如何修复 ggplot2 中 predict_gam 的锯齿线？
How to fix jagged line from predict_gam in ggplot2?
数据：
structure(list(ID = c(19903L, 28185L, 28207L, 28429L, 28522L,
29092L, 29127L, 29219L, 29304L, 30981L, 31166L, 31411L, 32010L,
33231L, 33640L, 33714L, 34093L, 34193L, 34385L, 35054L, 35337L,
35377L, 35608L, 35881L, 35940L, 37112L, 37122L, 37125L, 37170L,
37198L, 37266L, 37378L, 37589L, 37725L, 37877L, 38519L, 38522L,
38605L, 38623L, 38806L, 39040L, 39083L, 39159L, 39218L, 39593L,
39636L, 39657L, 39686L, 39700L, 39819L, 39820L, 39951L, 40151L,
40152L, 40181L, 40226L, 40248L, 40286L, 40382L, 40556L, 40623L,
40628L, 40798L, 40800L, 40815L, 40915L, 43282L, 43299L, 43450L,
43466L, 43509L, 43677L, 43740L, 43762L, 43998L, 44068L, 44130L,
44131L, 44307L, 44408L, 50679L, 50848L, 51064L, 51455L, 51690L,
51726L, 51727L, 51796L, 52126L, 52183L, 52461L, 52500L, 52502L,
52577L, 52614L, 53202L, 53320L, 53390L, 53456L, 53473L, 53474L,
53475L, 53577L, 53626L, 53851L, 53873L, 54153L, 54206L, 54532L,
54581L, 54913L, 55122L, 55267L, 55332L, 55462L, 55542L, 55612L,
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56420L, 56679L, 56703L, 56746L, 56919L, 57005L, 57035L, 57405L,
57445L, 57480L, 57725L, 57808L, 57809L, 57863L, 58004L, 58060L,
58130L, 58145L, 58215L, 58229L, 58503L, 58515L, 58667L, 58999L,
59326L, 59327L, 59344L, 59361L, 59428L, 59756L, 59865L, 60099L,
60100L, 60169L, 60252L, 60280L, 60306L, 60384L, 60429L, 60472L,
60493L, 60503L, 60575L, 60603L, 60662L, 60664L, 60806L, 60846L,
60925L, 61274L, 61415L, 61727L, 61749L, 61882L, 61883L, 62081L,
62144L, 62210L, 62285L, 62411L, 62809L, 62917L, 62934L, 62937L,
62983L, 62989L, 63327L, 63329L, 63383L, 63458L, 63470L, 63589L,
64081L, 64328L, 64418L, 64507L, 64596L, 65178L, 65250L, 65302L,
65478L, 65480L, 65487L, 65565L, 65572L, 65574L, 65617L, 65802L,
65865L, 65934L, 65935L, 65974L, 65975L, 65978L, 65991L, 65995L,
66013L, 66154L, 66232L, 66237L, 66245L, 66314L, 66389L, 66396L,
66460L, 66572L, 66589L, 66735L, 67174L, 73230L, 73525L, 73539L,
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74427L, 74439L, 74607L, 74618L, 74641L, 74657L, 74794L, 74800L,
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74989L, 75220L, 75229L, 75377L, 75407L, 75432L, 75653L, 75732L,
75735L, 75737L, 75757L, 75895L, 75898L, 76381L, 76559L, 76574L,
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80819L, 80901L, 80932L, 81064L, 81065L, 81071L, 81098L, 81112L,
81142L, 81175L, 81727L, 81938L, 82554L, 83744L, 83949L), Age = c(83L,
26L, 26L, 20L, 84L, 20L, 23L, 77L, 32L, 14L, 21L, 9L, 76L, 18L,
21L, 15L, 75L, 27L, 34L, 81L, 81L, 15L, 24L, 24L, 16L, 35L, 27L,
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83L, 79L, 18L, 26L, 66L, 23L, 83L, 21L, 77L, 24L, 57L, 42L, 32L,
76L, 85L, 29L, 77L, 65L, 79L, 9L, 34L, 20L, 11L, 16L, 9L, 21L,
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, 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, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 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, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
