直观的线性回归

Question

我正试图为线性回归分析创建一个交互式的图表，如https://plotly.com/r/ml-regression/#linear-regression-with-r所示

在我的模型下面，我希望这是计算具有两个协变量(年龄和DUDIT)的线性回归的正确方法：

lm(mFRONTAL ~ OLIFE_IntAn_Short + Age + DUDIT，data = NumericDatawoOutliers)

当我试图巧妙地把它想象出来的时候，它似乎可以达到拟合回归线的程度。在这里，代码：

data(NumericDatawoOutliers)
y = NumericDatawoOutliers$mFRONTAL
x = NumericDatawoOutliers$OLIFE_IntAn_Short
lm_model = linear_reg() %>% 
  set_engine('lm') %>% 
  set_mode('regression') %>%
  fit(mFRONTAL ~ OLIFE_IntAn_Short + Age + DUDIT, data = NumericDatawoOutliers) 
x_range = seq(min(x), max(x))
x_range = matrix(x_range, nrow=123)
xdf = data.frame(x_range)
colnames(xdf) = c('OLIFE_IntAn_Short')
ydf = lm_model %>% predict(xdf) 
colnames(ydf) = c('mFRONTAL')
xy = data.frame(xdf, ydf) 
fig = plot_ly(NumericDatawoOutliers, x = ~ OLIFE_IntAn_Short, y = ~mFRONTAL, type = 'scatter', alpha = 0.65, mode = 'markers', name = 'Case')
fig = fig %>% add_trace(data = xy, x = ~ OLIFE_IntAn_Short, y = ~mFRONTAL, name = 'Regression Fit', mode = 'lines', alpha = 1)
fig

首先，它警告我“data = NumericDatawoOutliers中不存在dataset NumericDatawoOutliers”。我想我必须更改第1行中的“数据”，我能完全删除吗？

然后，我假设相关的错误发生在第8行，这里说: seq.default(min(x)，max(x))：'from‘必须是一个有限的数。但是变量"OLIFE_IntAn_Short“有一定数量的情况，这些情况也是正确绘制的。我想，也许是因为有15个错误，但在输出中，它说：“忽略15个观察”，因此它正确地识别了它们。

