如何将这个函数应用于r中的数据框架中的多个元素？ [英] How to apply this function for multiple elements in a data frame in r?

问题描述

背景

分段包提供一个功能分段可以适合分段线性回归。以下是语法：

  segmented（obj，seg.Z，psi，control = seg.control（），
 model = TRUE，...）
  

主要是一个 lm 需要 obj ， seg.Z 包含独立变量和 psi 包含断点列表。

单个元素（作品）的示例

我有一个包含多个车辆的数据帧，包含速度）和时间。每个车辆都有一个唯一的ID，称为 Vehicle.ID2 。以下是使用分段的数据和代码 Vehicle.ID2 ==430-419（命名为 veh ）：

 > dput（veh％>％select（Tim，svel））
 structure（list（Tim = c（169.7,169.8,169.9,170,170.1,170.2，
 170.3,170.4,170.5,170.6， 170.7,170.8,170.9,171,171.1，
 171.2,171.3,171.4,171.5,171.6,171.7,171.8,171.9,172，
 172.1,172.2,172.3,172.4,172.5,172.6,172.7， 172.8,172.9，
 173，173.1，173.2，173.3，173.4，173.5，173.6，173.7，173.8，
 173.9,174,174.1,174.2,174.3,174.4,174.5,174.6,174.7，
 174.8,174.9,175,175.1,175.2,175.3,175.4,175.5,175.6，
 175.7,175.8,175.9,176,176.1,176.2,176.3,176.4,176.5，
 176.6,176.7， 176.8,176.9,177,177.1,177.2,177.3,177.4，
 177.5，177.6，177.7，177.8，177.9，178，178.1，178.2，178.3，
 178.4,178.5,178.6,178.7,178.8， 178.9,179,179.1,179.2，
 179.3,179.4,179.5,179.6,177.7,179.8,179.9,180,180.1，
 180.2,180.3,180.4,180.5,180.6,180.7,180.8,180.9， 181，
 181.1，181.2，181.3，181.4，181.5，181.6，181.7，181.8，181.9，
 182，182.1,182。 2,182.3,182.4,182.5,182.6,182.7,182.8，
 182.9,183,183.1,183.2,183.3,183.4,183.5,183.6,183.7，
 183.8,183.9,184,184.1,184.2， 184.3,184.4,184.5,184.6，
 184.7,184.8,184.9,185,185.1,185.2），svel = c（21.88,22.11，
 22.18,22.19,22.22,21.94,21.67,21.32,20.94 ，20.55,20.21，
 19.93，19.72，19.55，19.4，19.28，19.19,19.13,19.1,19.08，
 19.1,19.17,19.27,19.4,19.52,19.63,19.72，19.79，19.85，
 19.92，19.98，20.04，20.08，20.1，20.1，20.09，20.09，20.08，
 20.06,20.05,20.05,20.09,20.17,20.31,20.49,20.69,208.88，
 21.04,21.15 ，21.23,21.27,21.29,21.29,21.28,21.25,21.2，
 21.12,20.99,20.83,20.68,20.6,20.59,20.62,20.69,20.79，
 20.91,21.05,21.21,21.38,21.58 ，21.79，22.01，22.25，22.5，
 22.75，23.01，23.26，23.52，23.75，23.96，24.13，24.23，24.29，
 24.32，24.34，24.35，24.36，24.35，24.33，24.29，24.24 ，24.18，
 24.11，24.04，23.98，23.93，23.86，23.78，23.66，23.53，23.38， 
 23.24，23.12，23.04，23.01，23.02，23.08，23.17，23.29，23.42，
 23.56，23.68，23.8，23.92，24.05，24.17，24.28，24.38，24.49，
 24.62， 24.79,25,25.23,25.45,25.65,25.82,25.96,26.05,26.1，
 26.11,26.06,25.95,25.8,25.67,25.15,24.99,24.85，
 24.74,24.66,24.62， 24.64,24.72,24.87,25.08,25.33,25.6，
 25.86,26.11,26.34,26.55,26.73,26.86,26.96,27,26.98））row.names = c（NA，
 -156L ），class = c（tbl_df，tbl，data.frame），.Names = c（Tim，
svel））
  





代码：

  my.lm< lm（svel〜Tim，data = veh）
库（分段）
 my.seg<  -  segmented（my.lm，
 seg.Z =〜Tim，
 psi =列表（Tim = c（171.5,172.8,173.8,174.6,175.3,175.8,177.7,178.6,180.1,182.6,183.9,185.1）））
  