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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,
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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_FA_scaled = c(-1.52160414281774, -1.30073487609629,
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0.731789463645971, 0.0910470403091081, 0.618791802670374,
0.267229848163289, 0.199251694841068, 0.246957313356364,
1.87125072361518, -1.40312565725327, -0.190900477709198,
0.257180463051856, 1.48421907338698, 0.0556569866890196,
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0.91364858124609, 1.5448556730327, 0.930329981315788, 0.312119032378622,
1.15772266013046, -0.0360834735033167, 1.78212397237474,
-0.861407326257228, 0.476608931763807, 1.38366006055364,
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0.652132882236762, -0.639712677761503, 0.046078213992748,
-0.171257839519489, 0.349420496423362, 0.184018332971865,
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0.174801569825717, 0.584835306868775, -0.84211945742664,
1.05460061968224, 1.61507104537468, -1.62830066556388, 0.0799550676933195
), RAVLT_DELAY = c(NA, 12L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA,
7L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5L, 12L,
NA, NA, NA, NA, 14L, NA, NA, NA, NA, NA, 6L, 7L, NA, NA,
NA, NA, 7L, 1L, 1L, 11L, 4L, 12L, 7L, 9L, 9L, 8L, 14L, 12L,
7L, 12L, 7L, 6L, 13L, 10L, 13L, NA, 11L, 14L, 8L, 0L, 11L,
15L, 13L, 6L, 9L, 9L, 12L, 5L, 14L, 15L, 12L, 4L, 15L, 8L,
15L, 14L, 5L, 12L, 8L, 9L, 9L, 13L, 6L, 4L, 10L, NA, 4L,
13L, 9L, 14L, 8L, 15L, 14L, 9L, 15L, 14L, 11L, 11L, 15L,
12L, 9L, 13L, 14L, 7L, 13L, 9L, 12L, 10L, 6L, 9L, 10L, 11L,
15L, 11L, 11L, NA, 9L, 12L, 10L, 9L, 11L, 2L, 12L, NA, 6L,
12L, 12L, 10L, 11L, 4L, 13L, 4L, 5L, 6L, 12L, 15L, 11L, 11L,
14L, 2L, 11L, 5L, 10L, 12L, 10L, NA, 12L, 8L, 12L, 12L, 8L,
7L, 14L, 14L, 7L, 8L, NA, 9L, 6L, 15L, 7L, 14L, 8L, 14L,
11L, 13L, 6L, 12L, 11L, 14L, 15L, 10L, 6L, 13L, 7L, 4L, 12L,
14L, 7L, 13L, 3L, 13L, 7L, 10L, 6L, 8L, 3L, 15L, 11L, 15L,
11L, 11L, 8L, 4L, 7L, 10L, 5L, 7L, 8L, 9L, 14L, 12L, 14L,
12L, NA, NA, 11L, 10L, 13L, 7L, 12L, 12L, 14L, 8L, 13L, 2L,
11L, 8L, 7L, 4L, 7L, 9L, 4L, 12L, 14L, 15L, 12L, 13L, 9L,
7L, 11L, 10L, 14L, 6L, 5L, 5L, 10L, 8L, 5L, 12L, 2L, 11L,
8L, NA, 9L, 7L, 8L, 12L, 10L, 7L, 13L, 15L, 9L, 6L, 4L, 10L,
8L, 13L, 10L, 9L, 7L, 7L, 15L, 8L, 12L, 9L, 10L, 12L, 6L,
13L, 8L, 11L, 9L, 1L, 13L, 12L, NA, 8L, 2L, 11L, 9L, 7L,
6L, 10L, 13L, 15L, 6L, 5L, 7L, 5L, 5L, 11L, 11L, 13L, 9L,
4L, 10L, 2L, NA, 12L, 10L, 15L, NA, 6L)), row.names = c(NA,
-324L), class = c("tbl_df", "tbl", "data.frame"))
我在 mgcv::gam 中使用以下模型：
m1 <- gam(mean_FA_scaled ~ s(Age, bs = 'ad', k = -1) + Sex +
te(Age, by = Sex, bs ='fs') +
te(RAVLT_DELAY, by = Sex, bs = 'fs') + s(RAVLT_DELAY),
data = DF,
method = 'REML', family = gaussian)
我想重现 gam plot:
但是在 ggplot 中。但是，当我使用 predict_gam 时，我的情节非常参差不齐。当我尝试在 age.