不幸的是，作为编程新手，我无法确定问题所在，也许你们中的一个可以。会很感激的！

在这里，我使用的数据：

structure(list(Subject = c("v201", "v001", "v0011", "v0012", 
"v0016", "v0047", "v042", "v082", "v086", "v087", "v088", "v089", 
"v095", "v096", "v102", "v104", "v105", "v108", "v109", "v110", 
"v122", "v123", "v124", "v129", "v130", "v133", "v136", "v139", 
"v140", "v141", "v142", "v146", "v202", "v205", "v206", "v207", 
"v0013", "v0014", "v0015", "v0018", "v0019", "v0020", "v0043", 
"v0044", "v0049", "v0061", "v0083", "v0084", "v0085", "v046", 
"v050", "v051", "v062", "v093", "v094", "v098", "v103", "v107", 
"v121", "v125", "v131", "v135", "v138", "v144", "v145", "v148", 
"v149", "v151", "v208", "v209", "v210", "a002", "a003", "a004", 
"a006", "a007", "a010", "a011", "a013", "a014b", "a015", "a016", 
"a020", "a024", "a025", "a026", "a027", "a028", "a030", "a033", 
"a034", "a035", "a037", "a038", "a039", "a040", "a041", "a043", 
"a045", "a047"), Group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("control", 
"pat"), class = "factor"), Diagnosis_Group = structure(c(5L, 
7L, 7L, 7L, 7L, 1L, 1L, 7L, 5L, 7L, 1L, 1L, 7L, 1L, 8L, 7L, 1L, 
8L, 1L, 1L, 1L, 8L, 1L, 4L, 1L, 7L, 8L, 7L, 7L, 1L, 7L, 8L, 1L, 
1L, 6L, 8L, 5L, 7L, 8L, 5L, 7L, 1L, NA, 1L, 8L, 7L, 8L, 7L, 1L, 
1L, 7L, 8L, 7L, 9L, 1L, 9L, 1L, 1L, 8L, 8L, 1L, 8L, 8L, 1L, 7L, 
7L, NA, 1L, 8L, 1L, 8L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L), .Label = c("Affective", "Control", "Development", 
"Organic", "Personality", "Physiology", "Psychosis", "Stress", 
"Substance"), class = "factor"), Sex_Group = structure(c(3L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("0", "1", "2"), class = "factor"), Group_Num = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L), .Label = c("1", "99"), class = "factor"), Age = c(24, 
23, 44, 48, 35, 56, 64, 29, 20, 62, 35, 31, 32, 60, 57, 66, 46, 
18, 52, 63, 64, 35, 54, 58, 61, 52, 52, 33, 49, 28, 22, 27, 40, 
53, 18, 19, 43, 44, 26, 28, 38, 18, 50, 45, 23, 38, 50, 36, 72, 
62, 33, 28, 29, 42, 48, 42, 29, 70, 27, 33, 22, 62, 67, 20, 32, 
22, 32, 67, 55, 49, 19, 52, 20, 30, 24, 18, 24, 23, 22, 19, 20, 
29, 22, 19, 21, 18, 22, 22, 18, 24, 22, 24, 19, 25, 24, 25, 20, 
21, 23, 39), FTND = c(5, 7, 0, 6, 0, 6, 0, NA, 3, 4, 0, 7, NA, 
0, 4, 3, 4, 1, 0, 6, 0, 5, 0, NA, NA, 3, 0, 2, NA, 0, 0, 0, NA, 
NA, NA, NA, NA, 4, 0, 10, NA, NA, 8, NA, 3, 7, 0, 0, 5, 2, 0, 
6, 7, 0, 4, 2, 0, NA, 0, 0, 0, 0, 0, 0, 4, 0, 0, NA, NA, NA, 
NA, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, NA, 0, 0, 0, 1), AUDIT = c(12, 19, 7, 2, 0, 6, 
0, NA, 4, 0, 0, 5, NA, 0, 9, 0, 0, 0, 0, 0, 0, 4, 3, NA, NA, 
5, 5, 13, NA, 2, 0, 2, NA, NA, 1, NA, NA, 1, 2, 8, NA, NA, 2, 
NA, 3, 0, NA, 4, NA, 4, 0, 9, 4, 3, 5, 7, 17, NA, 15, 0, 2, 11, 
19, 4, 8, 1, 2, NA, NA, NA, NA, 1, 4, 1, 4, 12, 4, 6, 5, 6, 4, 
3, 1, 5, 4, 4, 4, 2, 2, 4, 4, 4, 7, 0, 3, 2, 8, 12, 3, 0), DUDIT = c(4, 
18, 0, 8, 0, 0, 0, NA, 0, 0, 0, 5, NA, 0, 0, 0, 0, 4, 0, 0, 0, 
