如何解决R中的Ts对象
我正在尝试使用功能ts和以下代码将多个站点的每月流量数据转换为R中的时间序列对象：
ts_MonthlyMean <- lapply(df_MonthyMean,function(x){ts(x$MonthlyMeanStreamflow,frequency=12,start=c(x[1,1],x[1,2]),end=c(tail(x$year,1),tail(x$month,1)))})
使用输入df_Monthly表示它是31个数据帧的列表。这是其中之一的结构：
> str(df_MonthyMean[[1]])
'data.frame': 809 obs. of 3 variables:
$ year : int 1953 1953 1953 1953 1953 1953 1953 1954 1954 1954 ...
$ month : int 6 7 8 9 10 11 12 1 2 3 ...
$ MonthlyMeanStreamflow: num 25.1 32.2 26.2 11.6 13.6 ...
> dput(round(df_MonthyMean[[1]],1))
structure(list(year = c(1953,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2020),month = c(6,7,8,9,10,11,12,1,2,3,4,5,6,10),MonthlyMeanStreamflow = c(25.1,32.2,26.2,11.6,13.6,22.7,20,26.5,38.6,322.6,279.7,68.3,14.7,36.5,87.7,34.7,22.5,29.5,28.5,36.6,46,67.9,49.5,25.1,14.4,342.9,55.8,30.5,42.9,42,80.5,273.4,189,65.1,17.2,20.9,27.4,9.4,15.1,29.1,28.4,77.9,223.1,257.8,239.3,148.1,56.9,44,376.2,103.7,61.1,124.1,75.5,47.9,141.4,760.8,1872.3,649.4,85.1,31.6,53.9,154,206.5,60.2,51.2,40.5,48.5,66.6,66.5,29.7,19.2,33.2,251.5,60.5,48.9,163.5,109,205.5,182.2,1000.3,506.9,131.3,42.7,16.5,20.4,21.6,41.8,36.4,35.6,37.7,46.7,154.9,197.2,23.5,23.3,32.4,36.1,37.9,124.9,182.1,172.7,654.5,427.5,1793.3,295.2,56.3,34.2,18.2,41.7,59.5,54.4,50.1,296.9,321.1,289.2,69.2,28.8,32.1,143.2,384.6,165.1,128,60.4,90.6,407.2,111.3,37.5,117.9,296.8,92.6,57.8,322.2,344,549.7,1282.8,380.5,68.1,122.4,139.9,47.2,34.4,96.9,472.7,391.7,167.2,1383.9,1208.9,209.6,39.7,45.4,90.8,87.1,51.6,27.3,45.6,38.1,46.6,70.1,62,25.4,22.3,84.2,378,203.1,52.5,49.1,132.4,537.7,798.6,1290.8,473.8,41.9,128.3,36.9,25.8,39.2,33.1,153.5,127.2,325,876.8,222.7,46.2,27.7,64.4,134.7,37.1,55.7,54.5,50.3,52.7,219.4,315.2,128.7,25.3,24.8,85,58.5,28.1,56.5,78.7,23.1,15.6,122,86.4,564.2,200.7,151.8,75.4,158.5,43.7,32.6,24.5,25.7,65.2,1210.7,299.5,174.6,222.9,276.7,674.2,2058.2,1933,244.3,102.8,54.8,20.7,38,41.5,131.9,110,30,9.8,35,95.4,143.6,52.2,87.2,819.2,1052.4,518.3,72.7,47.8,24.3,120.6,23.2,169.4,232.8,507.4,214.1,31.4,37.3,39.1,24.2,18.8,24.7,29.9,32.7,40.8,64,150.1,50.4,18,80.6,48.2,34.3,33.4,176.5,1623.7,1001.5,35.1,27,42.5,23.7,26.4,282.7,915.1,391.6,525.6,1020.9,2252.7,800.2,57.4,21.5,23,32,92.9,1036,812,1644.8,890.7,136.9,61.8,79.4,76.2,27.2,40.7,40.4,28.3,40.2,194.2,447.2,120.8,51.8,77.1,61.5,69.6,38.3,43.2,57.2,195.2,664.1,759,337.8,30.7,100.3,75.1,48.7,173,374.9,1102.8,1707.8,1262,230.1,55.4,97.4,171.7,851.3,96.1,196.7,256.2,171.8,322.4,260.1,107.5,22.6,68,80.8,268.8,107,602.6,503.5,611,1863.1,1336.3,552.2,108.8,61.2,123