peaks_find {alkahest} | R Documentation |

## Find Peaks

### Description

Finds local maxima in sequential data.

### Usage

```
peaks_find(x, y, ...)
## S4 method for signature 'numeric,numeric'
peaks_find(x, y, method = "MAD", SNR = 2, m = NULL, ...)
## S4 method for signature 'ANY,missing'
peaks_find(x, method = "MAD", SNR = 2, m = NULL, ...)
```

### Arguments

`x` , `y` |
A |

`...` |
Extra parameters to be passed to internal methods. |

`method` |
A |

`SNR` |
An |

`m` |
An odd |

### Details

A local maximum has to be the highest one in the given window and has to be
higher than `SNR \times noise`

to be recognized as peak.

The following methods are available for noise estimation:

`MAD`

Median Absolute Deviation.

Note that to improve peak detection, it may be helpful to smooth the data and remove the baseline beforehand.

### Value

Returns a `list`

with two components `x`

and `y`

.

### Note

There will be `(m - 1) / 2`

points both at the beginning and at the end
of the data series for which a complete `m`

-width window cannot be
obtained. To prevent data loss, progressively wider/narrower windows are
used at both ends of the data series.

Adapted from Stasia Grinberg's
`findPeaks`

function.

### Author(s)

N. Frerebeau

### See Also

Other peaks detection methods:
`peaks_fwhm()`

### Examples

```
## X-ray diffraction
data("XRD")
## 4S Peak Filling baseline
baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)
plot(XRD, type = "l", xlab = expression(2*theta), ylab = "Count")
lines(baseline, type = "l", col = "red")
## Correct baseline
XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)
## Find peaks
peaks <- peaks_find(XRD, SNR = 3, m = 11)
plot(XRD, type = "l", xlab = expression(2*theta), ylab = "Count")
lines(peaks, type = "p", pch = 16, col = "red")
abline(h = attr(peaks, "noise"), lty = 2) # noise threshold
## Half-Width at Half-Maximum
x <- seq(-4, 4, length = 1000)
y <- dnorm(x)
peaks_fwhm(x, y, center = 0) # Expected: 2 * sqrt(2 * log(2))
```

*alkahest*version 1.1.1 Index]