Encoding:	UTF-8
Type:	Package
Title:	Baseline Correction of Spectra
Version:	1.3-7
Date:	2025-06-18
Maintainer:	Kristian Hovde Liland <kristian.liland@nmbu.no>
Description:	Collection of baseline correction algorithms, along with a framework and a Tcl/Tk enabled GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.
License:	GPL-2
Copyright:	inst/COPYRIGHTS
Depends:	R (≥ 3.5.0)
Imports:	graphics, SparseM, grDevices, stats, methods
Suggests:	gWidgets2, gWidgets2tcltk, IDPmisc, lattice, pls, MASS
LazyLoad:	true
LazyData:	true
URL:	https://github.com/khliland/baseline/
BugReports:	https://github.com/khliland/baseline/issues/
ZipData:	true
NeedsCompilation:	yes
Packaged:	2025-06-18 10:32:08 UTC; kristian
Author:	Kristian Hovde Liland [aut, cre], Bjørn-Helge Mevik [aut], Roberto Canteri [ctb]
Repository:	CRAN
Date/Publication:	2025-06-18 11:00:05 UTC

Baseline correction

Description

A common framework with implementations of several baseline correction methods

Details

Use function baseline for baseline correction. This function takes matrices of spectra, a method name and parameters needed for the specific method. See helpfiles for details.

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

Maintainer: Kristian Hovde Liland <kristian.liland@nmbu.no>

References

Andreas F. Ruckstuhl, Matthew P. Jacobson, Robert W. Field, James A. Dodd: Baseline subtraction using robust local regression estimation; CHAD A. LIEBER and ANITA MAHADEVAN-JANSEN: Automated Method for Subtraction of Fluorescence from Biological Raman Spectra; Mark S. Friedrichs: A model-free algorithm for the removal of baseline artifacts; AHMET K. ATAKAN, W. E. BLASS, and D. E. JENNINGS: Elimination of Baseline Variations from a Recorded Spectrum by Ultra-low Frequency Filtering; M.A. Kneen, H.J. Annegarn: Algorithm for fitting XRF, SEM and PIXE X-ray spectra backgrounds; K.H. Liland, B.-H. Mevik, E.-O. Rukke, T. Almøy, M. Skaugen and T. Isaksson (2009) Quantitative whole spectrum analysis with MALDI-TOF MS, Part I: Measurement optimisation. Chemometrics and Intelligent Laboratory Systems, 96(2), 210–218.

Examples

# Load data
data(milk)
# The baseline() function is an S4 wrapper for all the different 
# baseline correction methods. The default correction method
# is IRLS. Data must be organized as row vectors in a matrix
# or data.frame.
bc.irls <- baseline(milk$spectra[1,, drop=FALSE])
## Not run: 
  # Computationally heavy
	plot(bc.irls)

## End(Not run)

# Available extractors are:
# getBaseline(bc.irls)
# getSpectra(bc.irls)
# getCorrected(bc.irls)
# getCall(bc.irls)

# Correction methods and parameters can be specified through the wrapper.
bc.fillPeaks <- baseline(milk$spectra[1,, drop=FALSE], lambda=6,
	hwi=50, it=10, int=2000, method='fillPeaks')
## Not run: 
  # Computationally heavy
	plot(bc.fillPeaks)

## End(Not run)

# If a suitable gWidgets2 implementation is installed, a 
# graphical user interface is available for interactive
# parameter adaption.
## Not run: 
  # Dependent on external software
  baselineGUI(milk$spectra)

## End(Not run)

Class "PLSRTest"

Description

A class describing a PLSR prediction test. To run the test, the "pls" package must be installed.

Objects from the Class

Objects can be created by calls of the form new("PLSRTest", ...).

Slots

ncomp:

Integer vector. The number of PLSR components to test

cvsegments:

A list of the segments to use in the cross-validation

Extends

Class predictionTest, directly.

Methods

runTest

signature(object = "PLSRTest"): Run the test

Author(s)

Bjørn-Helge Mevik and Krisitan Hovde Liland

See Also

The base class predictionTest. The runTest function. The plsr function from the "pls" package.

Examples

showClass("PLSRTest")

XPS core line data

Description

Matrix of x,y values from X-Ray Photoelectron Spectroscopy on test sample.
The data are about the Carbon and Oxygen element for 1s shell.