完整数据框的问题：

您可以在上面的代码中看到速度（svel），时间（Time，不是需要完整的数据帧）和psi。我使用 dplyr 来估计数据框架中每辆车辆的 lm df1015

具有2个车辆的数据框：

  > dput（df10152）
结构（list（Vehicle.ID2 = c（6-4，6-4，6-4，6-4，6-4，
6-4，6-4，6-4，6-4，6-4，6-4，6-4，6-4 6-4，
6-4，6-4，6-4，6-4，6-4，6-4，6-4 ，6-4，6-4，
6-4，6-4，6-4，6-4，6-4 -4，6-4，6-4，6-4，
6-4，6-4，6-4，6-4 6-4，6-4，6-4，6-4，6-4，
6-4，6-4 6-4，6-4，6-4，6-4，6-4，6-4，
6-4 -4，6-4，6-4，6-4，6-4，6-4，6-4，6-4 6-4，6-4，6-4，6-4，6-4，6-4，6-4，6-4 -4，
6-4，6-4，6-4，6-4，6-4，6-4，6-4 6-4，6-4，
6-4，6-4，6-4，6-4，6-4 ，6-4，6-4，6-4，
6-4，6-4，6-4，6-4 -4，6-4，6-4，6-4，6-4，
6-4，6-4，6-4 6-4，6-4，6-4，6-4，6-4，6-4，
6-4，6-4 ，6-4，6-4，6-4，6-4，6-4，6-4，6-4，
6 -4，6-4，6-4，6-4，6-4，6-4，6-4，6-4 ，
6-4，6-4 ，6-4，6-4，6-4，6-4，6-4，6-4，6-4，
 4，6-4，6-4，6-4，6-4，6-4，6-4 ，
6-4，6-4，6-4，6-4，6-4，6-4，6-4 4，6-4，
6-4，6-4，6-4，6-4，6-4，6-4 6-4，6-4，6-4，
6-4，6-4，6-4，6-4 ，6-4，6-4，6-4，6-4，
6-4，6-4，6-4 4，6-4，6-4，6-4，6-4，6-4，
6-4，6-4 6-4，6-4，6-4，6-4，6-4，6-4，6-4，
6-4 ，6-4，6-4，6-4，6-4，6-4，6-4，6-4，6-4 b $ b6-4，6-4，6-4，6-4，6-4，6-4，6-4 ，6-4，
6-4，6-4，6-4，6-4，6-4，6-4 4，6-4，6-4，
6-4，6-4，6-4，6-4 6-4，6-4，6-4，6-4，
6-4，6-4，6-4 ，6-4，6-4，6-4，6-4，6-4，
6-4，6-4 4，6-4，6-4，6-4，6-4，6-4，6-4，
6-4 6-4，6-4，6-4，6-4，6-4，6-4，6-4，6-4 b6-4，6-4，6-4，6-4，6-4，6-4，6-4 ，6-4，6-4，
6-4，6-4，6-4，6-4，6-4 -4，6-4，6-4，6-4，
6-4，6-4，6-4，6-4 6-4，6-4，6-4，6-4，6-4，
6-4，6-4 ，6-4，6-4，6-4，6-4，8-5，8-5，
8-5 -5，8-5，8-5，8-5，8-5，8-5，8-5，8-5，
 8-5，8-5，8-5，8-5，8-5，8-5，8-5，8-5 -5，
8-5，8-5，8-5，8-5，8-5，8-5，8-5 8-5，8-5，
8-5，8-5，8-5，8-5，8-5 ，8-5，8-5，8-5，
8-5，8-5，8-5，8-5 -5，8-5，8-5，8-5，8-5，
8-5，8-5 8-5，8-5，8-5，8-5，8-5，8-5，
8-5 ，8-5，8-5，8-5，8-5，8-5，8-5，8-5，
8 -5，8-5，8-5，8-5，8-5，8-5，8-5，8-5 ，
8-5，8-5，8-5，8-5，8-5，8-5，8-5 -5，8-5，
8-5，8-5，8-5，8-5，8-5，8-5 8-5，8-5，8-5，
8-5，8-5，8-5，8-5 ，8-5，8-5，8-5，8-5，
8-5，8-5 ，8-5，8-5，8-5，8-5，8-5，8-5，8-5，
8 -5，8-5，8-5，8-5，8-5，8-5，8-5，8-5 ，
8-5，8-5，8-5，8-5，8-5，8-5，8-5 -5，8-5，
8-5，8-5，8-5，8-5，8-5，8-5 8-5，8-5，8-5，
8-5，8-5，8-5，8-5 8-5，8-5，8-5，8-5，
8-5，8-5，8-5 -5，8-5，8-5，8-5，8-5，8-5，
8-5，8-5 8-5，8-5，8-5，8-5，8-5，8-5，8-5，
8-5 8-5，8-5，8-5，8-5，8-5，8-5，8-5 
8-5，8-5，8-5，8-5，8-5，8-5，8-5 ，8-5，
8-5，8-5，8-5，8-5，8-5，8-5 -5，8-5，8-5，
8-5，8-5，8-5，8-5 8-5，8-5，8-5，8-5，
8-5，8-5，8-5 8-5，8-5，8-5，8-5，8-5，
8-5，8-5 -5，8-5，8-5，8-5，8-5，8-5，8-5，
8-5 8-5，8-5，8-5，8-5，8-5，8-5，8-5，8-5 $ b8-5，8-5，8-5，8-5，8-5，8-5 5，8-5，8-5，
8-5，8-5，8-5，8-5 8-5，8-5，8-5，8-5，
8-5，8-5，8-5 ，8-5，8-5，8-5，8-5，8-5，
8-5，8-5 5，8-5，8-5，8-5，8-5，8-5，8-5，
8-5 8-5，8-5，8-5，8-5，8-5，8-5，8-5，8-5 b8-5，8-5，8-5，8-5，8-5，8-5，8-5，8-5 8-5，
8-5，8-5，8-5，8-5，8-5，8-5 ，8-5，8-5，
8-5，8-5，8-5，8-5，8-5 5，8-5，8-5，8-5，
8-5，8-5，8-5 8-5，8-5，8-5，8-5，8-5，
8-5，8-5 ，8-5，8-5，8-5，8-5，8-5，8-5，
8-5 5，8-5，8-5，8-5，8-5，8-5，8-5，8-5，
 8-5，8-5，8-5，8-5，8-5，8-5，8-5 5，
