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我知道filter()R 中的函数计算移动平均线。我想知道是否存在返回移动方差或标准差的函数，以便将其与函数的输出并排显示在图中filter()。

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4 回答 4

40

考虑zoo包。例如filter()给出：

> filter(1:100, rep(1/3,3))
Time Series:
Start = 1 
End = 100 
Frequency = 1 
  [1] NA  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
 [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
 [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 NA

而rollmean()在动物园里给出：

> rollmean(1:100, k = 3, na.pad = TRUE)
  [1] NA  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
 [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
 [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 NA

这是相同的（在此示例中为 3 点移动平均线）。

虽然zoo没有 arollsd()或rollvar()它有rollapply()，但它的工作方式类似于apply()将任何 R 函数应用于指定窗口的函数。

> rollapply(1:100, width = 3, FUN = sd, na.pad = TRUE)
  [1] NA  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [26]  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
 [51]  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
 [76]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 NA
Warning message:
In rollapply.zoo(zoo(data), ...) : na.pad argument is deprecated

或者更有趣的事情：

> rollapply(vec, width = 3, FUN = sd, na.pad = TRUE)
  [1]        NA 0.3655067 0.8472871 0.5660495 0.3491970 0.4732417 0.9236859
  [8] 0.8075226 1.8725851 1.1930784 0.6329325 1.1412416 0.8430772 0.5808005
 [15] 0.3838545 1.1738170 1.1655400 1.3241700 0.6876834 0.1534157 0.4858477
 [22] 0.9843506 0.6002713 0.6897541 2.0619563 2.5675788 6.3522039 6.0066864
 [29] 6.2618432 5.1704866 2.1360853 2.5602557 1.0408528 1.0316396 4.9441628
 [36] 5.0319314 5.7589716 3.2425000 4.8788158 2.0847286 4.5199291 2.5323486
 [43] 2.1987149 1.8393000 1.2278639 1.5998965 1.5341485 4.4287108 4.4159166
 [50] 4.3224546 3.6959067 4.9826264 5.3134044 8.4084322 9.1249234 7.5506725
 [57] 3.8499136 3.9680487 5.6362296 4.9124095 4.3452706 4.0227141 4.5867559
 [64] 4.7350394 4.3203807 4.4506799 7.2759499 7.6536424 7.8487654 2.0905576
 [71] 4.0056880 5.6209853 1.5551659 1.3615268 2.8469458 2.8323588 1.9848578
 [78] 1.1201124 1.4248380 1.7802571 1.4281773 2.5481935 1.8554451 1.0925410
 [85] 2.1823722 2.2788755 2.4205378 2.0733741 0.7462248 1.3873578 1.4265948
 [92] 0.7212619 0.7425993 1.0696432 2.4520585 3.0555819 3.1000885 1.0945292
 [99] 0.3726928        NA
Warning message:
In rollapply.zoo(zoo(data), ...) : na.pad argument is deprecated

fill = NA您可以通过使用参数来消除警告，如

> rollapply(vec, width = 3, FUN = sd, fill = NA)
于 2012-11-02T12:52:25.653 回答
21

TTR 包runSD包括：

> library(TTR)
> ls("package:TTR", pattern="run*")
 [1] "runCor"    "runCov"    "runMAD"    "runMax"    "runMean"  
 [6] "runMedian" "runMin"    "runSD"     "runSum"    "runVar"

runSD将比rollapply因为它避免进行许多 R 函数调用要快得多。例如：

rzoo <- function(x,n) rollapplyr(x, n, sd, fill=NA)
rttr <- function(x,n) runSD(x, n)
library(rbenchmark)
set.seed(21)
x <- rnorm(1e4)
all.equal(rzoo(x,250), rttr(x,250))
# [1] TRUE
benchmark(rzoo(x,250), rttr(x,250))[,1:6]
#           test replications elapsed relative user.self sys.self
# 2 rttr(x, 250)          100    0.58    1.000      0.58     0.00
# 1 rzoo(x, 250)          100   54.53   94.017     53.85     0.06
于 2012-11-02T14:20:40.467 回答
18

rollapply在zoo包中采用任意函数。它与默认情况下filter不包括s 不同。NA

话虽如此，但为一个很容易自己滚动的函数加载一个包并没有多大意义（双关语）。

这是右对齐的：

my.rollapply <- function(vec, width, FUN) 
    sapply(seq_along(vec), 
           function(i) if (i < width) NA else FUN(vec[i:(i-width+1)]))

set.seed(1)
vec <- sample(1:50, 50)
my.rollapply(vec, 3, sd)
 [1]        NA        NA  7.094599 12.124356 16.522712 18.502252 18.193405  7.234178  8.144528
[10] 14.468356 12.489996  3.055050 20.808652 19.467922 18.009257 18.248288 15.695010  7.505553
[19] 10.066446 11.846237 17.156146  6.557439  5.291503 23.629078 22.590558 21.197484 22.810816
[28] 24.433583 19.502137 16.165808 11.503623 12.288206  9.539392 13.051181 13.527749 19.974984
[37] 19.756855 17.616280 19.347696 18.248288 15.176737  6.082763 10.000000 10.016653  4.509250
[46]  2.645751  1.527525  5.291503 10.598742  6.557439

# rollapply output for comparison
rollapply(vec, width=3, sd, fill=NA, align='right')
 [1]        NA        NA  7.094599 12.124356 16.522712 18.502252 18.193405  7.234178  8.144528
[10] 14.468356 12.489996  3.055050 20.808652 19.467922 18.009257 18.248288 15.695010  7.505553
[19] 10.066446 11.846237 17.156146  6.557439  5.291503 23.629078 22.590558 21.197484 22.810816
[28] 24.433583 19.502137 16.165808 11.503623 12.288206  9.539392 13.051181 13.527749 19.974984
[37] 19.756855 17.616280 19.347696 18.248288 15.176737  6.082763 10.000000 10.016653  4.509250
[46]  2.645751  1.527525  5.291503 10.598742  6.557439
于 2012-11-02T12:47:40.337 回答
0

runnerrunner包中的函数在运行的窗口上应用任何 R 函数。可以通过runner设置长度k或指定简单窗口lag。下面sd按照 OOP 在 4 元素窗口上的建议移动。

在此处输入图像描述

library(runner)

set.seed(1)
x <- rnorm(20, sd = 1)
runner(x, sd, k = 4, na_pad = TRUE)

#[1]        NA        NA        NA 1.1021597 0.9967429 1.1556947 0.9884053 0.6902835 0.7180483 0.4647160
#[11] 0.7454670 0.7489618 0.9449882 1.5821988 1.4459037 1.3889432 1.3954101 0.6193867 0.5296744 0.4266423

要在日期窗口上应用运行函数，应该指定idx. 的长度idx应与日期或整数类型相同，x并且应为日期或整数类型。下面的示例说明了大小k = 4滞后的窗口lag = 1。每个窗口的括号中的索引范围。

在此处输入图像描述

idx <- c(4, 6, 7, 13, 17, 18, 18, 21, 27, 31, 37, 42, 44, 47, 48)
runner::runner(x = 1:15, 
               k = 5,
               lag = 1,
               idx = idx,
               f = function(x) mean(x))

# [1]   NA  1.0  1.5   NA  4.0  4.5  4.5  6.0   NA  9.0   NA 11.0 12.0 12.5 13.5

文档和小插图中的更多信息

于 2020-01-27T18:10:31.963 回答