24L, 44L, 69L, 11L, 10L, 12L, 10L, 10L, 70L, 54L, 45L, 42L,
84L, 54L, 23L, 23L, 14L, 81L, 17L, 42L, 44L, 16L, 15L, 43L, 45L,
50L, 53L, 23L, 53L, 49L, 13L, 69L, 14L, 65L, 14L, 13L, 22L, 67L,
59L, 52L, 54L, 44L, 78L, 62L, 69L, 10L, 63L, 57L, 22L, 12L, 62L,
9L, 82L, 53L, 54L, 66L, 49L, 63L, 51L, 9L, 45L, 49L, 77L, 49L,
61L, 62L, 57L, 67L, 16L, 65L, 75L, 45L, 16L, 55L, 17L, 64L, 67L,
56L, 52L, 63L, 10L, 62L, 14L, 66L, 68L, 15L, 13L, 43L, 47L, 55L,
69L, 21L, 67L, 34L, 52L, 15L, 31L, 64L, 55L, 13L, 48L, 71L, 64L,
13L, 25L, 34L, 50L, 61L, 70L, 33L, 57L, 51L, 46L, 57L, 69L, 46L,
8L, 11L, 46L, 71L, 33L, 38L, 56L, 17L, 29L, 28L, 6L, 8L), Sex = structure(c(1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L), .Label = c("Male", "Female"), class = "factor"),
mean_AD_scaled = c(3.15891332561581, -0.0551328105526693,
0.582747640515478, 1.94179165777054, 1.7064645993306, 2.37250948563045,
1.015775832203, 1.36189033704266, -1.05640048650493, 0.184814975542474,
-0.143366705302007, 1.81560178585347, 2.06325078470728, -0.473088628698217,
0.414641167726219, 0.199887349084444, -0.60620959209809,
-0.17879228399189, -1.03483709078065, -1.43497010225613,
-0.958595084469815, 1.0203965598582, -1.44731404613503, -1.17191867788498,
-2.02547709312595, -1.22395687266857, -1.09952727795348,
-1.0830246791849, 1.21072653232248, 1.69997357714829, 1.53648783201423,
0.208688735094353, 0.0862394522314924, 1.08662698958276,
-0.731299290763917, 2.29307697689102, -0.660008064083659,
-1.21425334459264, 1.10191939777498, -2.0957781638801, -1.14947514355972,
0.248845058764562, 2.6526135953958, 0.197907037232212, -0.222469162066061,
1.92880961340592, 1.23328008397287, -1.17288683034607, -0.308282675662673,
-1.02603570477074, -1.32647101621898, -1.58316343919798,
-0.0440210607151585, -0.388375288352846, -0.935491446193807,
-0.63789458173376, 0.454577456746182, -1.77391147749773,
0.709267564407921, 0.125735671950958, -0.821073428064989,
-0.126534054558056, 0.519597695894384, 0.188005477971066,
0.212319306823438, -1.45807374053215, 1.5856655763446, -1.25641198358011,
-0.910847565366061, -1.1191763722206, 0.25300371365424, -0.750772357310844,
0.37932560636146, -0.871791414947088, -1.92771569802088,
-1.1752191976387, 0.210449012296334, -0.347778895382139,
-0.132254955464496, 0.953616043508016, -0.0862677135627232,
0.838977990728951, -1.8993092246739, -0.0254281327692267,
0.298022803094927, -1.21559555595915, 0.0134079829994995,
-0.763094297724715, 0.334768589686298, -1.12568939786794,
-2.11786964276497, -0.0434709740895377, 0.388237009696492,
1.30050066962355, -0.260645173884043, -0.60620959209809,
1.05945271027717, -0.275717547426008, -0.0238878902174922,
0.496604074943496, 0.534009965485611, -0.692903244295693,
-0.566933407028871, 0.125625654625835, -0.518305749324122,
1.79381835547894, -0.790708646330802, -0.227860010997131,
0.347420582075538, 0.784189362817269, -0.660118081408782,
1.29962053102256, -0.561652575422924, -0.710395998990384,
-1.29315777017148, -0.457356151205503, -1.01756437073621,
0.146528946399368, -1.07136284272178, -1.42968927065019,
0.798601632408495, -0.799730066990963, -0.431348055546223,
0.569545561500617, 2.32168148142323, 0.472070211440872, 1.65145593676866,
-0.814142336582189, -0.544489872703603, -0.315433801795725,
0.382626126115175, -0.623812364117908, 0.216279930527897,
-0.606099574772967, -0.367207954999011, 0.719829227619811,
-0.749122097433987, 0.934693063586709, -0.79026857703031,
-0.371872689584264, 0.0769979969210905, -0.793899148759394,
1.50414273842782, 0.730280873506577, -0.290569886317732,
0.303743704001367, 0.390877425499463, -1.00359217044547,
-0.534918365417827, 0.325967203676389, 0.129036191704673,
0.34434009697207, -0.141386393449775, -0.363401355549725,
-0.395416397160769, -0.0235578382421178, -1.13583299524436,
1.16781977552417, -1.31890182425046, 0.139377820266317, 0.0160483988024708,
0.481311666751279, -1.05475022662807, 0.839858129329941,
0.652498624644007, -0.350199276534864, -0.262075399110649,
0.178543988010412, -1.13198238886502, -0.05117218684821,
-1.29678834190056, 0.429603523943066, 1.05098137624263, -0.956504755292464,
0.502765045150433, -0.81678275238516, -1.50263075720731,
-0.826684311646306, 2.40100397283753, 2.06633126981075, -0.470558230220369,