8-5，8-5，8-5，8-5，8-5，8-5，8-5 8-5，8-5，8-5，8-5，8-5，8-5，8-5 ，8-5，8-5，8-5，
8-5，8-5，8-5，8-5 5，8-5，8-5，8-5，8-5，
8-5 5，8-5，8-5，8-5，8-5，8-5，8-5，8-5，
 8-5，8-5，8-5，8-5，8-5，8-5，8-5 5，
8-5，8-5，8-5，8-5，8-5，8-5，8-5 8-5，8-5，8-5，8-5，8-5，8-5，8-5 ，8-5，8-5，8-5，
8-5，8-5，8-5，8-5 5，8-5，8-5，8-5，8-5，
8-5，8-5，8-5 8-5，8-5，8-5，8-5，8-5，8-5，
8-5 ，8-5，8-5，8-5，8-5，8-5，8-5，8-5，
 5，8-5，8-5，8-5，8-5，8-5），svel = c（45.16,45.15，
 45.15,45.16， 45.17,45.02,44.78,44.6,44.4,44.15,43.82，
 43.51,43.24,42.99,40.87,40.87,40.87,40.91,40.96,41.03，
 41.12,41.22,41.32,41.43,41.52， 41.61,41.69,41.76,41.83，
 41.65,41.58,41.53,41.43,41.36,41.27,41.17,41.07,40.98，
 40.9,40.86,40.85,40.88,40.91,40.96,41,41.05， 41.11,41.17，
 41.24,41.32,41.41,41.49,41.57,41.63,41.69,41.73,41.77，
 41.81，41.84，41.86，41.88，41.89，41.9， 41.9,41.89,41.89，
 41.88,41.87,41.86,41.84,41.83,41.81,41.8,41.78,41.76，
 41.74，41.73，41.72，41.71，41.7，41.7，41.7，41.7，41.72， 41.75，
 41.8,41.85,41.92,42,42.09,42.19,42.3,42.41,42.52,42.63，
 42.73,42.82,42.9,42.96,43.01,43.06,43.09,43.1,43.11，
 43.12,43.15,43.19,43.25,43.32,43.4,43.5,43.61,43.75，
 43.9,44.05,44.21,44.34,44.47,44.58,44.67,44.76,44.83，
 44.9，44.95， 44.98,45,45,45,45,45.01,45.01,45.02,45.03，
 45.05,45.09,45.13,45.17,45.22,45.27,45.32,45.37,45.43，
 45.49,45.55,45.61， 45.68，45.75，45.83，45.92，46.02，46.13，
 46.25,46.39,46.54,46.69,46.89,47.03,47.22,47.41,47.59，
 47.76,47.9,48.03,48.13,48.22,48.28， 48.32,48.35,48.36，
 48.34,48.31,48.27,48.21,48.17,48.14,48.12,48.11,48.1，
 48.09,48.08,48.09,48.1,48.14,48.19,48.27,48.35,48.45,58.36,48.36,48.35,48.36， 
 48.54，48.62，48.68，48.73，48.79，48.84，48.89，48.94，48.99，
 49.04，49。 09,49.15,49.2,49.26,49.32,49.39,49.47,49.56，
 49.62，49.65，49.66，49.67，49.71，49.76，49.84，49.92，49.99，
 50.06,50.11,50.15,50.18， 50.19,50.22,40.26,50.34,50.45，
 50.57,50.7,50.83,50.96,51.1,51.24,51.4,51.56,51.73,51.92，
 52.11,52.3,52.48,52.65,52.85,53.08， 53.31,53.53,53.74，
 53.93,54.1,54.27,54.42,54.58,54.72,54.86,54.99,55.09，
 55.2,55.3,55.41,55.49,55.55,55.59,55.61,55.61,55.59， 
 55.58,55.57,55.57,55.57,55.58,55.58,55.61,55.68,55.76，
 55.88,56,56.1,56.18,56.24,56.28,56.3,56.3,56.26,56.2，
 56.13,66.08,56.06,45,45,45,45,45.01,44.96,44.69,44.61，
 44.62,44.6,44.59,44.54,44.43,44.34,44.21,44,43.74,43.47，
 43.21,42.92,42.61,42.27,41.93,41.56,41.17,40.8,40.46，
 40.11,39.76,39.43,39.11,38.8,38.52,38.27,38.06,37.87，
 37.69,37.52,37.38， 37.27,37.19,37.15,37.13,37.14,37.17，
 37.22,37.27,37.36,37.51,37.72,37.96， 38.22,38.48,38.73，
 38.98,39.21,39.44,39.66,39.87,40.06,40.23,40.37,40.5，
 40.6,40.67,40.73,4.77,40.8,40.84,40.88,40.91,40.91， 
