我试着用R来计算矩阵中一系列值的移动平均值。R中似乎没有一个内置函数可以让我计算移动平均线。有任何软件包提供这样的服务吗?还是需要我自己写?
当前回答
为了配合坎迪奇西斯和罗德里戈·雷麦黛奥的回答;
moving_fun <- function(x, w, FUN, ...) {
# x: a double vector
# w: the length of the window, i.e., the section of the vector selected to apply FUN
# FUN: a function that takes a vector and return a summarize value, e.g., mean, sum, etc.
# Given a double type vector apply a FUN over a moving window from left to the right,
# when a window boundary is not a legal section, i.e. lower_bound and i (upper bound)
# are not contained in the length of the vector, return a NA_real_
if (w < 1) {
stop("The length of the window 'w' must be greater than 0")
}
output <- x
for (i in 1:length(x)) {
# plus 1 because the index is inclusive with the upper_bound 'i'
lower_bound <- i - w + 1
if (lower_bound < 1) {
output[i] <- NA_real_
} else {
output[i] <- FUN(x[lower_bound:i, ...])
}
}
output
}
# example
v <- seq(1:10)
# compute a MA(2)
moving_fun(v, 2, mean)
# compute moving sum of two periods
moving_fun(v, 2, sum)
其他回答
可以使用runner包来移动函数。在本例中是mean_run函数。cummean的问题是它不处理NA值,但mean_run可以。Runner包还支持不规则时间序列,Windows可以依赖于日期:
library(runner)
set.seed(11)
x1 <- rnorm(15)
x2 <- sample(c(rep(NA,5), rnorm(15)), 15, replace = TRUE)
date <- Sys.Date() + cumsum(sample(1:3, 15, replace = TRUE))
mean_run(x1)
#> [1] -0.5910311 -0.2822184 -0.6936633 -0.8609108 -0.4530308 -0.5332176
#> [7] -0.2679571 -0.1563477 -0.1440561 -0.2300625 -0.2844599 -0.2897842
#> [13] -0.3858234 -0.3765192 -0.4280809
mean_run(x2, na_rm = TRUE)
#> [1] -0.18760011 -0.09022066 -0.06543317 0.03906450 -0.12188853 -0.13873536
#> [7] -0.13873536 -0.14571604 -0.12596067 -0.11116961 -0.09881996 -0.08871569
#> [13] -0.05194292 -0.04699909 -0.05704202
mean_run(x2, na_rm = FALSE )
#> [1] -0.18760011 -0.09022066 -0.06543317 0.03906450 -0.12188853 -0.13873536
#> [7] NA NA NA NA NA NA
#> [13] NA NA NA
mean_run(x2, na_rm = TRUE, k = 4)
#> [1] -0.18760011 -0.09022066 -0.06543317 0.03906450 -0.10546063 -0.16299272
#> [7] -0.21203756 -0.39209010 -0.13274756 -0.05603811 -0.03894684 0.01103493
#> [13] 0.09609256 0.09738460 0.04740283
mean_run(x2, na_rm = TRUE, k = 4, idx = date)
#> [1] -0.187600111 -0.090220655 -0.004349696 0.168349653 -0.206571573 -0.494335093
#> [7] -0.222969541 -0.187600111 -0.087636571 0.009742884 0.009742884 0.012326968
#> [13] 0.182442234 0.125737145 0.059094786
还可以指定其他选项,如延迟和仅在特定索引处滚动。更多内容在包和函数文档中。
使用费用应充分、有效。假设你有一个向量x,你想要n个数的和
cx <- c(0,cumsum(x))
rsum <- (cx[(n+1):length(cx)] - cx[1:(length(cx) - n)]) / n
正如@mzuther在评论中指出的那样,这假设数据中没有NAs。要处理这些问题,需要将每个窗口除以非na值的数量。这里有一种方法,结合@里卡多·克鲁兹的评论:
cx <- c(0, cumsum(ifelse(is.na(x), 0, x)))
cn <- c(0, cumsum(ifelse(is.na(x), 0, 1)))
rx <- cx[(n+1):length(cx)] - cx[1:(length(cx) - n)]
rn <- cn[(n+1):length(cx)] - cn[1:(length(cx) - n)]
rsum <- rx / rn
这仍然有一个问题,如果窗口中的所有值都是NAs,那么将会有一个零误差的除法。
vector_avg <- function(x){
sum_x = 0
for(i in 1:length(x)){
if(!is.na(x[i]))
sum_x = sum_x + x[i]
}
return(sum_x/length(x))
}
下面是一个简单的带有过滤器的函数,演示了一种方法来处理带有填充的开始和结束NAs,并使用自定义权重计算加权平均值(由过滤器支持):
wma <- function(x) {
wts <- c(seq(0.5, 4, 0.5), seq(3.5, 0.5, -0.5))
nside <- (length(wts)-1)/2
# pad x with begin and end values for filter to avoid NAs
xp <- c(rep(first(x), nside), x, rep(last(x), nside))
z <- stats::filter(xp, wts/sum(wts), sides = 2) %>% as.vector
z[(nside+1):(nside+length(x))]
}
caTools包具有非常快速的滚动mean/min/max/sd和其他一些功能。我只使用过runmean和runsd,它们是迄今为止提到的其他包中最快的。