Usage

	data(C1s)
	data(O1s)
	

Format

A matrix with the following 2 variables (rows).

first row

is the abscissa, ( Binding Energy [eV] )

second row

is the Intensity, ( a.u. )

See Also

baseline.shirley

Examples

data(C1s)
data(O1s)
plot(C1s[1,], C1s[2,], type = "l")
plot(O1s[1,], O1s[2,], type = "l")

Extraction methods for "baselineAlgTest" objects

Description

Extraction methods specifically for objects of class baselineAlgTest

Usage

algorithm(object)
extraArgs(object)

Arguments

Value

The corresponding slot

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

baselineAlgTest

Baseline correction

Description

Common framework for baseline correction

Usage

baseline(spectra, method = "irls", ...)

Arguments

Details

Estimates baselines for the spectra, using the algorithm named in method.

Value

An object of class baseline.

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Kristian Hovde Liland, Trygve Almøy, Bjørn-Helge Mevik (2010), Optimal Choice of Baseline Correction for Multivariate Calibration of Spectra, Applied Spectroscopy 64, pp. 1007-1016.

See Also

The functions implementing the baseline algorithms: baseline.als, baseline.fillPeaks, baseline.irls, baseline.lowpass, baseline.medianWindow, baseline.modpolyfit, baseline.peakDetection, baseline.rfbaseline, baseline.rollingBall, baseline.shirley, baseline.TAP

Examples

# Load data
data(milk)
# The baseline() function is an S4 wrapper for all the different 
# baseline correction methods. The default correction method
# is IRLS. Data must be organized as row vectors in a matrix
# or data.frame.
bc.irls <- baseline(milk$spectra[1,, drop=FALSE])
## Not run: 
  # Computationally heavy
	plot(bc.irls)

## End(Not run)

# Available extractors are:
# getBaseline(bc.irls)
# getSpectra(bc.irls)
# getCorrected(bc.irls)
# getCall(bc.irls)

# Correction methods and parameters can be specified through the wrapper.
bc.fillPeaks <- baseline(milk$spectra[1,, drop=FALSE], lambda=6,
	hwi=50, it=10, int=2000, method='fillPeaks')
## Not run: 
  # Computationally heavy
	plot(bc.fillPeaks)

## End(Not run)

# If a suitable gWidgets2 implementation is installed, a 
# graphical user interface is available for interactive
# parameter adaption.
## Not run: 
  # Dependent on external software
  baselineGUI(milk$spectra)

## End(Not run)

Class "baseline"

Description

Stores the result of estimating baselines for one or more spectra.

Objects from the Class

The normal way to create objects is with the function baseline. Several baseline algorithms are available. See baseline for details. There is a plot method for the class; see plot,baseline-method.

Slots

baseline:

A matrix with the estimated baselines

corrected:

A matrix with the corrected spectra

spectra:

A matrix with the original spectra

call:

The call to baseline

Methods

getBaseline

signature(object = "baseline"): Extract the estimated baselines

getCall

signature(object = "baseline"): Extract the call to baseline used to create the object

getCorrected

signature(object = "baseline"): Extract the corrected spectra

getSpectra

signature(object = "baseline"): Extract the original spectra

Warning

In a future versoion, one of the slots might be removed from the class definition and calculated on the fly instead, in order to save space. Therefore, do use the extractor functions (getSpectra, getBaseline and getCorrected) instead of accessing the slots directly.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

baseline, getBaseline, getSpectra, getCorrected, getCall

Examples

showClass("baseline")

TAP

Description

An implementation of Roman Svoboda and Jirí Málek's algorithm for baseline identification in kinetic anlaysis of derivative kinetic data.

Usage

baseline.TAP(spectra, t, interval = 15, tol = 0.001)

Arguments

Details

(i) A first approximation of the baseline equation is selected as the straight line between start and end of the curve. (ii) Based on the first approximation of the baseline equation, the phase change progress parameter is calculated. (iii) An updated equation of the baseline is calculated and the phase change progress parameter equation from step (ii). (iv) The baseline equation from step (iii) is compared (point by point) with the one from the previous iteration. If the convergence criterion is met (the difference between every baseline value corresponding to two successive iterations was less than 0.1%) the procedure is stopped and the final baseline equation is selected. If the convergence criterion is not fulfilled then a new iteration is carried out from step (ii) until convergence was achieved.

Value

Author(s)

Kristian Hovde Liland

References

Roman Svoboda and Jirí Málek: Importance of proper baseline identification for the subsequent kinetic analysis of derivative kinetic data, Journal of Thermal Analysis and Calorimetry.