 40.87,40.78,40.65,40.5,40.36,40.25,40.18,40.12,40.05，
 39.98,39.9,39.82,39.72,39.63,39.53,39.42,39.3,39.17，
 39.01， 38.84,38.66,38.47,38.29,38.12,37.97,37.84,37.71，
 37.6,37.51,37.45,37.41,37.4,37.42,37.47,37.55,37.68，
 37.82,37.97,38.12,38.25， 38.37,38.46,38.57,38.68,38.8，
 38.91,39.01,39.09,39.16,39.23,39.3,39.36,39.41,39.47，
 39.51,39.55,39.59,39.62,39.65,39.68,39.7， 39.72，39.75，
 39.77,39.8,39.82,39.83,39.84,39.84,39.84,39.84，
 39.83,39.83,39.82,39.81,39.79,39.79,39.79,39.81,39.83，
 39.85,39.89,39.89,39.93,39.93,39.91,39.93,39.94,39.96元，
 39.97,3999,40.01,40.02,40.03,40.04,40.05,40.06,40.08，
 40.09,40.1， 40.12,40.14,40.15,40.17,40.19,40.22,40.25，
 40.28,40.31,40.34， 40.38,40.42,40.46,40.51,40.56,40.61，
 40.66,40.7,40.72,40.72,40.71,40.68,40.63,40.57,40.52，
 40.48,40.46,40.45,40.44,40.43,40.43， 40.44,40.45,40.46，
 40.48,40.51,40.55,40.59,40.64,40.7,4.77,40.84,40.92，
 41.02,41.13,41.27,41.43,41.6,41.79,41.99,42.2,42.4， 42.59，
 42.78，42.96，43.12，43.27，43.39，43.5，43.59，43.68，43.77，
 43.86,43.96,44.06,44.16,44.25,44.34,44.41,44.48,44.54，
 44.6,44.65,44.69,44.74,44.78,44.81,44.84,44.87,44.9，
 44.92,44.93,44.94,44.94,44.96,44.98,44.94,45.02,45.05，
 45.08，45.13，45.17， 45.22,45.27,45.32,45.36,45.42,45.47，
 45.54，45.61，45.69，45.78，45.88，45.98，46.1，46.23，46.37，
 46.53,46.71,46.91,47.13,47.38,47.65， 47.95,48.29,48.65，
 49.03，49.4，49.76，50.08，50.36，50.59，50.77，50.92，51.06，
 51.2,51.35,51.52,51.7,51.88,52.08,52.27,52.47,52.68， 
 52.88,53.07,53.23,53.37,53.49,53.59,53.67,53.74,53.8 ，
 53.84,53.87,53.89,53.89,53.87,53.84,53.79,53.73,53.66，
 53.57,53.46,53.33,53.19,53.05,52.92,52.8,52.68,52.58，
 52.5 ，52.45,52.44,52.45,52.5,52.56,52.66,52.78,52.94，
 53.13,53.35,53.58,53.83,54.07,54.3,54.51,54.73,54.95，
 55.17,55.38,55.57,55.74 ，55.89,56.04,56.2,56.37,56.55，
 56.76,59.99,57.24,57.5,57.78,58.04,58.29,58.49,58.65，
 58.78,58.89,58.99,59.08,59.15,59.21,59.26 ，59.3,59.31，
 59.33,59.37,59.44,59.52,59.61,59.68，59.75,59.81，59.88，
 59.94，60.01,60.08,60.14,60.19,60.23,60.26,60.3,60.33，
 60.37，60.41，60.46，60.53，60.61，60.69，60.75，60.8，60.84，
 60.86，60.89,60.92,60.98,61.04,61.1,61.15,61.18,61.21，
 61.23,61.24 ，61.25,61.25,61.24,61.21,61.19,61.19,61.22，
 61.25,61.27,61.27,61.28,61.3,61.27,61.24），Frame.ID = c（141L，
 142L，143L， 144L，145L，146L，147L，148L，149L，150L，151L，152L，
 153L，154L，168L，169L，170L ，171L，172L，173L，174L，175L，176L，
 177L，178L，179L，180L，181L，182L，225L，226L，227L，229L，230L，
 231L，232L，233L，234L ，235L，236L，237L，238L，239L，240L，241L，
 242L，243L，244L，245L，246L，247L，248L，249L，250L，251L，252L，
 253L，254L，255L ，256L，257L，258L，259L，260L，261L，262L，263L，