0, 0, NA, NA, 0, 0, 0, NA, 1, 0, 0, NA, NA, 0, NA, NA, 2, 0, 
4, NA, NA, 0, NA, 0, 0, 0, 0, NA, 0, 0, 13, 0, 5, 0, 10, 0, NA, 
0, 0, 5, 0, 0, 0, 4, 0, 0, NA, NA, NA, NA, 0, 5, 0, 2, 0, 0, 
0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 9, 
0, 0), EmQ = c(NA, 43, 49, 42, 56, 39, 45, NA, 61, 45, 43, 36, 
NA, 44, 41, 38, 51, 47, 19, 21, 64, 37, 40, NA, NA, 34, NA, NA, 
NA, 51, 54, NA, NA, NA, NA, NA, NA, 50, 45, 22, NA, NA, 39, NA, 
50, 60, 46, 44, 47, 28, 45, 32, 24, 40, 30, NA, 21, NA, 53, 25, 
25, NA, NA, 19, 46, NA, NA, NA, NA, NA, NA, 45, 44, 37, 43, 47, 
64, 30, 56, 55, 66, 57, 45, 52, 57, 48, 59, 48, 48, 42, 60, 34, 
48, 48, 60, 41, 46, 56, 34, 50), EmQ10 = c(12, 9, 11, 9, 12, 
8, 9, NA, 16, 13, 11, 7, NA, 12, 7, 6, 13, 13, 4, 3, 15, 11, 
8, NA, NA, 8, 9, 9, NA, 11, 16, 11, 7, NA, 5, NA, NA, 15, 10, 
8, NA, NA, 7, NA, 13, 20, NA, 10, 10, 7, 7, 8, 6, 9, 4, 7, 7, 
NA, 13, 6, 5, 3, 10, 3, 8, 7, 5, NA, NA, NA, NA, 10, 11, 7, 9, 
9, 18, 8, 15, 14, 19, 14, 13, 16, 13, 9, 17, 10, 11, 9, 14, 6, 
15, 13, 16, 10, 11, 16, 8, 12), TAS_EmotionIdentification = c(3, 
2.71, 2.14, 1.71, 3.29, 3.43, 2.29, NA, 2, 1.71, 2, 3, NA, 2.29, 
4.14, 2.29, 2, 2.71, 1.71, 2.43, 2, 2.43, 2.14, NA, NA, 3.14, 
2.43, 1.29, NA, 2.57, 1, 3.57, 1.71, NA, 1.86, NA, NA, 1, 1.71, 
2.29, NA, NA, 3.71, NA, 3.43, 2.71, 2.43, 2.43, NA, 3.71, 1.14, 
4.14, 1.86, 3, 2.29, 1.57, 3, NA, 2.14, 2.86, 3.71, 2.14, 1.14, 
3.57, 2.14, 2.57, 3.71, NA, NA, NA, NA, 1, 1.29, 2.14, NA, 2, 
2.43, 3.43, 1, 2.14, 2.29, 1.57, 1.57, 1.71, 1.29, 1.43, 1, 1, 
1.43, 1.29, 1.43, 1.57, 1.86, 1, 1.14, 1.86, 1.43, 3.57, 1.43, 
1), TAS_Alexithymia = c(2.44, 2.33, 2.78, 2.5, 3, 3.33, 2.83, 
NA, 2.06, 2.28, 2.22, 3.17, NA, 2.61, 3.78, 2.11, 2.39, 2.72, 
2.28, 2.71, 2.17, 2.78, 2.33, NA, NA, 2.11, 2.83, 1.78, NA, 2.11, 
1.89, 2.94, 2.22, NA, 2.44, NA, NA, 1.83, 2.17, 2.33, NA, NA, 
3.17, NA, 3.17, 1.89, 2.44, 2.83, NA, 3, 1.83, 3.5, 2.67, 3.22, 
2.83, 1.78, 3.06, NA, 2.56, 2.78, 2.78, 2.67, 1.67, 3.56, 2.28, 
3.28, 3.33, NA, NA, NA, NA, 1.83, 2, 2.22, NA, 2.67, 3, 3.06, 
1.72, 2.39, 2.61, 2.22, 1.94, 2.28, 2.17, 2, 1.61, 1.94, 2.17, 
2.5, 1.78, 2.17, 1.89, 1.44, 1.5, 2.44, 2.33, 3.17, 2.39, 1.39
), OLIFE_UnEx = c(NA, 22, 25, 2, 13, 16, 12, NA, 22, 1, 12, 16, 
NA, 17, 12, 8, 16, 18, 12, 11, 8, 11, 4, NA, NA, 14, NA, NA, 
NA, 22, 3, 11, NA, NA, NA, NA, 11, 11, 0, 10, NA, NA, 13, NA, 
2, 26, 2, 7, 5, 14, 3, 17, 16, 8, 14, NA, 13, NA, 4, 10, 6, NA, 
NA, 2, 12, NA, NA, NA, NA, NA, NA, 6, 2, 5, NA, 5, 19, 16, 1, 
2, 4, 5, 10, 7, 6, 0, 0, 1, 2, 3, 1, 7, 5, 2, 3, 4, 7, 14, 5, 
2), OLIFE_CogDis = c(NA, 21, 19, 8, 12, 20, 21, NA, 16, 2, 3, 
21, NA, 14, 13, 4, 17, 10, 12, NA, 17, 16, 12, NA, NA, 11, NA, 
NA, NA, 21, 0, 13, NA, NA, NA, NA, 10, 8, 9, 18, NA, NA, NA, 
NA, 4, 14, 7, 4, 5, 15, 12, NA, 21, 19, 23, NA, 15, NA, 11, 19, 
15, NA, NA, 18, 13, NA, NA, NA, NA, NA, NA, 9, 9, 7, NA, 18, 
9, 15, 4, 8, 15, 8, 11, 9, 10, 12, 2, 0, 10, 13, 2, 8, 9, 2, 