.4,75.6,100.6,73.2,82.7,50.5,329.9,759.4,538.3,82,39.3,47.5,48.8,76.9,368,227.4,96.8,232.2,741.5,1341.7,411.7,69.9,76.4,39.4,39.9,37,331.1,457.2,701.1,328.1,60.1,54.6,311.3,366.2,60.9,51,47.3,71.3,389.8,126.3,14.1,35.4,45.8,49.9,108.4,84.1,23.8,49.8,60.7,61.7,59.2,201.1,308.5,286,1004.2,1432.8,394,75.8,82.2,56.7,130.3,135.2,400.3,864.7,1120.7,406.4,226.6,202.7,79.7,52.8,240,1570.8,984.8,1577,1926.7,687.2,157.3,62.2,61,45.9,57.7,458.3,242.7,79.1,40.9,182.6,50.7,505.3,346.1,249,986.1,1164.9,429.3,227.9,69,30.1,54.9,54.2,30.4,39.5,25.2,44.6,66.8,85.8,101,79.2,108.1,194.7,677.3,342.1,109.5,29.8,36.3,54.7,124.5,802,1032.1,465.5,50.6,38.2,31.2,40,43,91.1,33.8,30.9,67.3,509,120.3,41.3,40.1,35.2,44.9,43.5,34.9,44.4,27.5,219,332.3,52.6,115.6,426.8,658.7,152.7,69.5,35.3,32.3,36.2,21.3,50.8,74.4,28.6,47.4,40.3,61.3,166,629.8,413.9,102.5,31.1,29.2,121.1,67.4,41,564.9,375.6,65.6,25,127.6,358.6,1124.4,591.4,766,229.9,50.9,29.3,53,38.9,30.3,34,53.1,24.6,24.9,44.5,599.1,149.7,79.6,95.2,321.3,145.9,53.8,203.4,70.9,48.1,152.8,393.6,600.7,991.5,532,156.2,67.8,138.4,97.1,46.1,169.8,235.3,697.6,256.7,103.5,42.3,30.6,35.7,164.4,661.3,1280.2,390.9,63.8,129.6,29,36,35.8,63.9,45.2,64.9,163,96,27.8,64.1,335,33.6,63.2,27.9,31.5,86.1,153.4,481,174.5,48.6,193.5,578.7,88.5,55.3,42.6,115.3,23.6,47,194.9,136.4,131.5,66,99.4,327.8,203,72.3,43.1,178.9,145.1,168.8,149.3,374.5,126.5,88.4,557.6,281,32.9,48.3,328.5,527.2,934.2,684.3,205.6,63.3,188.7,24.4,26.9,32.5,19.5,43.9,525.9,1537.6,611.8,46.4,35.9,217.2,152.1,393.1,1191.2,383.4,33.9,24.1,25.3)),row.names = c(NA,-809L),class = "data.frame")
代码似乎正常工作，产生以下结果：
> round(ts_MonthlyMean[[1]],1)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1953 25.1 32.2 26.2 11.6 13.6 22.7 20.0
1954 26.5 38.6 322.6 279.7 68.3 14.7 36.5 87.7 34.7 22.5 29.5 28.5
1955 36.6 46.0 67.9 49.5 25.1 14.4 46.0 342.9 55.8 26.5 30.5 42.9
1956 42.0 80.5 273.4 189.0 65.1 17.2 20.9 27.4 9.4 15.1 29.1 28.4
1957 77.9 223.1 257.8 239.3 148.1 56.9 44.0 376.2 103.7 61.1 124.1 75.5
1958 47.9 141.4 760.8 1872.3 649.4 85.1 31.6 53.9 154.0 206.5 60.2 51.2
1959 40.5 48.5 66.6 66.5 29.7 19.2 33.2 251.5 60.5 48.9 163.5 109.0
1960 205.5 182.2 1000.3 506.9 131.3 42.7 16.5 20.4 21.6 41.8 36.4 35.6
1961 37.7 46.7 154.9 197.2 40.5 23.5 23.3 32.4 36.1 37.9 124.9 182.1
1962 172.7 654.5 427.5 1793.3 295.2 56.3 34.2 18.2 41.7 59.5 54.4 46.0
1963 50.1 296.9 321.1 289.2 69.2 28.8 32.1 143.2 384.6 165.1 128.0 60.4
1964 36.6 36.6 90.6 407.2 111.3 37.5 48.5 117.9 296.8 92.6 50.1 57.8
