recalibrate {RMassBank} | R Documentation

## Predefined recalibration functions.

### Description

Predefined fits to use for recalibration: Loess fit and GAM fit.

### Usage

    recalibrate.loess(rcdata)

    		recalibrate.identity(rcdata)

    		recalibrate.mean(rcdata)

    		recalibrate.linear(rcdata)

### Arguments

`rcdata` | A data frame with at least the columns `recalfield` and `mzFound`. `recalfield` will usually contain delta(ppm) or delta(mz) values and is the target parameter for the recalibration.

### Details

`recalibrate.loess()` provides a Loess fit (`recalibrate.loess`) to a given recalibration parameter. If MS and MS/MS data should be fit together, recalibrate.loess provides good default settings for Orbitrap instruments.

`recalibrate.identity()` returns a non-recalibration, i.e. a predictor which predicts 0 for all input values. This can be used if the user wants to skip recalibration in the RMassBank workflow.

#' `recalibrate.mean()` and `recalibrate.linear()` are simple recalibrations which return a constant shift or a linear recalibration. They will be only useful in particular cases.

`recalibrate()` itself is only a dummy function and does not do anything.

Alternatively other functions can be defined. Which functions are used for recalibration is specified by the RMassBank options file. (Note: if `recalibrateMS1: common`, the `recalibrator: MS1` value is irrelevant, since for a common curve generated with the function specified in `recalibrator: MS2` will be used.)

### Value

Returns a model for recalibration to be used with `predict` and the like.

### Author(s)

Michael Stravs, EAWAG <michael.stravs@eawag.ch>

### Examples

    ## Not run:
    rcdata <- subset(spec$peaksMatched, formulaCount==1)
    ms1data <- recalibrate.addMS1data(spec, mode, 15)
    rcdata <- rbind(rcdata, ms1data)
    rcdata$recalfield <- rcdata$dppm
    rcCurve <- recalibrate.loess(rcdata)
    # define a spectrum and recalibrate it
    s <- matrix(c(100,150,200,88.8887,95.0005,222.2223), ncol=2)
    colnames(s) <- c("mz", "int")
    recalS <- recalibrateSingleSpec(s, rcCurve)

    Alternative: define an custom recalibrator function with different parameters
    recalibrate.MyOwnLoess <- function(rcdata)
    {
    	return(loess(recalfield ~ mzFound, data=rcdata, family=c("symmetric"),
    					degree = 2, span=0.4))
    }
    # This can then be specified in the RMassBank settings file:
    # recalibrateMS1: common
    # recalibrator:
    #    MS1: recalibrate.loess
    #    MS2: recalibrate.MyOwnLoess")
    # [...]

    ## End(Not run)

[Package RMassBank version 3.0.0 Index]