7, 16, 15, 20, 8, 1), OLIFE_IntAn = c(NA, 14, 17, 17, 8, 17, 
11, NA, 9, 8, 4, 14, NA, 7, 15, 6, 8, 7, 7, 11, 6, 9, 9, NA, 
NA, 3, NA, NA, NA, 2, 6, 5, NA, NA, NA, NA, 11, 16, 9, 16, NA, 
NA, NA, NA, 9, 8, 4, 6, 6, 5, 2, 11, 9, 11, 15, NA, 9, NA, 9, 
17, 11, NA, NA, 16, 3, NA, NA, NA, NA, NA, NA, 19, 0, 8, NA, 
4, 3, 11, 7, 1, 5, 6, 5, 8, 0, 1, 2, 3, 3, 1, 0, 2, 4, 3, 3, 
11, 2, 2, 6, 4), OLIFE_ImpNon = c(NA, 8, 13, 2, 1, 5, 0, NA, 
8, 3, 4, 4, NA, 5, 5, 1, 5, 5, 8, 7, 2, 9, 5, NA, NA, 7, NA, 
NA, NA, 6, 2, 2, NA, NA, NA, NA, 15, 4, 4, 9, NA, NA, 8, NA, 
3, 8, 4, 2, 6, 6, 3, 15, 9, 5, 4, NA, 5, NA, 4, 7, 11, NA, NA, 
6, 10, NA, NA, NA, NA, NA, NA, 5, 10, 5, NA, 7, 5, 6, 6, 4, 3, 
3, 8, 4, 4, 2, 6, 4, 2, 7, 4, 8, 5, 5, 4, 9, 3, 9, 7, 1), OLIFE_UnEx_Short = c(7, 
9, 11, 0, 4, 6, 6, NA, 9, 0, 4, 7, NA, 7, 4, 3, 5, 4, 2, 7, 2, 
2, 0, NA, NA, 6, 0, 8, NA, 7, 0, 4, 0, 11, 2, NA, 4, 4, 0, 4, 
NA, NA, 5, NA, 0, 11, 2, 2, 1, 7, 1, 7, 6, 5, 4, 3, 4, NA, 3, 
3, 2, 5, 0, 2, 7, 7, 5, NA, NA, NA, NA, 4, 1, 1, NA, 3, 8, 6, 
0, 1, 0, 1, 3, 1, 1, 0, 0, 0, 1, 0, 0, 2, 3, 1, 1, 0, 2, 6, 2, 
1), OLIFE_CogDis_Short = c(11, 9, 9, 2, 2, 8, 10, NA, 5, 0, 0, 
10, NA, 6, 6, 2, 7, 4, 7, NA, 6, 6, 5, NA, NA, 4, 3, 8, NA, 9, 
0, 4, 8, 11, 10, NA, 3, 3, 3, 6, NA, NA, NA, NA, 0, 5, 4, 3, 
3, 6, 5, NA, 10, 9, 10, 3, 8, NA, 4, 8, 6, 6, 2, 8, 6, 10, 10, 
NA, NA, NA, NA, 4, 5, 4, NA, 8, 2, 9, 3, 4, 5, 4, 7, 3, 3, 5, 
2, 0, 5, 5, 0, 5, 2, 1, 2, 5, 7, 8, 4, 0), OLIFE_IntAn_Short = c(1, 
5, 6, 6, 1, 5, 3, NA, 1, 2, 1, 4, NA, 3, 4, 2, 2, 2, 3, 2, 1, 
4, 3, NA, NA, 1, 2, 1, NA, 0, 2, 1, 3, 3, 1, NA, 3, 5, 3, 7, 
NA, NA, 4, NA, 1, 2, 0, 1, 2, 3, 0, 4, 3, 4, 6, 2, 4, NA, 2, 
5, 3, 4, 1, 6, 0, 4, 3, NA, NA, NA, NA, 7, 0, 2, NA, 2, 1, 6, 
1, 1, 1, 2, 3, 3, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 2, 0, 1, 2, 
1), OLIFE_ImpNon_Short = c(7, 4, 7, 0, 1, 1, 0, NA, 4, 1, 2, 
1, NA, 4, 3, 0, 3, 2, 2, 1, 1, 5, 3, NA, NA, 3, 4, 2, NA, 4, 
0, 1, 2, 5, 2, NA, 6, 4, 0, 5, NA, NA, 4, NA, 2, 5, 3, 1, 2, 
2, 0, 7, 5, 1, 0, 1, 2, NA, 1, 2, 4, 0, 2, 1, 6, 2, 4, NA, NA, 
NA, NA, 1, 3, 2, NA, 4, 1, 3, 2, 3, 1, 1, 4, 0, 2, 0, 0, 1, 0, 
2, 0, 3, 1, 1, 3, 5, 3, 4, 2, 1), SCSQ_VerbMem = c(9, 8, 8, 8, 
6, 10, 9, 9, 8, 7, 10, 9, 9, 8, 9, 10, 10, 9, 10, 9, 8, 10, 9, 
NA, 9, 8, 8, NA, NA, 9, 7, 10, 8, NA, 10, NA, 10, 9, 10, 8, 7, 
NA, 10, 7, 10, 7, 6, 9, 7, 7, 7, 10, 7, 9, 7, NA, 8, 8, 10, 7, 
9, 8, 9, 10, 8, 6, 8, NA, NA, NA, NA, 9, 9, 10, 10, 9, 9, 8, 
9, 10, 9, 8, 10, 7, 8, 10, 9, 10, 10, 9, 8, 10, 10, 9, 10, 10, 
9, 7, 9, 7), SCSQ_SchemInf = c(8, 6, 7, 9, 8, 9, 7, 8, 9, 6, 
8, 9, 6, 6, 8, 7, 5, 9, 8, 6, 7, 9, 9, NA, 6, 8, 8, NA, NA, 9, 
7, 9, 7, NA, 8, NA, 6, 7, 8, 9, 6, NA, 9, 5, 7, 6, 5, 7, 8, 4, 
4, 9, 7, 9, 6, NA, 6, 6, 9, 5, 7, 9, 8, 9, 9, 6, 8, NA, NA, NA, 
NA, 8, 9, 8, 7, 8, 8, 9, 9, 8, 9, 9, 9, 9, 6, 9, 9, 7, 9, 7, 
9, 8, 9, 9, 8, 8, 9, 7, 8, 8), SCSQ_TOM = c(10, 6, 8, 7, 3, 6, 
5, 4, 6, 5, 7, 9, 6, 6, 9, 6, 7, 8, 7, 8, 7, 7, 6, NA, 6, 4, 
