Question

我正在处理NDVI Time-Series一年内23 observations的数据。我能够检测14 - 19 observation之间出现的峰值。现在我想找到start and end of the Peak。通过使用diff function查找符号更改，我可以找到峰值的开始和结束。但在某些情况下，我注意到能够找到结束，因为峰值的结束是在明年。解决方案是在23次观察后重复这些值，使其循环并找到结束。

下面给出的例子将详细解释问题

x = c(250.7943,292.2904,340.459,368.811,363.4534,330.2302,291.6527,275.2815,299.9305,367.0331,461.2618,559.0772,639.6197,691.723,713.9833,709.5409,680.4415,626.1153,547.0395,450.4623,353.0839,277.257,241.597)

我正在寻找从山顶向两个方向的标志变化，并且能够在8点观察时找到峰值的开始但是当我从峰值开始寻找结束时我直到23才能找到任何变化。在这种情况下，我应该在23处结束峰值。如表所示，我在Excel中手动重复这些值以获得符号更改。

如何在R ???

中完成此操作

一个解决方案可能是设置一个条件来检查是否找不到符号更改，直到23次观察，然后所有23个值将在向量的末尾填充，然后查找符号更改。

有没有一种简单的方法可以实现这个目标？

Answer 1

另一种可能性：（1）使用前导和滞后Inf填充您的值，以在时间序列的开头和结尾创建虚拟局部最小值*。（2）查找所有最小值（包括假人）的索引。（3）找到最大值旁边的两个最小值的索引。

# pad values with Inf and get indexes of all local minima
i.mins  <- which(diff(sign(diff(c(Inf, x, Inf)))) == 2)

# index of max value
i.mx <- which.max(x)

# combine indexes of local minima and the max
i <- sort(c(i.mins, i.mx))

# select the two minima on either side of the max  
ix <- i[which(i == i.mx) + c(-1, 1)]
ix 
# [1]  8 23

plot(x, type = "b")
points(x = c(ix[1], i.mx, ix[2]),
       y = c(x[ix[1]], max(y), x[ix[2]]),
       col = c("blue", "red", "blue"), pch = 19, cex = 2)

*参见例如Finding local maxima and minima

Answer 2

这只是为了创建一个可重现的例子：

y = data.frame(x = x, y = c(x[2:length(x)], NA))
y$diff <- y$y - y$x

然后我们开始生成一个新列：

y$startEndPeak <- NA

然后我们遍历data.frame，保存所有差异记录。因此，我们通过比较所有差异与之前的对应部分来识别起点/终点和峰值：

for(i in 2:(nrow(y) - 1)){
  thisDif <- y$diff[i]
  prevDif <- y$diff[i-1]

  if (thisDif < 0 && prevDif > 0){
    y$startEndPeak[i] <- "start/end"
  }

  if (thisDif > 0 && prevDif < 0){
    y$startEndPeak[i] <- "peak"
  }

}
y

#         x        y     diff      startEndPeak
#   1  250.7943 292.2904  41.4961         <NA>
#   2  292.2904 340.4590  48.1686         <NA>
#   3  340.4590 368.8110  28.3520         <NA>
#   4  368.8110 363.4534  -5.3576    start/end
#   5  363.4534 330.2302 -33.2232         <NA>
#   6  330.2302 291.6527 -38.5775         <NA>
#   7  291.6527 275.2815 -16.3712         <NA>
#   8  275.2815 299.9305  24.6490         peak
#   9  299.9305 367.0331  67.1026         <NA>
#   10 367.0331 461.2618  94.2287         <NA>
#   11 461.2618 559.0772  97.8154         <NA>
#   12 559.0772 639.6197  80.5425         <NA>
#   13 639.6197 691.7230  52.1033         <NA>
#   14 691.7230 713.9833  22.2603         <NA>
#   15 713.9833 709.5409  -4.4424    start/end
#   16 709.5409 680.4415 -29.0994         <NA>
#   17 680.4415 626.1153 -54.3262         <NA>
#   18 626.1153 547.0395 -79.0758         <NA>
#   19 547.0395 450.4623 -96.5772         <NA>
#   20 450.4623 353.0839 -97.3784         <NA>
#   21 353.0839 277.2570 -75.8269         <NA>
#   22 277.2570 241.5970 -35.6600         <NA>
#   23 241.5970       NA       NA         <NA>

然后我们使用向量来放置起点和终点

y$startEndPeak[which(y$startEndPeak == "start/end")] <- c("start", "end")
y
#            x        y     diff startEndPeak
# ...........
#   3  340.4590 368.8110  28.3520         <NA>
#   4  368.8110 363.4534  -5.3576        start
# ...........
#   8  275.2815 299.9305  24.6490         peak
# ...........
#   15 713.9833 709.5409  -4.4424          end
# ...........

Answer 3

Usimg Loki的方法我能够部分解决我的问题......

y = data.frame(x = x, y = c(x[2:length(x)], x[1]))

y$diff <- y$y - y$x
y$startEndPeak <- NA

for(i in 2:(nrow(y))){
  thisDif <- y$diff[i]
  prevDif <- y$diff[i-1]

  if (thisDif < 0 && prevDif > 0){
      y$startEndPeak[i] <- "peak"
     }


  if (thisDif > 0 && prevDif < 0){
      y$startEndPeak[i-1] <- "end"
      y$startEndPeak[i] <- "start"
   }
}

y
  #      x        y     diff startEndPeak
  # 250.7943 292.2904  41.4961         <NA>
  # 292.2904 340.4590  48.1686         <NA>
  # 340.4590 368.8110  28.3520         <NA>
  # 368.8110 363.4534  -5.3576         peak
  # 363.4534 330.2302 -33.2232         <NA>
  # 330.2302 291.6527 -38.5775         <NA>
  # 291.6527 275.2815 -16.3712          end
  # 275.2815 299.9305  24.6490        start
  # 299.9305 367.0331  67.1026         <NA>
  # 367.0331 461.2618  94.2287         <NA>
  # 461.2618 559.0772  97.8154         <NA>
  # 559.0772 639.6197  80.5425         <NA>
  # 639.6197 691.7230  52.1033         <NA>
  # 691.7230 713.9833  22.2603         <NA>
  # 713.9833 709.5409  -4.4424         peak
  # 709.5409 680.4415 -29.0994         <NA>
  # 680.4415 626.1153 -54.3262         <NA>
  # 626.1153 547.0395 -79.0758         <NA>
  # 547.0395 450.4623 -96.5772         <NA>
  # 450.4623 353.0839 -97.3784         <NA>
  # 353.0839 277.2570 -75.8269         <NA>
  # 277.2570 241.5970 -35.6600          end
  # 241.5970 250.7943   9.1973        start

在R

3 个答案: