我有一個這樣的資料集：
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45.955, 43.072, 41.058, 96.478, 14.185, 40.901, 23.789),
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76.02, 67.71, 97.68, 54.93, 74.42, 55.3, 18.1, 2.06, 33.06,
10.25, 59.29, 74.78, 76.28), feature89 = c(7.33, 90.95, 76.06,
82.27, 94.44, 73.66, 20.11, 1.66, 97.32, 26.21, 10.91, 51.56,
72.5, 46.4, 1.14, 85.54, 24.05, 11.1, 10.34, 67.6), feature90 = c(53.076,
88.507, 43.996, 94.229, 18.833, 65.553, 23.558, 38.634, 77.058,
47.78, 67.097, 1.725, 84.267, 56.327, 81.975, 31.377, 28.556,
49.231, 99.165, 25.616), feature91 = c(51.777, 41.82, 50.859,
65.633, 96.74, 42.07, 99.326, 35.319, 18.219, 66.611, 97.898,
3.879, 58.617, 39.951, 37.59, 98.76, 25.39, 69.328, 48.38,
47.361), feature92 = c(6.96, 64.358, 30.942, 64.467, 82.39,
83.392, 11.737, 47.91, 4.047, 18.796, 16.315, 57.25, 92.195,
81.872, 46.367, 38.273, 13.997, 20.488, 39.599, 87.521),
feature93 = c(37.38, 31.9, 25.2, 38.71, 76.45, 4.46, 82.43,
5.5, 79.96, 29.96, 12.3, 82.56, 99.67, 93.29, 83.95, 34.29,
81.27, 38.88, 81.6, 8.2), feature94 = c(87.52, 53.81, 63.54,
61.91, 6.61, 50.97, 89.52, 98.42, 11.04, 58.36, 44.56, 31.87,
34.41, 3.48, 78.76, 9.78, 88.21, 39.77, 0.52, 17.41), feature95 = c(15.171,
57.584, 5.31, 7.044, 48.888, 15.277, 37.406, 30.006, 96.997,
30.373, 75.704, 66.61, 19.355, 44.956, 14.121, 70.713, 83.999,
66.549, 40.903, 7.43), feature96 = c(52.781, 96.875, 92.053,
10.032, 96.775, 21.113, 91.297, 78.021, 44.392, 36.631, 8.119,
12.063, 97.484, 59.671, 60.837, 68.308, 58.559, 77.111, 49.712,
75.36), feature97 = c(4.113, 56.873, 98.058, 17.523, 83.594,
86.386, 96.583, 81.796, 29.536, 14.735, 49.08, 33.498, 54.154,
31.97, 14.861, 33.095, 57.756, 39.089, 27.392, 5.017), feature98 = c(46.713,
34.426, 33.162, 47.504, 24.766, 31.063, 2.875, 40.256, 71.992,
22.2, 85.934, 77.418, 23.695, 44.463, 37.19, 89.376, 28.426,
26.929, 71.987, 20.238), feature99 = c(58.47, 11.38, 68.43,
99.25, 53.5, 96.66, 67.14, 29.46, 35.84, 17.53, 54.88, 50.55,
19.38, 63.69, 68.78, 64.02, 35.79, 10.26, 9.78, 18.29), feature100 = c(30.78,
25.77, 55.23, 5.64, 46.85, 48.38, 81.24, 37.03, 54.66, 17.03,
62.5, 88.22, 28.04, 39.85, 76.26, 66.9, 20.46, 35.75, 35.95,
69.03)), row.names = c(NA, -20L), class = "data.frame")
現在我需要運行 glm 模型并檢索具有結果的每個變數的 p 值 < 0.05：狀態。我正在嘗試使用回圈來實作它，但我無法寫出正確的。
我的想法是首先，我創建一個串列來保存 glm 模型代碼的所有結果，然后，使用另一個串列來存盤“摘要”中的所有 p<-value，然后使用過濾器過濾掉 >0.05 的記錄。
for (i in colnames(df2)){
list_glm<-list()
z<-list()
list_glm<-glm(status~i, data =df2, family = binomial())
z<-summary(list_glm)$coefficients[,4]
}
有人可以幫忙弄清楚嗎？非常感謝~~！
uj5u.com熱心網友回復：
我會從寬到長，嵌套資料，然后同時運行回歸。然后，您可以繪制模型的 p 值并過濾掉 p < 0.05 的特征。看起來有 4 個模型符合您的示例資料的標準。
library(tidyverse)
df |>
pivot_longer(cols = -status) |>
nest(data = -name) |>
mutate(mod = map(data, ~glm(status~value, data = .x, family = binomial())),
p.value = map_dbl(mod, ~summary(.x)$coefficients[2,4])) |>
select(name, p.value) |>
filter(p.value < 0.05)
#> # A tibble: 4 x 2
#> name p.value
#> <chr> <dbl>
#> 1 feature10 0.0370
#> 2 feature34 0.0243
#> 3 feature41 0.0189
#> 4 feature86 0.0498
uj5u.com熱心網友回復：
list_glm<-list()
z<-list()
for (i in colnames(df2)[2:length(colnames(df2)]){
formula <- paste0("status ~", i)
list_glm[[i]] <- glm(formula = formula, data =df2, family = binomial())
z[[i]] <-summary( list_glm[[i]])$coefficients[,4]
}
轉載請註明出處，本文鏈接：https://www.uj5u.com/qita/515269.html
標籤：r
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