7, NA, NA, 7, 5, 7, 5, NA, 8, NA, 7, 7, 8, 9, 4, NA, 8, 8, 5, 
4, 5, 8, 6, 6, 7, 6, 6, 9, 5, NA, 8, 2, 8, 5, 9, 9, 8, 7, 7, 
5, 5, NA, NA, NA, NA, 8, 8, 8, 8, 7, 5, 10, 7, 7, 4, 7, 8, 8, 
7, 6, 7, 8, 6, 7, 9, 5, 6, 7, 6, 7, 6, 7, 7, 7), SCSQ_MetaCog = c(2, 
3, 2, 1.666666667, 1.5, 2.5, 2.333333333, 1, 3, 2, 2, 3, 1.5, 
2, 2, 0, 1.5, 1.5, 0.6666666667, 3, 3, 2.5, 3, NA, 2.333333333, 
2.25, 2, NA, NA, 1, 3, 1, 1.666666667, NA, 3, NA, 2.5, 2.5, 1.5, 
0.5, 1.5, NA, 0, 1, 2, 2.333333333, 2, 1.5, 2.333333333, 2.5, 
1.5, 2, 1.5, 1, 3, NA, 0, 0.1666666667, 2, 1, 1, 0, 0, 2, 1, 
1, 1.666666667, NA, NA, NA, NA, 2, 2, 2, 0, 2.5, 0.5, 0, 2, 2.5, 
1.4, 1.333333333, 0, 0, 1, 2.5, 2, 1.5, 2, 3, 0, 2.25, 2, 1.5, 
2.5, 1.5, 3, 1, 1, 3), SCSQ_HoBias = c(2, 3, 2, 1.666666667, 
1.5, 2.5, 2.333333333, 1, 3, 2, 2, 3, 1.5, 2, 2, 0, 1.5, 1.5, 
0.6666666667, 3, 3, 2.5, 3, NA, 2.333333333, 2.25, 2, NA, NA, 
1, 3, 1, 1.666666667, NA, 3, NA, 2.5, 2.5, 1.5, 0.5, 1.5, NA, 
0, 1, 2, 2.333333333, 2, 1.5, 2.333333333, 2.5, 1.5, 2, 1.5, 
1, 3, NA, 0, 0.1666666667, 2, 1, 1, 0, 0, 2, 1, 1, 1.666666667, 
NA, NA, NA, NA, 2, 2, 2, 0, 2.5, 0.5, 0, 2, 2.5, 1.4, 1.333333333, 
0, 0, 1, 2.5, 2, 1.5, 2, 3, 0, 2.25, 2, 1.5, 2.5, 1.5, 3, 1, 
1, 3), SCSQ_Total = c(91.1, 64.51612903, 77.76666667, 79.125, 
57.51515152, 81.70967742, 67.6875, 72.03225806, 74.19354839, 
60.19354839, 84.43333333, 90, 70.96774194, 65.59375, 87.76666667, 
80, 74.16129032, 87.06451613, 85.40625, 76.66666667, 68.75, 84.93548387, 
77.41935484, NA, 67.6875, 63.60606061, 76.32258065, NA, NA, 85.53333333, 
59.375, 87.46875, 66.625, NA, 86.66666667, NA, 75.25806452, 71.6969697, 
87.06451613, 89.22580645, 58.06451613, NA, 93.10344828, 68.77419355, 
73.09677419, 52.34285714, 51.37142857, 80.61290323, 67.6875, 
55.90322581, 61.29032258, 84.43333333, 67.70967742, 92.2, 56.25, 
NA, 75.86206897, 61.88571429, 89.22580645, 59.57575758, 84.93548387, 
89.65517241, 86.20689655, 86, 81.67741935, 59.51515152, 69.75, 
NA, NA, NA, NA, 82.77419355, 87.76666667, 87.76666667, 86.20689655, 
78.48387097, 76.32258065, 93.10344828, 82.77419355, 81.70967742, 
72.47058824, 80.15625, 93.10344828, 82.75862069, 71.84375, 81.70967742, 
82.77419355, 83.83870968, 82.77419355, 74.19354839, 89.65517241, 
72.6969697, 82.77419355, 83.83870968, 78.48387097, 83.83870968, 
80, 72, 81.21875, 68.75), ASR_AnxDep = c(25, 8, 28, 6, 15, 21, 
23, NA, 27, 0, 5, 25, NA, 5, 20, 6, 17, 16, 10, NA, 18, 18, 13, 
NA, NA, 12, 14, 7, NA, 25, 2, 16, 11, NA, 8, NA, 14, 6, 12, 19, 
NA, NA, 19, NA, 6, 0, 16, 1, NA, 8, 8, 16, 12, 18, 14, 5, 18, 
NA, 13, 17, 20, 14, NA, 13, 21, 19, 21, NA, NA, NA, NA, 9, 0, 
7, NA, NA, NA, 8, 5, 8, 8, 4, 3, 11, 3, 10, 5, 2, 7, 7, 1, NA, 
5, 2, 9, 18, 6, 14, 6, 0), ASR_Withdraw = c(3, 4, 13, 4, 6, 11, 
7, NA, 5, 2, 1, 10, NA, 1, 10, 4, 5, 5, 10, 6, 4, 3, 8, NA, NA, 
3, 6, 1, NA, 9, 2, 3, 5, NA, 3, NA, 9, 10, 5, 11, NA, NA, 7, 