1965 322.2 344.0 549.7 1282.8 380.5 68.1 122.4 139.9 47.2 34.4 96.9 472.7
1966 391.7 167.2 1383.9 1208.9 209.6 39.7 45.4 90.8 87.1 51.6 27.3 45.6
1967 38.1 46.6 70.1 62.0 25.4 22.3 84.2 378.0 203.1 52.5 49.1 61.1
1968 132.4 537.7 798.6 1290.8 473.8 77.9 41.9 128.3 36.9 25.8 39.2 33.1
1969 153.5 127.2 325.0 876.8 222.7 46.2 27.7 64.4 134.7 37.1 55.7 54.5
1970 50.3 52.7 219.4 315.2 128.7 25.3 24.8 36.6 85.0 58.5 26.5 28.1
1971 44.0 56.5 78.7 45.6 23.1 15.6 28.8 122.0 86.4 564.2 200.7 203.1
1972 151.8 75.4 158.5 43.7 24.8 32.6 24.5 25.7 65.2 1210.7 299.5 174.6
1973 222.9 276.7 674.2 2058.2 1933.0 244.3 102.8 54.8 20.7 22.3 38.0 36.9
1974 41.5 34.7 131.9 110.0 30.0 9.8 22.3 75.5 35.0 95.4 143.6 48.5
1975 52.2 87.2 819.2 1052.4 518.3 72.7 47.8 24.3 120.6 23.2 25.1 27.7
1976 32.2 169.4 232.8 507.4 214.1 31.4 37.3 39.1 24.2 18.8 24.7 29.9
1977 32.7 40.8 64.0 150.1 50.4 18.0 42.7 80.6 48.2 34.3 33.4 31.4
1978 40.5 176.5 1623.7 1001.5 222.7 35.1 27.0 42.5 23.7 26.4 282.7 915.1
1979 391.6 525.6 1020.9 2252.7 800.2 239.3 57.4 62.0 21.5 23.0 32.0 24.5
1980 92.9 1036.0 812.0 1644.8 890.7 136.9 61.8 79.4 76.2 27.2 40.7 40.4
1981 28.3 40.2 194.2 447.2 120.8 36.5 51.8 77.1 61.5 69.6 38.3 43.2
1982 57.2 195.2 664.1 759.0 337.8 60.5 30.7 100.3 75.1 18.8 48.7 195.2
1983 173.0 374.9 1102.8 1707.8 1262.0 230.1 55.4 97.4 171.7 851.3 96.1 196.7
1984 256.2 171.8 322.4 260.1 107.5 22.6 25.3 68.0 80.8 268.8 107.0 602.6
1985 503.5 611.0 1863.1 1336.3 552.2 108.8 61.2 123.4 75.6 100.6 73.2 82.7
1986 50.5 329.9 759.4 538.3 82.0 39.3 54.4 47.5 48.8 76.9 368.0 227.4
1987 96.8 232.2 741.5 1341.7 411.7 69.9 39.7 76.4 39.4 39.9 97.4 37.0
1988 35.1 331.1 457.2 701.1 328.1 60.1 54.6 311.3 366.2 60.9 51.0 47.3
1989 71.3 96.9 389.8 126.3 42.7 21.6 14.1 48.7 29.5 33.1 32.2 35.4
1990 45.8 49.9 108.4 84.1 44.0 23.8 49.8 60.7 49.8 61.7 59.2 201.1
1991 308.5 286.0 1004.2 1432.8 394.0 75.8 50.5 82.2 131.3 36.9 56.7 130.3
1992 135.2 400.3 864.7 1120.7 406.4 226.6 57.8 202.7 79.7 46.2 52.8 240.0
1993 1570.8 984.8 1577.0 1926.7 687.2 157.3 62.2 61.7 61.0 45.9 49.1 50.1
1994 41.5 57.7 458.3 242.7 79.1 27.3 17.2 40.9 182.6 50.7 505.3 346.1
1995 249.0 986.1 1164.9 429.3 227.9 69.0 30.1 54.9 54.2 30.4 33.1 23.8
1996 23.0 39.5 30.1 32.0 22.5 25.2 44.6 66.8 85.8 58.5 101.0 79.2
1997 108.1 194.7 677.3 342.1 109.5 34.7 29.8 39.7 42.7 32.1 36.3 37.7
1998 54.7 124.5 802.0 1032.1 465.5 66.5 50.6 38.2 31.2 40.0 37.1 43.0
1999 38.6 39.3 31.2 91.1 33.8 30.9 67.3 509.0 120.3 41.3 38.2 40.4
2000 40.1 35.2 44.9 43.5 31.2 34.9 34.2 44.4 27.5 219.0 332.3 100.6