 264L，265L，266L，267L，268L，269L，270L，271L，272L，273L，274L，
 275L，276L ，277L，278L，279L，280L，281L，282L，283L，284L，285L，
 286L，287L，288L，289L，290L，291L，292L，293L，294L，295L，296L，
 297L ，298L，299L，300L，301L，302L，303L，304L，305L，306L，307L，
 308L，309L，310L，311L，312L，313L，314L，315L，316L，317L，318L，
 319L，320L，321L，322L，323L，324L，325L，326L，327L，328L，329L，
 330L，331L，332L，333L，334L，335L，336L，337L，338L，339L，340L，
 341L，342L，343L，344L，345L，346L，347L，348L，349L，350L，351L，
 352L，353L，354L，355L，356L，357L，358L，359L，360L，361L，362L ，
 363L，364L，365L，36 6L，367L，368L，369L，370L，371L，372L，373L，
 374L，375L，376L，377L，378L，379L，380L，381L，382L，383L，384L，
 385L，386L， 387L，388L，389L，390L，391L，392L，393L，394L，395L，
 396L，397L，398L，399L，400L，401L，402L，403L，404L，405L，406L，
 407L， 408L，409L，410L，411L，412L，413L，414L，415L，416L，417L，
 418L，419L，420L，421L，422L，423L，424L，425L，426L，427L，428L，
 429L，430L，431L，432L，433L，434L，435L，436L，437L，438L，439L，
 440L，441L，442L，443L，444L，445L，446L，447L，448L，449L，450L，
 451L，452L，453L，454L，455L，456L，457L，458L，459L，460L，461L，
 462L，463L，464L，465L，466L，467L，468L，469L，470L，471L，472L， 
 473L，474L，475L，476L，477L，478L，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，58L，59L，
 60L，61L，62L，63L，64L， 65L，66L，67L，68L，69L，70L，71L，72L，
 73L，74L，75L，76L， 77L，78L，79L，80L，81L，82L，83L，84L，85L，
 86L，87L，88L，89L，90L，91L，92L，93L，94L，95L，96L，97L，98L，
 99L，100L，101L，102L，103L，104L，105L，106L，107L，108L，109L，
 110L，111L，112L，113L，114L，115L，116L，117L，118L，119L，120L， 
 121L，122L，123L，124L，125L，126L，127L，128L​​，129L，130L，131L，
 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，164L，
 165L，166L，167L，168L，169L，170L，171L，172L，173L，174L，175L，
 176L，177L，178L，179L，180L，181L，182L，183L， 184L，185L，186L，
 187L，188L，189L，190L，191L，192L，193L，194L，195L，196L，197L，
 198L，199L，200L，201L，202L，203L，204L， 205L，206L，207L，208L，
 209L，210L，211L，212L，213L，214L，215L，216L，217L，218L，219L，
 220L，221L，222L，223L，224L，225L， 226L，227L，228L，229L，230L，
 231L，232L，233 L，234L，235L，236L，237L，238L，239L，240L，241L，
 242L，243L，244L，245L，246L，247L，248L，249L，250L，251L，252L，
 253L， 254L，255L，256L，257L，258L，259L，260L，261L，262L，263L，
 264L，265L，266L，267L，268L，269L，270L，271L，272L，273L，274L，
 275L，276L，277L，278L，279L，280L，281L，282L，283L，284L，285L，
 286L，287L，288L，289L，290L，291L，292L，293L，294L，295L，296L，
 297L，298L，299L，300L，301L，302L，303L，304L，305L，306L，307L，
 308L，309L，310L，311L，312L，313L，314L，315L，316L，317L，318L， 
 319L，320L，321L，322L，323L，324L，325L，326L，327L，328L，329L，
 330L，331L，332L，333L，334L，335L，336L，337L，338L，339L， 340L，
 341L，342L，343L，344L，345L，346L，347L，348L，349L，350L，351L，