NA, 4, 0, 3, 4, NA, 2, 2, 5, 5, 6, 7, 5, 9, NA, 2, 6, 4, 6, NA, 
9, 8, 6, 5, NA, NA, NA, NA, 10, 1, 4, NA, NA, NA, 8, 4, 2, 1, 
4, 2, 3, 1, 0, 1, 0, 0, 1, 0, NA, 0, 0, 4, 10, 1, 1, 3, 1), ASR_ThoughtProb = c(3, 
8, 3, 2, 4, 2, 6, NA, 7, 0, 2, 2, NA, 0, 3, 4, 3, 5, 6, 12, NA, 
4, 5, NA, NA, 3, 2, 6, NA, 6, 0, 2, 2, NA, 5, NA, 9, 0, 0, 7, 
NA, NA, 7, NA, 5, 2, 4, 4, NA, 6, 1, 5, 5, 7, 2, 3, 10, NA, 4, 
1, 10, 2, 1, 2, 17, NA, 4, NA, NA, NA, NA, 1, 2, 4, NA, NA, NA, 
7, 1, 3, 2, 1, 4, 3, 0, 2, 0, 0, 1, 0, 0, NA, 1, 4, 3, 3, 0, 
6, 0, 0), ASR_Internalizing = c(NA, 16, 54, 11, 31, 38, 39, NA, 
33, 2, 9, 41, NA, 6, 44, 11, 30, 27, 22, NA, NA, 28, 24, NA, 
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39, NA, 25, 32, 32, 23, NA, 28, 42, NA, 31, NA, NA, NA, NA, 19, 
2, 17, NA, NA, NA, 20, 10, 15, 13, 13, 14, 17, 7, 14, 6, 2, 8, 
8, 4, NA, 9, 2, 18, 32, 7, 23, 11, 1), ASR_Externalizing = c(20, 
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36, 6, 5, 24, NA, NA, 17, NA, 10, 2, NA, 2, NA, 14, 5, 26, 17, 
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11, 1, 6, 18, NA, NA, 14, NA, 5, 0, 13, 1, NA, 6, 3, 14, 8, 13, 
10, 4, 14, NA, 9, 10, 8, 7, NA, 11, 15, 16, 15, NA, NA, NA, NA, 
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0, NA, 8, 1, 10, 10, 2, 9, 3, 0), ASR_Education = c(NA, NA, 4, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8, 6, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, 10, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10, 
NA, NA, NA, NA, NA, 5, NA, NA, NA, 7, NA, NA, 3, NA, NA, NA, 
NA, NA, NA, NA, 9, 9, 9, NA, NA, NA, 3, 8, 10, 9, NA, 8, 10, 
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14, 14, 1, 5, 9, 4, NA, 11, 0, 5, 5, NA, 7, 5, 10, 7, 5, 16, 
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0, 3, NA, NA, 4, NA, 1, 24, 2, 1, 0, 7, 3, 9, 14, 6, 1, 5, 5, 
NA, 1, 1, 2, 1, 0, 0, NA, 9, 5, NA, NA, NA, NA, NA, NA, NA, NA, 
1, 9, 10, NA, 2, NA, 1, 1, 10, 1, 0, NA, 0, NA, NA, 2, 3, 1, 
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4, 1, NA, NA, 2, 3, 2, 7, 4, 0, NA, 3, 14, 2, 5, NA, NA, 14, 
NA, 2, 10, 1, 1, 1, 3, 3, 1, 4, 10, 10, 1, 10, NA, 1, 7, 3, 4, 
0, 14, NA, 5, 5, NA, NA, NA, NA, NA, NA, NA, NA, 2, 4, 8, NA, 
0, NA, 4, 5, 3, 0, 0, NA, 0, NA, NA, 0, 1, 0, NA, NA, 1, NA, 
0, NA, NA), MSS_Disorg = c(23, NA, NA, NA, NA, NA, NA, NA, 0, 
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NA, NA, NA, NA, NA, NA, NA, NA, NA, 16, NA, 4, 15, 1, NA, NA, 
NA, NA, NA, 0, 0, 16, NA, 17, 11, NA, NA, NA, NA, NA, NA, NA, 
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-0.0593, 0.2235, -0.0335, -0.2998, -0.0615), R_AMYG = c(-0.5309, 
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0.068, 0.1151, 0.0667, -0.0715, -0.2093, -0.0106, 0.0358, 0.2127, 
-0.1133, -0.1324, -0.0139, 0.1567, 0.0959, 0.0112, 0.008, 0.0142, 
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0.0392, 0.0653, -0.0488, -0.1191, 0.0476, -0.0139, -0.1694, 0.3619, 