2001 52.6 115.6 426.8 658.7 152.7 32.0 33.4 69.5 35.3 29.7 32.3 38.3
2002 36.2 35.1 35.2 27.0 23.1 21.3 50.8 52.6 74.4 28.6 47.4 40.3
2003 61.3 166.0 629.8 413.9 102.5 31.1 29.2 44.4 121.1 30.4 67.4 41.0
2004 42.5 60.7 564.9 375.6 65.6 25.0 37.7 30.7 29.7 30.0 47.2 127.6
2005 358.6 1124.4 591.4 766.0 229.9 50.9 29.3 53.0 38.9 30.3 34.0 32.7
2006 30.9 30.4 41.3 53.1 24.6 24.9 44.5 599.1 149.7 79.6 43.5 36.4
2007 41.0 95.2 321.3 145.9 53.8 32.4 32.7 203.4 70.9 54.2 48.1 152.8
2008 393.6 600.7 991.5 532.0 156.2 50.5 67.8 138.4 296.8 97.1 39.7 46.1
2009 169.8 235.3 697.6 256.7 103.5 42.3 36.5 30.6 34.4 32.6 35.7 38.0
2010 128.3 164.4 661.3 1280.2 390.9 52.6 63.8 129.6 44.9 29.3 29.0 36.0
2011 35.8 35.4 63.9 45.2 25.3 26.5 64.9 163.0 96.0 39.7 27.8 36.3
2012 64.1 108.8 335.0 153.5 33.6 25.2 33.1 63.2 52.7 23.7 27.9 31.5
2013 86.1 153.4 481.0 174.5 48.6 25.7 203.1 193.5 578.7 88.5 55.3 67.8
2014 42.6 44.0 115.3 41.8 26.5 23.6 47.0 194.9 136.4 131.5 49.1 66.0
2015 99.4 327.8 203.0 72.3 43.1 28.1 178.9 145.1 168.8 149.3 374.5 126.5
2016 88.4 557.6 281.0 85.1 41.8 31.4 32.9 44.5 26.2 36.6 48.3 328.5
2017 527.2 934.2 684.3 205.6 63.3 27.4 66.8 188.7 30.4 31.4 22.3 24.4
2018 26.9 32.5 32.9 23.5 24.7 19.5 29.9 42.5 43.9 61.1 33.4 29.0
2019 75.5 525.9 1537.6 611.8 154.0 46.4 28.4 41.0 35.9 31.6 40.4 217.2
2020 152.1 393.1 1191.2 383.4 78.7 29.1 33.9 29.9 24.1 25.3
>
但是，在代码环境中，时间序列数据（对象ts）从1953变为 2021 ，而不是2020。
> str(ts_MonthlyMean[[1]])
Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
发生这种情况的任何原因以及如何解决？
同时，我在将季节性Sen Slope应用于数据时遇到以下错误：
> sea.sens.slope(ts_MonthlyMean[[1]])
Error in d[,i] <- .d(dat) :
number of items to replace is not a multiple of replacement length
解决方法
问题在于sea.sens.slop仅在整个周期内有效。
这可以按预期工作：
trend::sea.sens.slope(window(ts_MonthlyMean[[1]],end = c(2020,5)))
#> [1] 0.01801948
您的数据由68年零5个月组成。您只能在68年内使用sea.sens.slope。这就是为什么我提取了window的数据。
看到2021年的原因：
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
这仅仅是因为start和end点在str中默认为四舍五入：
tsp(ts_MonthlyMean[[1]])
#> [1] 1953.417 2020.750 12.000
oo <- options(digits = 3) # change options the same way str does
tsp(ts_MonthlyMean[[1]])
#> [1] 1953 2021 12
options(oo) # reset options
如果您想不对它进行四舍五入：
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
# change str options
stro <- getOption("str")
stro$digits.d <- 7
oo <- options(str = stro)
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953.417 to 2020.75: 25.1 32.2 26.2 11.6 13.6 22.7 20 26.5 38.6 322.6 ...
options(oo) # reset options
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