使用mgcvgam的Gtsummary輸出-有解無憂

我有以下資料集：

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"))

我正在使用這個游戲模型：

m1 <- gam(mean_AD_scaled ~ s(Age, bs = 'ad', k = -1)    Sex   ti(Age, by = Sex, bs ='fs'),  
          data = DF, 
          method = 'REML', 
          family = gaussian)

輸出：

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

但是當我使用 gtsummary 時，我會得到每個性別“互動”的重復值：

  tbl_regression(m1, tidy_fun = tidy_gam)

使用 mgcv gam 的 Gtsummary 輸出

我在一份出版物中看到了以下內容，我試圖用性別和年齡來復制：使用 mgcv gam 的 Gtsummary 輸出

我不知道如何解決這個問題。我的目標是為手稿列印一張表格，以便可以添加任何其他與游戲相關的資訊，例如edf和R^2。

uj5u.com熱心網友回復：

我認為您在處理這些型別的互動時發現了一個錯誤。雖然我們正在修復該錯誤，但此代碼應該可以滿足您的需求。謝謝

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

特征	貝塔	95% 置信度	p 值
年級
一世	—	—
二	-0.39	-0.70, -0.08	0.014
三	-0.13	-0.43, 0.18	0.4
智者）			>0.9
ti（年齡）：I級			0.6
ti（年齡）：二級			>0.9
ti（年齡）：Ⅲ級			0.6

^{由reprex 包于 2022-02-21 創建(v2.0.1)}

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