0.5153, -0.0549, 0.1472, 0.0607, 0.2295, -0.1706, -8e-04, -0.083, 
-0.1244, -0.0969, 0.0368, 2e-04, -0.3045, 0.1546, 0.3629, -0.1059, 
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0.2142, 0.0306, -0.0843, 0.316, -0.0256, -0.1302, 0.0715, 0.1741, 
0.0611, 0.137, -0.2914, -0.3268, -0.0631), L_aTEMPORAL = c(-0.0301, 
0.1187, 0.1987, -0.01, -0.0467, 0.149, -0.0965, 0.3232, 0.114, 
0.0327, 0.1568, 0.2586, -0.0808, 0.1651, 0.1069, -0.0786, 6e-04, 
0.1679, 3e-04, -0.1272, -0.0771, 0.0847, 0.0324, 0.0785, -0.1063, 
0.1185, 0.0185, 0.0796, 0.0796, 0.0615, 0.0652, 0.0874, 0.2335, 
0.0455, 0.0289, -0.1491, 0.0024, 0.1571, 0.2195, 0.1254, 0.3199, 
0.1374, 0.196, 0.1324, 0.1145, 0.1764, 0.0929, 0.084, 0.2013, 
0.0646, -0.1193, 0.1997, -0.0325, 0.1518, -0.0687, 0.1533, -0.0532, 
0.188, 0.0321, -0.0233, 0.1959, 0.1668, 0.0456, 0.1866, 0.3929, 
-0.1085, 0.1955, 0.0883, 0.269, 0.1775, 0.2185, 0.09, 0.2204, 
0.1223, 0.1842, 0.1668, -0.1406, 0.179, 0.4016, 0.1622, 0.1703, 
0.1679, 0.1321, 0.1369, 0.0923, 0.1127, -0.128, 0.3241, 0.0652, 
-0.1252, -0.0099, -0.1216, 0.0362, 0.1024, 0.2562, 0.1174, 0.1801, 
0.0119, 0.0868, 0.1153), R_aTEMPORAL = c(-0.2239, 0.0547, 0.0804, 
-0.0932, -0.0231, 0.2141, -0.0641, 0.2311, 0.0626, 0.0289, 0.1422, 
0.1743, -0.0169, 0.1459, 0.0399, -0.0982, 0.0196, 0.2487, -0.01, 
-0.0761, -0.0169, 0.0222, 0.0662, 0.0188, -0.1485, 0.1344, 0.2303, 
0.1155, 0.1033, -0.0339, 0.0996, 0.118, 0.1558, 0.0101, -0.0123, 
0.1047, -0.0852, 0.0275, 0.1588, 0.0707, 0.2288, 0.1626, 0.2185, 
0.0806, 0.0741, 0.1184, 0.1497, 0.3015, 0.0313, 0.0255, -0.0542, 
0.0489, 0.0309, 0.0556, -0.0451, 0.0919, -0.0107, 0.046, 0.0868, 
0.0188, 0.1606, 0.1586, 0.1022, 0.273, 0.5084, -0.066, 0.2596, 
0.1371, 0.2237, 0.0714, 0.215, 0.0527, 0.1712, 0.1241, 0.2499, 
0.1802, -0.0462, 0.2023, 0.2523, 0.1661, 0.3844, 0.1296, 0.0935, 
0.1126, 0.0779, 0.0481, 0.0316, 0.3425, 0.1299, 0.0738, 0.2035, 
-2e-04, 0.0868, 0.0585, 0.2053, 0.1247, 0.0776, -0.0161, -0.0076, 
0.1101), mPARIETAL = c(0.0409, 0.205, 0.0604, 0.1205, 0.0373, 
0.1973, -0.319, 0.4948, 0.1063, 0.0858, 0.5267, 0.351, -0.0623, 
0.3925, 0.1549, 0.065, 0.2647, 0.4726, 0.1168, 0.0691, 0.101, 
0.2214, 0.2105, 0.1303, 0.1113, 0.3061, 0.0643, 0.1397, 0.3502, 
0.0457, 0.3479, 0.031, 0.4519, 0.1497, -0.0469, 0.3031, -0.1359, 
0.4121, 0.3389, 0.2197, 0.8623, 0.6276, 0.329, 0.3398, 0.0763, 
0.5494, 0.3834, 0.1228, 0.0936, -0.1259, 0.1612, 0.4462, 0.1499, 
0.2648, -0.0384, 0.4475, -0.0458, -0.0147, 0.1299, -0.0047, 0.5185, 
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0.4, 0.2344, 0.3435, 0.1454, 0.4288, 0.6463, 0.2965, -0.0382, 
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0.0342, 0.3031, 0.4241, 0.4149, 0.2992, 0.1665, 0.4392), L_TPJ = c(0.0362, 
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0.4053)), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
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), class = "data.frame")