上绘制平滑项效应时，不会发生这种情况
# Plot
m1_p <- predict_gam(m1)
m1_p %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex))
geom_smooth_ci(Sex, size = 1, alpha = 1) +
theme_classic(base_size = 24)
您的拟合对象在 RAVLY_DELAY 的长度上对每个年龄和每个性别都有预测。使用您现有的代码，每个系列都会尝试将这些不同线条的所有值绘制为一个系列，因此出现锯齿。
如果我们告诉 ggplot 将每个年龄、性别组合视为不同的系列（又名组），我们得到：
m1_p %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex, group = interaction(Age,Sex)))
这里有很多年龄段，我们可以分别看到：
m1_p %>%
mutate(Age = round(Age, 1)) %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex)) +
facet_wrap(~Age, ncol = 10)
虽然错了，但我喜欢仅按年龄分组时出现的审美品质：
我遇到了同样的问题，我终于设法解决了，或者这就是我的想法。我是初学者，不是专家，很抱歉我在这个领域的哑巴语言。
发生这种情况是因为除了您正在绘制的变量之外，您的模型中还有更多变量会导致差异。
因此，您需要做的是创建一个新数据库，其中包含您未以某种方式固定的所有变量、您可以使用的数字均值、因子、选择一个等。
然后 运行 带有函数 predict.gam（不是“_”）的模型，它让你添加一个新的数据库，这将是你的变量固定的那个。
然后 predict.gam 必须变成一个数据库来绘制它，所以你将它（结果）与你的新数据绑定，然后你可以毫无问题地使用 ggplot2 和 geom_smooth_ci .
示例：
model<-x~gam(s(v4, by=v3) + s(v2, by=v1)
#I want to plot the first smooth, first create the data:
new=expand.grid(v1=levels(circ$v1)[1], v2=mean(circ$v2), v3=levels(circ$v3), v4=seq(0,23, 0.1))
# see that I maintain the levels and the numbers of v3 and v4, and I fix the other ones randomly.
predict<-predict.gam(model, newdata = new, se.fit = TRUE)
mew=cbind(new,preddist2n)
mew %>%
ggplot(aes(v4, fit)) +
geom_smooth_ci(v3, ci_z = 1.96, ci_alpha = 0.05 )
如果你需要绘制第二个平滑之后，你应该创建另一个数据库来修复第二个平滑之外的变量...
告诉我它是否适合你:)
数据：
structure(list(ID = c(19903L, 28185L, 28207L, 28429L, 28522L,
29092L, 29127L, 29219L, 29304L, 30981L, 31166L, 31411L, 32010L,
33231L, 33640L, 33714L, 34093L, 34193L, 34385L, 35054L, 35337L,
35377L, 35608L, 35881L, 35940L, 37112L, 37122L, 37125L, 37170L,
37198L, 37266L, 37378L, 37589L, 37725L, 37877L, 38519L, 38522L,
38605L, 38623L, 38806L, 39040L, 39083L, 39159L, 39218L, 39593L,
39636L, 39657L, 39686L, 39700L, 39819L, 39820L, 39951L, 40151L,
40152L, 40181L, 40226L, 40248L, 40286L, 40382L, 40556L, 40623L,
40628L, 40798L, 40800L, 40815L, 40915L, 43282L, 43299L, 43450L,
43466L, 43509L, 43677L, 43740L, 43762L, 43998L, 44068L, 44130L,
44131L, 44307L, 44408L, 50679L, 50848L, 51064L, 51455L, 51690L,
51726L, 51727L, 51796L, 52126L, 52183L, 52461L, 52500L, 52502L,
52577L, 52614L, 53202L, 53320L, 53390L, 53456L, 53473L, 53474L,
53475L, 53577L, 53626L, 53851L, 53873L, 54153L, 54206L, 54532L,
54581L, 54913L, 55122L, 55267L, 55332L, 55462L, 55542L, 55612L,
55728L, 55867L, 55903L, 55920L, 55991L, 56022L, 56098L, 56307L,
56420L, 56679L, 56703L, 56746L, 56919L, 57005L, 57035L, 57405L,
57445L, 57480L, 57725L, 57808L, 57809L, 57863L, 58004L, 58060L,
58130L, 58145L, 58215L, 58229L, 58503L, 58515L, 58667L, 58999L,
59326L, 59327L, 59344L, 59361L, 59428L, 59756L, 59865L, 60099L,
60100L, 60169L, 60252L, 60280L, 60306L, 60384L, 60429L, 60472L,
60493L, 60503L, 60575L, 60603L, 60662L, 60664L, 60806L, 60846L,
60925L, 61274L, 61415L, 61727L, 61749L, 61882L, 61883L, 62081L,
62144L, 62210L, 62285L, 62411L, 62809L, 62917L, 62934L, 62937L,
