R函数从向量的后续值中减去向量的连续值之间的差

问题描述

我有一个数字矢量，其中包含重量数据的时间序列.我需要做的是通过识别连续权重之间的不切实际差异，并从系列中的所有后续值中减去该差异，来消除权重变化伪影.

I have a numerical vector that contains a time series of weight data. What I need to do is remove weight change artifacts by identifying unrealistic differences between consecutive weights and subtracting that change from all subsequent values in the series.

例如，在系列:c(5,4,6,8,8,9,8,12,10,100,101,101)中，我将第9项和第10项(100-10 = 90)之间的增量权重标识为一个伪像，我会通过从后续值中减去90来更正它，得出c(5,4,6,8,8,9,8,12,10,10,11,11).

For example, in the series: c(5,4,6,8,8,9,8,12,10,100,101,101), I would identify the delta weight between items 9 and 10 (100 - 10 = 90) as an artifact, and I would correct it by subtracting 90 from the subsequent values, yielding c(5,4,6,8,8,9,8,12,10,10,11,11).

原则上，我的代码如下所示:

In principal, my code would look something like:

cancel_artifacts <- function(weights, delta_max) {
    for (i in 0:length(weights)) {
        if (weights[i] - weights[i-1] > abs(delta_max)) {
            weights[i:length(weights)] <- weights[i:length(weights)] - (weights[i] - weights[i-1])
        }
    }

很明显，我的语法简直是一场灾难.有人可以帮我设置吗?

Obviously my syntax is a disaster. Can anyone help me set this up?

推荐答案

您可以采用矢量化方式进行操作:

You can do this in a vectorized manner:

remove_artifacts <- function(weights, delta_max) {
  # calculate deltas, and set first delta to zero
  dw <- c(0, diff(x))
  # create vector of zeros and abs(observations) > delta_max
  # dw * (logical vector) results in either:
  # dw * 0 (if FALSE)
  # dw * 1 (if TRUE)
  dm <- dw * (abs(dw) > delta_max)
  # subtract the cumulative sum of observations > delta_max
  return(weights - cumsum(dm))
}
x <- c(5, 4, 6, 8, 8, 9, 8, 12, 10, 100, 101, 101)
remove_artifacts(x, 50)
# [1]  5  4  6  8  8  9  8 12 10 10 11 11

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