Answer 1

仅适用于OLIFE_IntAn_Short：

library(plotly)
library(tidymodels)

y = NumericDatawoOutliers$mFRONTAL
x = NumericDatawoOutliers$OLIFE_IntAn_Short
lm_model = linear_reg() %>% 
  set_engine('lm') %>% 
  set_mode('regression') %>%
  fit(mFRONTAL ~ OLIFE_IntAn_Short, data = NumericDatawoOutliers) 
x_range = seq(min(x, na.rm = TRUE), max(x, na.rm = TRUE), length.out = 100)
x_range = matrix(x_range, nrow=100, ncol = 1)
xdf = data.frame(x_range)
colnames(xdf) = c('OLIFE_IntAn_Short')
ydf = lm_model %>% predict(xdf) 
colnames(ydf) = c('mFRONTAL')
xy = data.frame(xdf, ydf) 
fig = plot_ly(NumericDatawoOutliers, x = ~ OLIFE_IntAn_Short, y = ~mFRONTAL, type = 'scatter', alpha = 0.65, mode = 'markers', name = 'Case')
fig = fig %>% add_trace(data = xy, x = ~ OLIFE_IntAn_Short, y = ~mFRONTAL, name = 'Regression Fit', mode = 'lines', alpha = 1)
fig

​

​

下面是一个关于初始模型的输入数据的示例(将示例数据分成一半用于学习，另一半用于预测)。用于学习的所有变量(OLIFE_IntAn_Short + Age + DUDIT)都需要传递给predict

library(tidymodels)

learningData <- head(NumericDatawoOutliers, 50)
xdf <- tail(NumericDatawoOutliers, 50)

lm_model = linear_reg() %>% 
  set_engine('lm') %>% 
  set_mode('regression') %>%
  fit(mFRONTAL ~ OLIFE_IntAn_Short + Age + DUDIT, data = learningData)

ydf = lm_model %>% predict(xdf) 
colnames(ydf) <- c('mFRONTAL')
xy = data.frame(xdf, ydf)

xy <- xy[order(xy$OLIFE_IntAn_Short),]

然而，我不是建模方面的专家，而且似乎这些都不是与plotly相关的问题。