Description Usage Arguments Details Value See Also Examples

This function identifies peaks in time series and helps to identify the time window of the first maximum according to given rules.

1 | ```
peakwindow(x, y = NULL, xstart = 0, xmax = max(x), minpeak = 0.1, mincut = 0.382)
``` |

`x, y` |
the x (in day of year) and y coordinates of a set of points. Alternatively, a single argument x can be provided. |

`xstart` |
x value (e.g. time of ice-out) before the maximum value of the searched peak (this is a “weak” limit), |

`xmax` |
maximum of the end of the searched peak (this is a “hard” maximum, |

`minpeak` |
minimum value of the total maximum which is regarded as peak, |

`mincut` |
minimum relative height of a pit compared to the lower of the two neighbouring maxima at which these maxima are regarded as separate peaks (default value is derived from golden section). |

This is a heuristic peak detection algorithm. It can be used for two related purposes, (i) to identify all relevant peaks within a time-series and (ii) to identify the time window which belongs to one single peak (smd = specified mass development, e.g. spring maximum in phytoplankton time series).

A list with the following elements:

`peaks` |
a data frame with the characteristics (index, xleft, x, xright and y) of all identified peaks, |

`data` |
the original data set ( |

`smd.max.index` |
index of the maximum value of the “specified” peak, |

`smd.max.x` |
x-value of the maximum of the “specified” peak, |

`smd.indices` |
indices (data window) of all data belonging to the “specified” peak, |

`smd.x` |
x-values (time window) of all data belonging to the “specified” peak, |

`smd.y` |
corresponding y-values of all data belonging to the “specified” peak, |

`peakid` |
vector with peak-id-numbers for all data. |

`weibull4`

,
`weibull6`

,
`fitweibull`

,
`CDW`

`plot.cardiPeakwindow`

`cardidates`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ```
## generate test data with 3 peaks
set.seed(123)
x <- seq(0, 360, length = 20)
y <- abs(rnorm(20, mean = 1, sd = 0.1))
y[5:10] <- c(2, 4, 7, 3, 4, 2)
y <- c(y, 0.8 * y, 1.2 * y)
x <- seq(0, 360, along = y)
y[6] <- y[7] # test case with 2 neighbouring equal points
## plot the test data
plot(x, y, type="b")
## identify the "spring mass development"
peaks <- peakwindow(x, y)
ind <- peaks$smd.indices
lines(x[ind], y[ind], col="red", lwd=2)
## now fit the cardinal dates
fit <- fitweibull6(peaks$smd.x, peaks$smd.y)
CDW(fit)
plot(fit)
## some more options ...
peaks <- peakwindow(x, y, xstart=150, mincut = 0.455)
ind <- peaks$smd.indices
lines(x[ind], y[ind], col = "blue")
points(x, y, col = peaks$peakid +1, pch = 16) # all peaks
## work with indices only
peaks <- peakwindow(y)
## test case with disturbed sinus
x<- 1:100
y <- sin(x/5) +1.5 + rnorm(x, sd = 0.2)
peaks <- peakwindow(x, y)
plot(x, y, type = "l", ylim = c(0, 3))
points(x, y, col = peaks$peakid + 2, pch = 16)
## test case: only one peak
yy <- c(1:10, 11:1)
peakwindow(yy)
## error handling test case: no turnpoints
# yy <- rep(1, length(x))
# peakwindow(x, yy)
``` |

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