profGenerate | R Documentation |

Generates profile (binned) data in a given range from an indexed pair of vectors.

```
profBin(x, y, num, xstart = min(x), xend = max(x), param = list())
profBinM(x, y, zidx, num, xstart = min(x), xend = max(x), NAOK = FALSE,
param = list())
profBinLin(x, y, num, xstart = min(x), xend = max(x), param = list())
profBinLinM(x, y, zidx, num, xstart = min(x), xend = max(x), NAOK = FALSE,
param = list())
profBinLinBase(x, y, num, xstart = min(x), xend = max(x), param = list())
profBinLinBaseM(x, y, zidx, num, xstart = min(x), xend = max(x), NAOK = FALSE,
param = list())
profIntLin(x, y, num, xstart = min(x), xend = max(x), param = list())
profIntLinM(x, y, zidx, num, xstart = min(x), xend = max(x), NAOK = FALSE,
param = list())
profMaxIdx(x, y, num, xstart = min(x), xend = max(x), param = list())
profMaxIdxM(x, y, zidx, num, xstart = min(x), xend = max(x), NAOK = FALSE,
param = list())
```

`x` |
numeric vector of value positions |

`y` |
numeric vector of values to bin |

`zidx` |
starting position of each new segment |

`num` |
number of equally spaced x bins |

`xstart` |
starting x value |

`xend` |
ending x value |

`NAOK` |
allow NA values (faster) |

`param` |
parameters for profile generation |

These functions take a vector of unequally spaced `y`

values
and transform them into either a vector or matrix, depending on
whether there is an index or not. Each point in the vector or
matrix represents the data for the point centered at its corresponding
`x`

value, plus or minus half the `x`

step size
(`xend-xstart/(num-1)`

).

The `Bin`

functions set each matrix or vector value to the
maximal point that gets binned into it.

The `BinLin`

functions do the same except that they linearly
interpolate values into which nothing was binned.

The `BinLinBase`

functions do the same except that they populate
empty parts of spectra with a base value. They take to two parameters:
1) `baselevel`

, the intensity level to fill in for empty parts
of the spectra. It defaluts to half of the minimum intensity. 2)
`basespace`

, the m/z length after which the signal will drop to
the base level. Linear interpolation will be used between consecuitive
data points falling within `2*basespace`

of eachother. It defaluts
to 0.075.

The `IntLin`

functions set each matrix or vector value to
the integral of the linearly interpolated data from plus to minus
half the step size.

The `MaxIdx`

functions work similarly to the `Bin`

functions execpt that the return the integer index of which x,y
pair would be placed in a particular cell.

For `prof*`

, a numeric vector of length `num`

.

For `prof*M`

, a matrix with dimensions `num`

by
`length(zidx)`

.

For `MaxIdx`

, the data type is integer, for all others it
is double.

There are some issues with the `profBinLin`

method, see
https://github.com/sneumann/xcms/issues/46 and
https://github.com/sneumann/xcms/issues/49. Thus it is suggested
to use the functions `binYonX`

in combination with
`imputeLinInterpol`

instead.

Colin A. Smith, csmith@scripps.edu

```
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xraw <- xcmsRaw(cdffiles[1])
image(xraw) ## not how with intLin the intensity's blur
profMethod(xraw) <- "bin"
image(xraw) ## now with 'bin' there is no blurring good for centroid data
##try binlinbase for profile data
## End(Not run)
```

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