profGenerate: Generation of profile data

In anupbharade09/xcms_test: LC/MS and GC/MS Data Analysis

Description

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

Usage

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())

Arguments

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

Details

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.

Value

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.

Note

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.

Author(s)

Colin A. Smith, [e-mail]

Examples

	## 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)

anupbharade09/xcms_test documentation built on May 14, 2019, 4:07 a.m.