根据R中的ID对数据行执行线性回归答案

【问题标题】：Perform linear regression on rows of data based on ID in R根据R中的ID对数据行执行线性回归
【发布时间】：2022-10-12 21:32:37
【问题描述】：

我想根据 ID 号对数据框中的行应用线性回归。我想使用 ch4_ppm 列来执行此操作。例如，对于所有具有 'measurement_id'==1 的行，对此集进行回归并将结果存储在数据框中。我总共有 157 套我想重复这个。下面是我的数据集中的一个示例。

structure(list(time = c("2022-07-26 09:11:40", "2022-07-26 09:11:45",  "2022-07-26 09:11:50", "2022-07-26 09:11:55", "2022-07-26 09:12:00",  "2022-07-26 09:12:05", "2022-07-26 09:12:10", "2022-07-26 09:12:15",  "2022-07-26 09:12:20", "2022-07-26 09:12:25", "2022-07-26 09:12:30",  "2022-07-26 09:12:35", "2022-07-26 09:12:40", "2022-07-26 09:12:45",  "2022-07-26 09:12:50", "2022-07-26 09:12:55", "2022-07-26 09:13:00",  "2022-07-26 09:13:05", "2022-07-26 09:13:10", "2022-07-26 09:13:15",  "2022-07-26 09:13:20", "2022-07-26 09:13:25", "2022-07-26 09:13:30",  "2022-07-26 09:13:35", "2022-07-26 09:13:40", "2022-07-26 09:13:45",  "2022-07-26 09:13:50", "2022-07-26 09:13:55", "2022-07-26 09:14:00",  "2022-07-26 09:14:05", "2022-07-26 09:14:10", "2022-07-26 09:14:15",  "2022-07-26 09:14:20", "2022-07-26 09:14:25", "2022-07-26 09:14:30",  "2022-07-26 09:14:35", "2022-07-26 09:14:40", "2022-07-26 09:20:55",  "2022-07-26 09:21:00", "2022-07-26 09:21:05", "2022-07-26 09:21:10",  "2022-07-26 09:21:15", "2022-07-26 09:21:20", "2022-07-26 09:21:25",  "2022-07-26 09:21:30", "2022-07-26 09:21:35", "2022-07-26 09:21:40",  "2022-07-26 09:21:45", "2022-07-26 09:21:50", "2022-07-26 09:21:55",  "2022-07-26 09:22:00", "2022-07-26 09:22:05", "2022-07-26 09:22:10",  "2022-07-26 09:22:15", "2022-07-26 09:22:20", "2022-07-26 09:22:25",  "2022-07-26 09:22:30", "2022-07-26 09:22:35", "2022-07-26 09:22:40",  "2022-07-26 09:22:45", "2022-07-26 09:22:50", "2022-07-26 09:22:55",  "2022-07-26 09:23:00", "2022-07-26 09:23:05", "2022-07-26 09:23:10",  "2022-07-26 09:23:15", "2022-07-26 09:23:20", "2022-07-26 09:23:25",  "2022-07-26 09:23:30", "2022-07-26 09:28:10", "2022-07-26 09:28:15",  "2022-07-26 09:28:20", "2022-07-26 09:28:25", "2022-07-26 09:28:30",  "2022-07-26 09:28:35", "2022-07-26 09:28:40", "2022-07-26 09:28:45",  "2022-07-26 09:28:50", "2022-07-26 09:28:55", "2022-07-26 09:29:00",  "2022-07-26 09:29:05", "2022-07-26 09:29:10", "2022-07-26 09:29:15",  "2022-07-26 09:29:20", "2022-07-26 09:29:25", "2022-07-26 09:29:30",  "2022-07-26 09:29:35", "2022-07-26 09:29:40", "2022-07-26 09:29:45",  "2022-07-26 09:29:50", "2022-07-26 09:29:55", "2022-07-26 09:30:00",  "2022-07-26 09:30:05", "2022-07-26 09:30:10", "2022-07-26 09:30:15",  "2022-07-26 09:30:20", "2022-07-26 09:30:25", "2022-07-26 09:30:30",  "2022-07-26 09:30:35", "2022-07-26 09:30:40"), ch4_ppm = c(27.75, 
36.32, 51.63, 54.56, 92, 121.5, 127.6, 193.7, 189.3, 279.2, 323.2, 
368.6, 426.1, 485.7, 545.7, 492.8, 548.1, 751.6, 820, 892, 959,  1018, 1080, 1152, 1214, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,  NA, NA,
3.346, 3.672, 3.958, 8.33, 21.09, 47.68, 53.71, 150.8, 
190.6, 222.4, 267.3, 326.4, 314.5, 375.8, 435.3, 595.7, 534.3, 
740.6, 819, 873, 923, 987, 1067, 1140, 1206, NA, NA, NA, NA,  NA, NA, NA, 14.71, 14.7, 29.95, 48.91, 75.21, 102, 129.9, 131.9, 
199.3, 140, 272.2, 313.3, 354.1, 398.4, 449.3, 503.6, 557.9, 