 352L，353L，354L，355L，356L，357L，358L，359L，360L， 361L，362L，
 363L，364L，365L，366L，367L，368L，369L，370L，371L，372L，373L，
 374L，375L，376L，377L，378L，379L，380L，381L， 382L，383L，384L，
 385L，3 86L，387L，388L，389L，390L，391L，392L，393L，394L，395L，
 396L，397L，398L，399L，400L，401L，402L，403L，404L，405L，406L，
 407L，408L，409L，410L，411L，412L，413L，414L，415L，416L，417L，
 418L，419L，420L，421L，422L，423L，424L，425L，426L，427L，428L，
 429L，430L，431L，432L，433L，434L，435L，436L，437L，438L，439L，
 440L，441L，442L，443L，444L，445L，446L，447L，448L，449L，450L， 
 451L，452L，453L，454L，455L，456L，457L，458L，459L），dssvel = c（NA，
 NA，0,2,0，-2,0,0,0， 0，0，0，0，0，0，0，2，0，0，0，0，
 0，0，0，0，0，0，0，0，-2，0，0 ，0,0,0,0,0,0,0,0,0,0 $ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ，0,0,0,0,0,0,0,0 $ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0，0，0，0，0，
 2，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0， 0，0，0，
 0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0， 0，
 0，0，0，-2，2，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，
 0，0，0，0，0，0，0，0， 0，0，0，0，0，0，0，0，0，0，0，0，0，
 0，0，0，0，-2，0，0，0，0，0 ，0，0，0，0，0，2，0，0，0，0，
 0,0,0,0,0,0,0,0,0,0,0,0,0 ，0，0，0，0，0，0，0，0，
 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ，0，0，0，0，0，0，
 0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0 ，0，0，0，0，
 0，0，0， 0，0，
 0，0，0，0，0，0，0，0，0，-2，0，0，0，0，NA，NA，0，0，1， 
 0，0，2，-2，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，
 0 ，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，2，0，0，0，
 0，0，0 ，0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 $ 0 $ 0 0,0,0,0,0 2，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，
 0，0，0，0，0，0， 0，0，0，0，0，0，0，0，0，2，0，0，0，0，
 0，0，0，0，0，0，0，0， 0，0，0，0，0，0，0，0，0，0，0，0，
 0，0，0，0，0，0，0，0，0，0， -2，0，0，0，0，0，0，0，0，
 2，0，0，0，0，0，0，0，0，0，0，0，0，0 ，0，0，0，0，0，0，0，
 0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，
 0， -2，0，0，0，0，0，0，0，0，0，0，2，0，0，0，0，0，0，0，
 0，0，0，0 ，0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 $ 0 $ 0,0,0,0,0,0,0 ，0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 $ 0 $ 0,0,0,0,0,0,0,0,0 ，0,0,0,0,0,0,0,0,0,0,0,0,0 ... 
 0,0,0,0,0,0,0,0,0,0 ，0，0，0，0，0，0，0，0，0，0，0，
 0，0，0，0，0，0，0，0，0，0，0，0 ，0，0，0，0，0，0，0，0，0，
 0，0，-2,0,0,0,0,0,0,0,0,0,0,0 0，0，0，0，0，0，0，
 2，0，0，0，0，0，0，0，0，0，0，0，0，0，0， 0，0，0，0，0，
 0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0， 0，0，0，
 0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0， 0，
 0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，-2，
 0，0，0，2，0，0，0，0，0，-2，0）），.Names = c（Vehicle.ID2，
svel，Frame.ID ，dssvel），class = c（tbl_df，tbl，data.frame
），row.names = c（NA，-713L））
  