62983L, 62989L, 63327L, 63329L, 63383L, 63458L, 63470L, 63589L,
64081L, 64328L, 64418L, 64507L, 64596L, 65178L, 65250L, 65302L,
65478L, 65480L, 65487L, 65565L, 65572L, 65574L, 65617L, 65802L,
65865L, 65934L, 65935L, 65974L, 65975L, 65978L, 65991L, 65995L,
66013L, 66154L, 66232L, 66237L, 66245L, 66314L, 66389L, 66396L,
66460L, 66572L, 66589L, 66735L, 67174L, 73230L, 73525L, 73539L,
73677L, 73705L, 73942L, 73953L, 74034L, 74113L, 74114L, 74425L,
74427L, 74439L, 74607L, 74618L, 74641L, 74657L, 74794L, 74800L,
74836L, 74942L, 74952L, 74962L, 74969L, 74975L, 74977L, 74985L,
74989L, 75220L, 75229L, 75377L, 75407L, 75432L, 75653L, 75732L,
75735L, 75737L, 75757L, 75895L, 75898L, 76381L, 76559L, 76574L,
76594L, 76595L, 76746L, 76751L, 76755L, 76759L, 76775L, 77088L,
77091L, 77099L, 77109L, 77134L, 77182L, 77188L, 77203L, 77204L,
77252L, 77304L, 77453L, 77528L, 77556L, 77585L, 77668L, 77733L,
77758L, 78262L, 79724L, 79730L, 79747L, 79850L, 79977L, 80052L,
80819L, 80901L, 80932L, 81064L, 81065L, 81071L, 81098L, 81112L,
81142L, 81175L, 81727L, 81938L, 82554L, 83744L, 83949L), Age = c(83L,
26L, 26L, 20L, 84L, 20L, 23L, 77L, 32L, 14L, 21L, 9L, 76L, 18L,
21L, 15L, 75L, 27L, 34L, 81L, 81L, 15L, 24L, 24L, 16L, 35L, 27L,
7L, 30L, 31L, 24L, 24L, 79L, 30L, 19L, 78L, 25L, 20L, 42L, 62L,
83L, 79L, 18L, 26L, 66L, 23L, 83L, 21L, 77L, 24L, 57L, 42L, 32L,
76L, 85L, 29L, 77L, 65L, 79L, 9L, 34L, 20L, 11L, 16L, 9L, 21L,
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, 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, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 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, 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_FA_scaled = c(-1.52160414281774, -1.30073487609629,
-1.39164271432334, -1.83373601712535, -2.19478262184568,
-0.47769168350816, -1.66624867866514, -0.36061779499817,
-1.10976759821506, -2.01706489349897, -1.21708170925372,
-0.68001882107227, -0.770347444019124, -1.21756680205088,
-1.04908755742334, -0.654272701867476, 0.791455877697352,
0.0263414533200063, -1.48353521852673, -1.48465744813212,
0.885781086077571, 0.937258844105155, -1.76609091258925,
-1.40930154017838, -1.42620014597815, -0.395529996012095,
-1.79188771313106, -1.6968602062236, -1.6213377738768, -1.26578647412735,
-1.3364652186935, -1.52114801078458, 0.587760344033774, -1.4860765255686,
-1.41824317606643, -1.08076339305916, -1.84290933912549,
-1.42950167307528, -0.186882171702826, 0.94192876730175,
-1.96157606965602, -0.668579319288362, -1.2972378638421,
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-1.42625806750617, -0.635317881823001, -1.05738481631217,
-0.905876579394418, 0.0731565283419971, -1.15139145628828,
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0.366463906043185, 0.957024565541906, 0.669292134912623,
1.05465854121026, 1.82844671440856, -0.181835758574102, 0.736386984932541,
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0.829691326381412, 1.37355999410519, -0.459526044282955,