614.2, 676, 734.4, 628.6, 672.4, 536.2, 767, 1002, 1054, 1099,  1143, 1194, NA, NA), co2_ppm = c(491, 500.3, 509.1, 517.7, 526.6, 
530.3, 534.7, 536.1, 539, 542.5, 549.8, 557.7, 570.5, 584.4, 
602.6, 622.9, 648.3, 674.4, 705.5, 736.2, 772.3, 807, 846, 883,  927, 967, 1010, 1049, 1094, 1135, 1181, 1226, 1268, 1313, 1359,  1403, 1445, 426.7, 427.2, 427, 428.5, 428.5, 429.8, 430.5, 434.4, 
438.4, 444.4, 452.9, 464.3, 477.2, 493.1, 510.4, 532.7, 556, 
584.6, 613.6, 648.9, 681.6, 719, 753.5, 794.5, 831, 874, 917,  958, 1001, 1045, 1090, 1131, 439.7, 436.8, 436, 435.3, 436.9, 
438.7, 443.4, 450.5, 457.3, 467.6, 480.3, 495.3, 512.9, 532.9, 
554.9, 578.3, 605.7, 633.1, 664.3, 692.8, 727.2, 758.5, 794.7,  828, 868, 903, 945, 983, 1023, 1062, 1104), chamber_temp_degC = c(20.85, 
20.86, 20.87, 20.88, 20.89, 20.9, 20.91, 20.91, 20.93, 20.94, 
20.96, 20.96, 20.97, 20.98, 20.99, 20.99, 21, 21.01, 21.02, 21.03, 
21.03, 21.05, 21.06, 21.06, 21.07, 21.08, 21.09, 21.09, 21.09, 
21.1, 21.11, 21.11, 21.12, 21.13, 21.13, 21.14, 21.15, 21.6, 
21.6, 21.61, 21.61, 21.62, 21.63, 21.63, 21.64, 21.65, 21.67, 
21.67, 21.67, 21.67, 21.68, 21.68, 21.69, 21.69, 21.7, 21.71, 
21.71, 21.72, 21.72, 21.73, 21.73, 21.74, 21.74, 21.75, 21.75, 
21.76, 21.76, 21.76, 21.77, 22.1, 22.12, 22.13, 22.14, 22.15, 
22.16, 22.17, 22.18, 22.19, 22.19, 22.2, 22.2, 22.21, 22.21, 
22.22, 22.22, 22.23, 22.23, 22.24, 22.24, 22.25, 22.26, 22.26, 
22.26, 22.27, 22.27, 22.27, 22.27, 22.27, 22.27, 22.27), measurement_id = c(1,  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,  2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,  2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,  3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3)), row.names = c(21L,  22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,  35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,  48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 132L, 133L,  134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L,  145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L,  156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 219L, 220L, 221L,  222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L,  233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L,  244L, 245L, 246L, 247L, 248L, 249L), class = "data.frame")

我希望输出是在每个序列的开始时间（每个 ID 的第一行中的时间戳）旁边的计算斜率的数据框，在回归计算的斜率旁边，如下所示

time          slope    measurement_id
2022-09-18    8.67     1
2022-09-18    5.34     2
...           ...

任何帮助将不胜感激：）

【问题讨论】：

你的因变量和自变量是什么？
时间是我的自变量，ch4_ppm 是我的依赖。

标签： r group-by linear-regression

【解决方案1】：

您可以使用row_number 和summarise 在lm 的coefficients 中的斜率添加一个列，该列是每个组的行ID，如下所示：

library(dplyr)
df %>% 
  group_by(measurement_id) %>% 
  mutate(rowid = row_number()) %>% 
  summarise(time = first(time), 
            slope = lm(ch4_ppm ~ rowid)$coefficients['rowid'])
#> # A tibble: 3 × 3
#>   measurement_id time                slope
#>            <dbl> <chr>               <dbl>
#> 1              1 2022-07-26 09:11:40  51.8
#> 2              2 2022-07-26 09:20:55  53.7
#> 3              3 2022-07-26 09:28:10  42.3

^{创建于 2022-10-12，reprex v2.0.2}

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