Examples

# My T
myT <- 40:170

# My artifical curve
myAlpha <- c(seq(0.01, 0.02, length.out=40),
             dnorm(seq(-3,3,length.out=51))/2+(0:50)/2000+0.02)
myAlpha <- c(myAlpha,
             seq(myAlpha[90]-0.001, 0.01, length.out=40))
myAlpha <- myAlpha - min(myAlpha)
myAlpha <- cumsum(dadt <- myAlpha/sum(myAlpha))

# Discrete derivative
mydAlpha <- c(0,diff(myAlpha)); mydAlpha <- matrix(mydAlpha, ncol=length(mydAlpha))
rm(myAlpha) # Throw away myAlpha

# Compute baseline from T and derivative
B <- baseline(mydAlpha, t=myT, method="TAP")

# Plot 
plot(B, xlab = "T", ylab = "da/dT")

Asymmetric Least Squares

Description

Baseline correction by 2nd derivative constrained weighted regression. Original algorithm proposed by Paul H. C. Eilers and Hans F.M. Boelens

Usage

baseline.als(spectra, lambda = 6, p = 0.05, maxit = 20)

Arguments

Details

Iterative algorithm applying 2nd derivative constraints. Weights from previous iteration is p for positive residuals and 1-p for negative residuals.

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Paul H. C. Eilers and Hans F.M. Boelens: Baseline Correction with Asymmetric Least Squares Smoothing

Examples

data(milk)
bc.als <- baseline(milk$spectra[1,, drop=FALSE], lambda=10, method='als')
## Not run: 
plot(bc.als)

## End(Not run)

Fill peaks

Description

An iterative algorithm using suppression of baseline by means in local windows

Usage

baseline.fillPeaks(spectra, lambda, hwi, it, int)

Arguments

Details

In local windows of buckets the minimum of the mean and the previous iteration is chosen as the new baseline

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Kristian Hovde Liland, 4S Peak Filling - baseline estimation by iterative mean suppression, MethodsX 2015

Examples

data(milk)
bc.fillPeaks <- baseline(milk$spectra[1,, drop=FALSE], lambda=6,
	hwi=50, it=10, int=2000, method='fillPeaks')
## Not run: 
	plot(bc.fillPeaks)

## End(Not run)

Iterative Restricted Least Squares

Description

An algorithm with primary smoothing and repeated baseline suppressions and regressions with 2nd derivative constraint

Usage

baseline.irls(spectra, lambda1 = 5, lambda2 = 9, maxit = 200, wi = 0.05)

Arguments

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

Examples

data(milk)
bc.irls <- baseline(milk$spectra[1,, drop=FALSE], method='irls')
## Not run: 
	plot(bc.irls)

## End(Not run)

Low-pass FFT filter

Description

An algorithm for removing baselines based on Fast Fourier Transform filtering

Usage

baseline.lowpass(spectra, steep = 2, half = 5)

Arguments

Details

Since the scale of the spectra will be different after filtering, baselines will not be returned by the algorithm

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

AHMET K. ATAKAN, W. E. BLASS, and D. E. JENNINGS: Elimination of Baseline Variations from a Recorded Spectrum by Ultra-low Frequency Filtering

Examples

data(milk)
bc.lowpass <- baseline(milk$spectra[1,, drop=FALSE], method='lowpass')
## Not run: 
	plot(bc.lowpass)

## End(Not run)

Median window

Description

An implementation and extention of Mark S. Friedrichs' model-free algorithm

Usage

baseline.medianWindow(spectra, hwm, hws, end)

Arguments

Details

An algorithm finding medians in local windows and smoothing with gaussian weighting

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Mark S. Friedrichs: A model-free algorithm for the removal of baseline artifacts

Examples

data(milk)
bc.medianWindow <- baseline(milk$spectra[1,, drop=FALSE], hwm=300,
	method='medianWindow')
## Not run: 
	plot(bc.medianWindow)

## End(Not run)

Modified polynomial fitting

Description

An implementation of CHAD A. LIEBER and ANITA MAHADEVAN-JANSENs algorithm for polynomial fiting

Usage

baseline.modpolyfit(spectra, t, degree = 4, tol = 0.001, rep = 100)

Arguments

Details

Polynomial fitting with baseline suppression relative to original spectrum

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

CHAD A. LIEBER and ANITA MAHADEVAN-JANSEN: Automated Method for Subtraction of Fluorescence from Biological Raman Spectra

Examples

data(milk)
bc.modpolyfit <- baseline(milk$spectra[1,, drop=FALSE], method='modpolyfit', deg=6)
## Not run: 
	plot(bc.modpolyfit)

## End(Not run)

Simultaneous Peak Detection and Baseline Correction

Description

A translation from Kevin R. Coombes et al.'s MATLAB code for detecting peaks and removing baselines

Usage

baseline.peakDetection(spectra, left, right, lwin, rwin, snminimum,
mono=0, multiplier=5, left.right, lwin.rwin)

Arguments

Details

Peak detection is done in several steps sorting out real peaks through different criteria. Peaks are removed from spectra and minimums and medians are used to smooth the remaining parts of the spectra. If snminimum is omitted, y3, midspec, y and y2 are not returned (faster)

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

KEVIN R. COOMBES et al.: Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization.