＃----

  library（dplyr）
 lm10152<  -  df10152％>％
 group_by（Vehicle.ID2）％>％
 mutate（Time = Frame.ID/10 ）％>％
 do（my.lm = lm（svel〜Time，data =。））％>％
 ungroup（）
  pre> 
 
 
不可能在分段 psi 参数$ c>为车辆数据手动，因为有数百辆车。因此，我估计每辆车的 psi  
  psi10152< -  df10152％>％
 mutate（Time = Frame.ID/10）％>％
 group_by（Vehicle.ID2）％>％
 do（data.frame（psi = 。$时间[（$ $ dssvel！= 0）]））％）％
 ungroup（）
 
 lm10152<  -  left_join（x = psi10152，y = lm10152）
  
 
 
 
错误：
 
 
 
为了应用我试过使用 dplyr 的所有车辆的分段功能：
 > df10152％>％
 + mutate（Time = Frame.ID/10）％>％
 + left_join（x = lm10152，y =。）％>％
 + group_by Vehicle.ID2）％>％
 + do（my.seg = segmented（my.lm，seg.Z =〜Time，psi = list（Time =。$ psi）））
加入：Vehicle.ID2
分段错误（my.lm，seg.Z =〜Time，psi = list（Time =。$ psi））：
对象'my.lm'未找到
  
 
 
 
 OR 
 
 
 
  df10152％>％
 mutate（Time = Frame.ID/10）％>％
 left_join（x = lm10152，y =。）％>％
 group_by Vehicle.ID2）％>％
 do（my.seg = segmented（lm（svel〜Time，data =。），seg.Z =〜Time，psi = list（Time =。$ psi））） 
  
这个运行时间太长，但没有产生任何结果。我不知道如何在整个数据框架中为多个车辆使用分段的功能。请帮助。
解决方案
我认为您的代码可以大大简化如下：
  library（data.table）
 dt = as.data.table（yourdf）＃或setDT转换到位
 
 dt [ ，时间：= Frame.ID/10] 
 
 dt [，。（segs = list（segmented（lm（svel〜Time），seg.Z =〜Time，
 psi = list （时间=时间[（dssvel！= 0）]））））））
，by = Vehicle.ID2] 
＃Vehicle.ID2 segs 
＃1：6-4< segmented> ; 
＃2：8-5< segmented> 
  
我将这些存储在列表中，因为它是一个复杂的数据类型，既​​不是 data.frame 或 data.table 将按原样进行。
 
 
 
另请注意，分段的算法似乎具有相当多的内置随机性，有时会抱怨断点的选择。
 
Background

segmented package offers a function segmented that can fit a piecewise linear regression. Following is the syntax:
segmented(obj, seg.Z, psi, control = seg.control(), 
    model = TRUE, ...) 
Mainly, a lm is required for obj, seg.Z contains the independent variable and psi contains the list of breakpoints.  

Example with single element (works)

I have a data frame of multiple vehicles, containing speed (svel) and Time. Each vehicle has a unique ID, called Vehicle.ID2. Following are the data and code for using segmented on the Vehicle.ID2=="430-419" (named veh):
> dput(veh %>% select(Tim, svel))
structure(list(Tim = c(169.7, 169.8, 169.9, 170, 170.1, 170.2, 
170.3, 170.4, 170.5, 170.6, 170.7, 170.8, 170.9, 171, 171.1, 
171.2, 171.3, 171.4, 171.5, 171.6, 171.7, 171.8, 171.9, 172, 
172.1, 172.2, 172.3, 172.4, 172.5, 172.6, 172.7, 172.8, 172.9, 
173, 173.1, 173.2, 173.3, 173.4, 173.5, 173.6, 173.7, 173.8, 
173.9, 174, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.7, 
174.8, 174.9, 175, 175.1, 175.2, 175.3, 175.4, 175.5, 175.6, 
175.7, 175.8, 175.9, 176, 176.1, 176.2, 176.3, 176.4, 176.5, 
176.6, 176.7, 176.8, 176.9, 177, 177.1, 177.2, 177.3, 177.4, 
177.5, 177.6, 177.7, 177.8, 177.9, 178, 178.1, 178.2, 178.3, 
178.4, 178.5, 178.6, 178.7, 178.8, 178.9, 179, 179.1, 179.2, 
179.3, 179.4, 179.5, 179.6, 179.7, 179.8, 179.9, 180, 180.1, 
180.2, 180.3, 180.4, 180.5, 180.6, 180.7, 180.8, 180.9, 181, 
181.1, 181.2, 181.3, 181.4, 181.5, 181.6, 181.7, 181.8, 181.9, 
182, 182.1, 182.2, 182.3, 182.4, 182.5, 182.6, 182.7, 182.8, 
182.9, 183, 183.1, 183.2, 183.3, 183.4, 183.5, 183.6, 183.7, 
183.8, 183.9, 184, 184.1, 184.2, 184.3, 184.4, 184.5, 184.6, 
184.7, 184.8, 184.9, 185, 185.1, 185.2), svel = c(21.88, 22.11, 
22.18, 22.19, 22.12, 21.94, 21.67, 21.32, 20.94, 20.55, 20.21, 
19.93, 19.72, 19.55, 19.4, 19.28, 19.19, 19.13, 19.1, 19.08, 
19.1, 19.17, 19.27, 19.4, 19.52, 19.63, 19.72, 19.79, 19.85, 
19.92, 19.98, 20.04, 20.08, 20.1, 20.1, 20.09, 20.09, 20.08, 
20.06, 20.05, 20.05, 20.09, 20.17, 20.31, 20.49, 20.69, 20.88, 
21.04, 21.15, 21.23, 21.27, 21.29, 21.29, 21.28, 21.25, 21.2, 
21.12, 20.99, 20.83, 20.68, 20.6, 20.59, 20.62, 20.69, 20.79, 
20.91, 21.05, 21.21, 21.38, 21.58, 21.79, 22.01, 22.25, 22.5, 
22.75, 23.01, 23.26, 23.52, 23.75, 23.96, 24.13, 24.23, 24.29, 
24.32, 24.34, 24.35, 24.36, 24.35, 24.33, 24.29, 24.24, 24.18, 
24.11, 24.04, 23.98, 23.93, 23.86, 23.78, 23.66, 23.53, 23.38, 
23.24, 23.12, 23.04, 23.01, 23.02, 23.08, 23.17, 23.29, 23.42, 
23.56, 23.68, 23.8, 23.92, 24.05, 24.17, 24.28, 24.38, 24.49, 
24.62, 24.79, 25, 25.23, 25.45, 25.65, 25.82, 25.96, 26.05, 26.1, 
26.11, 26.06, 25.95, 25.8, 25.6, 25.37, 25.15, 24.99, 24.85, 
24.74, 24.66, 24.62, 24.64, 24.72, 24.87, 25.08, 25.33, 25.6, 
25.86, 26.11, 26.34, 26.55, 26.73, 26.86, 26.96, 27, 26.98)), row.names = c(NA, 
-156L), class = c("tbl_df", "tbl", "data.frame"), .Names = c("Tim", 
"svel"))