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0.731789463645971, 0.0910470403091081, 0.618791802670374,
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0.570130478942752, 0.862605234678655, -0.328963679935357,
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1.09066401106555, 0.810541021179871, 1.62963106948065, 1.03406743799922,
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0.4092534348678, 0.813183690895774, 1.08099111588625, 0.708867018932142,
0.0693192271106869, 1.26885235182742, -0.117571823236151,
0.174801569825717, 0.584835306868775, -0.84211945742664,
1.05460061968224, 1.61507104537468, -1.62830066556388, 0.0799550676933195
), RAVLT_DELAY = c(NA, 12L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA,
7L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5L, 12L,
NA, NA, NA, NA, 14L, NA, NA, NA, NA, NA, 6L, 7L, NA, NA,
NA, NA, 7L, 1L, 1L, 11L, 4L, 12L, 7L, 9L, 9L, 8L, 14L, 12L,
7L, 12L, 7L, 6L, 13L, 10L, 13L, NA, 11L, 14L, 8L, 0L, 11L,
15L, 13L, 6L, 9L, 9L, 12L, 5L, 14L, 15L, 12L, 4L, 15L, 8L,
15L, 14L, 5L, 12L, 8L, 9L, 9L, 13L, 6L, 4L, 10L, NA, 4L,
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4L, 10L, 2L, NA, 12L, 10L, 15L, NA, 6L)), row.names = c(NA,
-324L), class = c("tbl_df", "tbl", "data.frame"))
我在 mgcv::gam 中使用以下模型：
m1 <- gam(mean_FA_scaled ~ s(Age, bs = 'ad', k = -1) + Sex +
te(Age, by = Sex, bs ='fs') +
te(RAVLT_DELAY, by = Sex, bs = 'fs') + s(RAVLT_DELAY),
data = DF,
method = 'REML', family = gaussian)
我想重现 gam plot:
但是在 ggplot 中。但是，当我使用 predict_gam 时，我的情节非常参差不齐。当我尝试在 age.
# Plot
m1_p <- predict_gam(m1)
m1_p %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex))
geom_smooth_ci(Sex, size = 1, alpha = 1) +
theme_classic(base_size = 24)
您的拟合对象在 RAVLY_DELAY 的长度上对每个年龄和每个性别都有预测。使用您现有的代码，每个系列都会尝试将这些不同线条的所有值绘制为一个系列，因此出现锯齿。
如果我们告诉 ggplot 将每个年龄、性别组合视为不同的系列（又名组），我们得到：
m1_p %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex, group = interaction(Age,Sex)))
这里有很多年龄段，我们可以分别看到：
m1_p %>%
mutate(Age = round(Age, 1)) %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex)) +
facet_wrap(~Age, ncol = 10)
虽然错了，但我喜欢仅按年龄分组时出现的审美品质：
我遇到了同样的问题，我终于设法解决了，或者这就是我的想法。我是初学者，不是专家，很抱歉我在这个领域的哑巴语言。 发生这种情况是因为除了您正在绘制的变量之外，您的模型中还有更多变量会导致差异。 因此，您需要做的是创建一个新数据库，其中包含您未以某种方式固定的所有变量、您可以使用的数字均值、因子、选择一个等。 然后 运行 带有函数 predict.gam（不是“_”）的模型，它让你添加一个新的数据库，这将是你的变量固定的那个。
然后 predict.gam 必须变成一个数据库来绘制它，所以你将它（结果）与你的新数据绑定，然后你可以毫无问题地使用 ggplot2 和 geom_smooth_ci .
示例：
model<-x~gam(s(v4, by=v3) + s(v2, by=v1)
#I want to plot the first smooth, first create the data:
new=expand.grid(v1=levels(circ$v1)[1], v2=mean(circ$v2), v3=levels(circ$v3), v4=seq(0,23, 0.1))
# see that I maintain the levels and the numbers of v3 and v4, and I fix the other ones randomly.
predict<-predict.gam(model, newdata = new, se.fit = TRUE)
mew=cbind(new,preddist2n)
mew %>%
ggplot(aes(v4, fit)) +
geom_smooth_ci(v3, ci_z = 1.96, ci_alpha = 0.05 )
如果你需要绘制第二个平滑之后，你应该创建另一个数据库来修复第二个平滑之外的变量...
告诉我它是否适合你:)