用精确准则和无for循环求噪声数据的局部极小值

我有一个时间表，希望找到满足特定标准的最小值。即两个峰值之间的谷内（红线下方）的点数应超过某个值，并且对于相邻谷的每个峰值，红线上方的点数应超过某个值。此外，两个峰值中较低的峰值应在低于其高度50%的值下解析（这意味着两个峰值中较低的峰值的最大（强度）应至少为两个峰值之间谷内最低强度强度的2倍，如下代码示例中计算）。我在这里画了一条特定高度的红线，但实际上，这些未解决的山峰可以有任何高度，也可以被任何距离分开。所以我现在做的是用红线“扫描”时间序列的每个点，这当然是非常缓慢和低效的

下面是我目前使用的for循环：

detect_double_peaks <- function(pot.doubleP.v, Min.PpP = 10) {

  peak.dt <-
    data.table(
      breakP = NA,
      breakH = NA,
      resolved = NA
    )


  for (point in pot.doubleP.v[pot.doubleP.v > 0 &
                              pot.doubleP.v < 0.8 * max(pot.doubleP.v)]) {
    doublePeak.rle <- S4Vectors::Rle(pot.doubleP.v > point)
    doublePeak.rle.dt <-
      data.table(
        idx = as.numeric(seq.int(length(
          doublePeak.rle@values
        ))),
        values = doublePeak.rle@values,
        lengths = doublePeak.rle@lengths,
        start = start(doublePeak.rle),
        end = end(doublePeak.rle)
      )

    doublePeak.rle.dt_p <-
      doublePeak.rle.dt[values == TRUE & lengths > Min.PpP]

    if (nrow(doublePeak.rle.dt_p) > 1) { 


      for(peak in 1:nrow(doublePeak.rle.dt_p)-1){
        doublePeak.rle.dt_v <- doublePeak.rle.dt[idx > doublePeak.rle.dt_p[peak]$idx & idx < doublePeak.rle.dt_p[peak + 1]$idx]


        if(sum(doublePeak.rle.dt_v[values == FALSE]$lengths) >= max(max(doublePeak.rle.dt_p[peak]$lengths, doublePeak.rle.dt_p[peak+1]$lengths) * 0.5, Min.PpP)){


          dp.p_height_h <-
            max(max(pot.doubleP.v[(doublePeak.rle.dt_p[peak]$start):(doublePeak.rle.dt_p[peak]$end)]),
                max(pot.doubleP.v[(doublePeak.rle.dt_p[peak + 1]$start):(doublePeak.rle.dt_p[peak + 1]$end)]))# - baseL

          dp.p_height_l <-
            min(max(pot.doubleP.v[(doublePeak.rle.dt_p[peak]$start):(doublePeak.rle.dt_p[peak]$end)]),
                max(pot.doubleP.v[(doublePeak.rle.dt_p[peak + 1]$start):(doublePeak.rle.dt_p[peak + 1]$end)]))# - baseL

          breakH <-
            min(pot.doubleP.v[min(doublePeak.rle.dt[idx > doublePeak.rle.dt_p[peak]$idx]$start):max(doublePeak.rle.dt[idx < doublePeak.rle.dt_p[peak+1]$idx]$end)])# - baseL

          resolved <-
            breakH / dp.p_height_l * 100

          breakP <-
            which.min(pot.doubleP.v[min(doublePeak.rle.dt[idx > doublePeak.rle.dt_p[peak]$idx]$start):max(doublePeak.rle.dt[idx < doublePeak.rle.dt_p[peak+1]$idx]$end)]) +
            doublePeak.rle.dt_p[peak]$end

          peak.dt <- rbind(peak.dt,
                           data.table(breakP = breakP,
                                      breakH = breakH,
                                      resolved = resolved))
        }
      }
    }
    }


  if(nrow(peak.dt) == 1) {return(NULL)} else{
    return(na.omit(unique(peak.dt, by="breakP")))
  }
}

detect_double_peaks=max（max（doublePeak.rle.dt_p[peak]$长度，doublePeak.rle.dt_p[peak+1]$长度）*0.5，Min.PpP））{
dp.p_height_h你能再解释一下找到56和125的条件吗？这很混乱。你需要找到局部极小值？为什么不返回4？我编辑了这些问题。希望这使问题更容易理解“…两个峰值中较低的应在低于其高度50%的值下解析…”中“解析”的含义是什么？此外——该条款中“高度”的含义是什么？它意味着谷中的smalles值与谷旁两个峰值中较小者的最大值之间的比率应低于0.5:
min（谷强度）/min（max（相邻峰强度））<0.5
你能再解释一下找到56和125的条件吗？这很混乱。你需要找到局部极小值？为什么不返回4？我编辑了这些问题。希望这使问题更容易理解“…两个峰值中较低的应在低于其高度50%的值下解析…”中“解析”的含义是什么？此外——该条款中“高度”的含义是什么？它意味着谷中的smalles值与谷旁两个峰值中较小者的最大值之间的比率应低于0.5:
min（谷强度）/min（max（相邻峰强度））<0.5
testvector <- c(13126.177734375, 12040.060546875, 10810.6171875, 10325.94140625, 
13492.8359375, 33648.5703125, 14402.603515625, 29920.12890625, 
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36980.3125, 43365.76171875, 40480.75, 39057.96484375, 31622.58984375, 
23830.455078125, 27393.30078125, 30675.208984375, 27327.48046875, 
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24208.515625, 31496.083984375, 26250.646484375, 14318.302734375
)