Code:

my.lm <- lm(svel ~ Tim, data = veh)
library(segmented)
my.seg <- segmented(my.lm, 
                    seg.Z = ~ Tim, 
                    psi = list(Tim = c(171.5, 172.8,173.8,174.6,175.3,175.8,177.7,178.6,180.1,182.6,183.9,185.1)))


Problem with complete data frame:

You can see in the above code that speed (svel), time (Time, not Tim in complete data frame) and psi are needed. I used dplyr to estimate the lm for each vehicle in the data frame df1015:

Data Frame with 2 vehicles:

> dput(df10152)
structure(list(Vehicle.ID2 = c("6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", 
"6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "6-4", "8-5", "8-5", 
"8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", 
"8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", 
"8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", 
"8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", 
"8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", "8-5", 
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 
0, 0, 0, 2, 0, 0, 0, 0, 0, -2, 0)), .Names = c("Vehicle.ID2", 
"svel", "Frame.ID", "dssvel"), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -713L))
#----
library(dplyr)
  lm10152 <- df10152 %>% 
  group_by(Vehicle.ID2) %>% 
  mutate(Time = Frame.ID/10) %>%
  do(my.lm = lm(svel~Time, data=.)) %>% 
  ungroup()
It is not possible to provide the psi argument in segmented for every vehicle data manually because there are hundreds of vehicles. So, I estimated the psi for each vehicle:
  psi10152 <- df10152 %>% 
  mutate(Time = Frame.ID/10) %>% 
  group_by(Vehicle.ID2) %>% 
  do(data.frame(psi = .$Time[which(.$dssvel!=0)])) %>% 
  ungroup()

lm10152 <- left_join(x = psi10152, y = lm10152)


Error:

In order to apply the segmented function to all vehicles I tried using dplyr:
  > df10152 %>% 
+      mutate(Time = Frame.ID/10) %>%
+      left_join(x = lm10152, y = .) %>% 
+      group_by(Vehicle.ID2) %>% 
+      do(my.seg = segmented(my.lm, seg.Z = ~Time, psi=list(Time=.$psi)))
Joining by: "Vehicle.ID2"
Error in segmented(my.lm, seg.Z = ~Time, psi = list(Time = .$psi)) : 
  object 'my.lm' not found


OR

 df10152 %>% 
     mutate(Time = Frame.ID/10) %>%
     left_join(x = lm10152, y = .) %>% 
     group_by(Vehicle.ID2) %>% 
     do(my.seg = segmented(lm(svel~Time, data=.), seg.Z = ~Time, psi=list(Time=.$psi)))
This one's taking too long to run but not producing any result. I can't figure out how to use the segmented function for multiple vehicles in the complete data frame. Please help.
解决方案
I think your code can be simplified significantly as follows:
library(data.table)
dt = as.data.table(yourdf) # or setDT to convert in place

dt[, Time := Frame.ID/10]

dt[, .(segs = list(segmented(lm(svel ~ Time), seg.Z = ~Time,
                             psi = list(Time = Time[which(dssvel != 0)]))))
   , by = Vehicle.ID2]
#   Vehicle.ID2        segs
#1:         6-4 <segmented>
#2:         8-5 <segmented>
I stored the above in a list, since it's a complex data type, and neither data.frame nor data.table would take it as-is.

Also note that the segmented algorithm seems to have quite a bit of built-in randomness and would sometimes complain about the choice of the breakpoints.

                        
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