0.484942238480364, 0.822035322659877, 0.143888530596397,
0.384056351341786, -0.63580425255641, 0.358422314587926,
-0.372422776209885, 0.0607154328027556, -0.113221958218067,
1.02710761669075, -0.349649189909243, 2.27195365046724, -0.507634068787109,
-0.326105482332738, -1.0396778530861, 1.06484355920824, 1.32151397872221,
-0.185173288849074, -0.651888785489516, -0.171311105883464,
-0.104200537557911, -0.693673365571561, -1.26609350819101,
0.411230630647381, -0.929770545287362, -0.481009876107135,
0.386146680519137, 0.0482834750637615, -0.198265350538812,
0.790020281048832, 0.926001694901924, -1.08918564939184,
0.50298507980068, -0.0694350628187722, 1.04966116834114,
0.00878725534429612, 1.48742010500899, 0.750194009353997,
0.423772605711498, -0.596418050162068, -0.652636903300361,
-0.308942779613417, 0.314437388003408, 0.679562886624478,
-1.24312189070515, -0.432712270377761, 0.00427654501421597,
-0.197935298563442, 0.228821905592019, 1.06957430418856,
-1.61612462980509, 1.9499329398297, -0.263285589687014, 0.156430505660519,
-0.322254875953402, -0.451085163673446, -0.35526007349056,
0.10780284795577, 0.408700232169533, -0.957604928543701,
-1.05662052115517, 1.00345389178912, -0.238751726184391,
0.300003114947154, -0.397946795638617, -0.0802167606809086,
0.943714484246865, 1.10973062785877, 1.76279346979401, 1.62087112038423,
0.25533608094687, 0.226841593739787, 0.869672824438507, -1.44960240649761,
-0.450315042397579, -0.199629565370345, 0.29813282042005,
0.760425620590513, 1.87391096816911, -0.454275666102039,
-0.0559029318285365, -0.343048150401812, -1.01371376435687,
0.68880434193488, -0.29222014619459, 1.16132875334186, -1.95715633422403,
-0.534368278792206, -0.560112332871189, 1.84508642898666,
-1.19150176175703, -0.772203732244971, -0.3443683583033,
-1.45684154649076, -0.633823940704178, -1.77454957798344,
0.279539892474118, -0.875532004001301, 1.26001429397797,
-0.536590628759707, 2.1869102581465, 0.211109116247078, 0.130246382281038,
-0.355810160116181, -0.898085555651692, -0.429741802599415,
1.13360438741065, 1.61338994227581, 0.588688576072169, 0.454137387445685,
0.747113524250528, 0.460848444278238, -0.38177424884541,
-0.169990897981981, -0.747361820232001, -0.760123829946369,
0.208028631143609, -1.28748087619509, 2.33950428809329, -0.973029357526068,
-1.06091119683501, 0.917530360867389, -0.35041931118511,
-1.90613029883158, -1.15057531681095, 0.65348878057012, 0.43147381847017
)), row.names = c(NA, -308L), class = c("tbl_df", "tbl",
"data.frame"))
我正在使用这个 gam 模型：
m1 <- gam(mean_AD_scaled ~ s(Age, bs = 'ad', k = -1) + Sex + ti(Age, by = Sex, bs ='fs'),
data = DF,
method = 'REML',
family = gaussian)
输出：
Family: gaussian
Link function: identity
Formula:
mean_AD_scaled ~ s(Age, bs = "ad", k = -1) + Sex + ti(Age,
by = Sex, bs = "fs")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04691 0.06976 0.672 0.502
SexFemale -0.12950 0.09428 -1.374 0.171
Approximate significance of smooth terms:
edf Ref.df F p-value
s(Age) 2.980 3.959 8.72 2.24e-06 ***
ti(Age):SexMale 2.391 2.873 23.47 < 2e-16 ***
ti(Age):SexFemale 1.000 1.000 43.40 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Rank: 48/49
R-sq.(adj) = 0.34 Deviance explained = 35.6%
-REML = 375.4 Scale est. = 0.63867 n = 308
但是当我使用 gtsummary 时，我得到了每个性别的重复值 'interaction':
tbl_regression(m1, tidy_fun = tidy_gam)
我在一份出版物中看到以下内容，我正试图复制它的性别和年龄：
我不知道如何解决这个问题。我的目标是为手稿打印 table，以便可以添加任何其他与 gam 相关的信息，例如 edf 和 R^2。
我认为您在处理这些类型的交互时发现了一个错误。在我们修复错误的同时，这段代码应该可以满足您的需求。谢谢
library(gtsummary)
#> #BlackLivesMatter
library(mgcv)
packageVersion("gtsummary")
#> [1] ‘1.5.2’
m1 <- gam(marker ~ s(age, bs = 'ad', k = -1) + grade + ti(age, by = grade, bs ='fs'),
data = gtsummary::trial,
method = 'REML',
family = gaussian)
tbl_regression(m1, tidy_fun = gtsummary::tidy_gam) %>%
modify_table_body(
~ .x %>%
dplyr::select(-n_obs) %>%
dplyr::distinct()
) %>%
as_kable() # convert to kable to display on SO
| Characteristic | Beta | 95% CI | p-value |
|---|---|---|---|
| Grade | |||
| I | — | — | |
| II | -0.39 | -0.70, -0.08 | 0.014 |
| III | -0.13 | -0.43, 0.18 | 0.4 |
| s(age) | >0.9 | ||
| ti(age):gradeI | 0.6 | ||
| ti(age):gradeII | >0.9 | ||
| ti(age):gradeIII | 0.6 |
由 reprex package (v2.0.1)
创建于 2022-02-21