Examples

data(milk)
bc.peakDetection <- baseline(milk$spectra[1,, drop=FALSE], method='peakDetection',
	left=300, right=300, lwin=50, rwin=50)
## Not run: 
	plot(bc.peakDetection)

## End(Not run)

Robust Baseline Estimation

Description

Wrapper for Andreas F. Ruckstuhl, Matthew P. Jacobson, Robert W. Field, James A. Dodd's algorithm based on LOWESS and weighted regression

Usage

baseline.rfbaseline(spectra, span = 2/3, NoXP = NULL, maxit = c(2, 2),
  b = 3.5, weight = NULL, Scale = function(r) median(abs(r))/0.6745,
  delta = NULL, SORT = FALSE, DOT = FALSE, init = NULL)

Arguments

Details

Most of the code is the original code as given by the authors. The ability to sort by X-values has been removed and ability to handle multiple spectra has been added

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Andreas F. Ruckstuhl, Matthew P. Jacobson, Robert W. Field, James A. Dodd: Baseline subtraction using robust local regression estimation

Examples

data(milk)
bc.rbe <- baseline(milk$spectra[1,, drop=FALSE], method='rfbaseline',
  span=NULL, NoXP=1000)
## Not run: 
	plot(bc.rbe)

## End(Not run)

Rolling ball

Description

Ideas from Rolling Ball algorithm for X-ray spectra by M.A.Kneen and H.J. Annegarn. Variable window width has been left out

Usage

baseline.rollingBall(spectra, wm, ws)

Arguments

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

M.A. Kneen, H.J. Annegarn: Algorithm for fitting XRF, SEM and PIXE X-ray spectra backgrounds

Examples

data(milk)
bc.rollingBall <- baseline(milk$spectra[1,, drop=FALSE], wm=200, ws=200,
	method='rollingBall')
## Not run: 
	plot(bc.rollingBall)

## End(Not run)

Shirley Background Estimation

Description

Shirley Background correction for X-ray Photoelectron Spectroscopy.

Usage

baseline.shirley(spectra, t = NULL, limits = NULL, maxit = 50, err = 1e-6)

Arguments

Details

The shape of the spectrum background or baseline is affected by inelastic energy loss processes, secondary electrons and nearby peaks. A reasonable approximation is essential for a qualitative and quantitative analysis of XPS data especially if several components interfere in one spectrum. The choice of an adequate background model is determined by the physical and chemical conditions of the measurements and the significance of the background to the information to be obtained. The subtraction of the baseline before entering the fit iterations or the calculation of the peak area can be an acceptable approximation for simple analytical problems. In order to obtain chemical and physical parameters in detail, however, it is absolutely necessary to include the background function in the iterative peak fit procedure. The primary function F(E) results from the experimentally obtained function M(E) and the background function U(E) as

F(E) = M(E)-U(E)

The kinetic energy E of the spectra can be described as

E = SE + SW * (i-1)

SE means the start energy in eV, SW is the step width in eV and i the channel number. i can assume values between 1 and N with N as the number of data points.

In case of baseline calculation before initiating the fit procedure, the background is set to the averaged experimental function M(E) in a sector around the chosen start and end channels. With i{_1} as left channel (E{_1}: low energy side) and i{_2} as right channel (E{_2}: high energy side) the simulation of the baseline is obtained as

U(E_{1})=M(E_{1})

and

U(E_{2})=M(E_{2})

If ZAP is the number of points used for averaging (can be set in the preferences), the intensity of the averaged measuring function at the low energy side is calculated by

M(i_{1})=\frac{\sum_{i=0}^{ZAP-1}M(i_{1}+i)}{ZAP}

and at the high energy side by

M(i_{2})=\frac{\sum_{i=0}^{ZAP-1}M(i_{2}+i)}{ZAP}

In many cases the Shirley model turned out to be a successful approximation for the inelastic background of core level peaks of buried species, which suffered significantly from inelastic losses of the emitted photoelectrons. The calculation of the baseline is an iterative procedure. The number of iteration cycles should be chosen high enough so that the shape of the obtained background function does not change anymore. The analytical expression for the Shirley background is

U(E)= \int_{E}^{\infty}F(E')dE'+c

The algorithm of Proctor and Sherwood ([1] A. Proctor, P.M.A. Sherwood, Anal. Chem. 54 (1982) 13) is based on the assumption that for every point of the spectrum the background intensity generated by a photoelectron line is proportional to the number of all photoelectrons with higher kinetic energy. The intensity of the background U(i) in channel i is given by

U(i)=\frac{(a-b)Q(i)}{P(i)+Q(i)}+b

where a and b are the measured intensities in channel i{_1} and i{_2}, respectively, and P(i) and Q(i) represent the effective peak areas to lower and higher kinetic energies relative to the channel under consideration. An iterative procedure is necessary because P, Q, and U(i) are unknown. In first approximation U(i) = b is used.

