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
或者你可以简单地计算它使用过滤器,这是我使用的函数:
ma <- function(x, n = 5){filter(x, rep(1 / n, n), sides = 2)}
如果使用dplyr,请注意在上面的函数中指定stats::filter。
其他回答
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)
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))]
}
我使用聚合和一个由rep()创建的向量。这样做的好处是可以使用cbind()一次在数据帧中聚合1个以上的列。下面是一个长度为1000的向量(v)的移动平均值为60的例子:
v=1:1000*0.002+rnorm(1000)
mrng=rep(1:round(length(v)/60+0.5), length.out=length(v), each=60)
aggregate(v~mrng, FUN=mean, na.rm=T)
请注意,rep中的第一个参数只是根据向量的长度和要平均的数量,为移动范围获取足够的唯一值;第二个参数保持长度等于向量长度,最后一个参数重复第一个参数的值的次数与平均周期相同。
总的来说,你可以使用几个函数(中值,最大值,最小值)-例如所示的平均值。同样,could可以使用cbind公式对数据帧中的多个(或所有)列执行此操作。
编辑:非常喜欢添加侧参数,例如,一个日期向量的过去7天的移动平均值(或总和,或…)。
对于那些只想自己计算的人来说,它无非是:
# x = vector with numeric data
# w = window length
y <- numeric(length = length(x))
for (i in seq_len(length(x))) {
ind <- c((i - floor(w / 2)):(i + floor(w / 2)))
ind <- ind[ind %in% seq_len(length(x))]
y[i] <- mean(x[ind])
}
y
但是让它独立于mean()会很有趣,所以你可以计算任何“移动”函数!
# our working horse:
moving_fn <- function(x, w, fun, ...) {
# x = vector with numeric data
# w = window length
# fun = function to apply
# side = side to take, (c)entre, (l)eft or (r)ight
# ... = parameters passed on to 'fun'
y <- numeric(length(x))
for (i in seq_len(length(x))) {
if (side %in% c("c", "centre", "center")) {
ind <- c((i - floor(w / 2)):(i + floor(w / 2)))
} else if (side %in% c("l", "left")) {
ind <- c((i - floor(w) + 1):i)
} else if (side %in% c("r", "right")) {
ind <- c(i:(i + floor(w) - 1))
} else {
stop("'side' must be one of 'centre', 'left', 'right'", call. = FALSE)
}
ind <- ind[ind %in% seq_len(length(x))]
y[i] <- fun(x[ind], ...)
}
y
}
# and now any variation you can think of!
moving_average <- function(x, w = 5, side = "centre", na.rm = FALSE) {
moving_fn(x = x, w = w, fun = mean, side = side, na.rm = na.rm)
}
moving_sum <- function(x, w = 5, side = "centre", na.rm = FALSE) {
moving_fn(x = x, w = w, fun = sum, side = side, na.rm = na.rm)
}
moving_maximum <- function(x, w = 5, side = "centre", na.rm = FALSE) {
moving_fn(x = x, w = w, fun = max, side = side, na.rm = na.rm)
}
moving_median <- function(x, w = 5, side = "centre", na.rm = FALSE) {
moving_fn(x = x, w = w, fun = median, side = side, na.rm = na.rm)
}
moving_Q1 <- function(x, w = 5, side = "centre", na.rm = FALSE) {
moving_fn(x = x, w = w, fun = quantile, side = side, na.rm = na.rm, 0.25)
}
moving_Q3 <- function(x, w = 5, side = "centre", na.rm = FALSE) {
moving_fn(x = x, w = w, fun = quantile, side = side, na.rm = na.rm, 0.75)
}
动物园包中的滚动平均值/最大值/中位数(rollmean) TTR中的移动平均线 马云在预测