The function baseline.shirley implements the shirley baseline. It is an iterative algorithm. The iteration stops when the deviation between two consequent iteration is lower than err or when the max number of iterations maxit is reached.

Value

The baseline function return an object of class baseline.

References

A. Proctor, P.M.A. Sherwood, Anal. Chem. 54 (1982) 13.

See Also

baseline

Examples

data("O1s")
Data <- O1s

## The same example with C1s data 
# data("C1s")
# Data <- C1s

Y <- Data[2,, drop = FALSE]
X <- Data[1,]

corrected <- baseline(Y, method = "shirley", t = X)
plot(corrected, rev.x = TRUE, labels = X)

## Not run: 
# Dependent on external software
baselineGUI(Y, labels=X, method="shirley")

## End(Not run)

Class "baselineAlg"

Description

A class that describes a baseline correction algorithm. The idea is that it contains all information needed to use an algorithm with the optimisation framework and the graphical user interface (but see Notes below).

Objects from the Class

Objects can be created by calls of the form new("baselineAlg", ...).

Slots

name:

Short-name of the algorithm. This must match the name of the object in the baselineAlgorithms list of algorithms, and is used throughout the code to identify the algorithm. It should thus start with a letter and contain only letters, digits, underscores ("_") or dots (".").

description:

Description of the algorithm, typically the full name. This will be used in the code to describe the algorith, so it should not be too long, and not contain newline characters.

funcName:

The name of the function used to estimate the baseline. The function must take an argument spectra, and return a list with the estimated baselines (baseline) original spectra (spectra) and the corrected spectra (corrected). It can also take other arguments (typically parameters) and return additional components in the list.

param:

A data frame with information about the parameters of the algorithm. It should contain the following coloumns: name - the name of the parameter; integer - TRUE if the parameter only takes integer values, otherwise FALSE; min - the lower limit of allowed values; incl.min - TRUE if the lower limit is an allowed value, otherwise FALSE; default - the default value; max - the upper limit of allowed values; incl.max - TRUE if the upper limit is an allowed value, otherwise FALSE

Methods

description

signature(object = "baselineAlg"): Extract the description slot

funcName

signature(object = "baselineAlg"): Extract the funcName slot

name

signature(object = "baselineAlg"): Extract the name slot

param

signature(object = "baselineAlg"): Extract the param slot

Note

The goal is that the optimisation framework and the GUI code should get all information about available baseline algorithms through a list of baselineAlg objects. This will make it relatively simple to add new baseline algorithms.

Currenly, there is information about the algorithms spread around in the code. We plan to move that information into the baselineAlg objects, and expand the class accordingly.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

Examples

showClass("baselineAlg")

Class "baselineAlgResult"

Description

A class describing the result of a baseline algorithm test

Objects from the Class

Objects are typically created by running runTest on a baselineAlgTest object.

Slots

param:

A named list with the parameter values that were tested. This includes both the predictor parameters and the baseline algorithm parameters. All combinations of values are tested.

qualMeas:

A matrix of quality measure values for the different combinations of parameter values. Each row corresponds to one prediction parameter value, and each coloumn to one combination of baseline parameters.

qualMeas.ind.min:

The index in qualMeas of the minimum quality measure value

minQualMeas:

The minimum quality measure value

param.ind.min:

A vector of indices into the elemets of param of the parameter values corresponding to the minimum quality measure value

param.min:

A list of the parameter values corresponding to the minimum quality measure value

qualMeasName:

The name of the quality measure

Methods

minQualMeas

signature(object = "baselineAlgResult"): Extract the minQualMeas slot

param

signature(object = "baselineAlgResult"): Extract the param slot

param.ind.min

signature(object = "baselineAlgResult"): Extract the param.ind.min slot

param.min

signature(object = "baselineAlgResult"): Extract the param.min slot

qualMeas

signature(object = "baselineAlgResult"): Extract the qualMeas slot

qualMeas.ind.min

signature(object = "baselineAlgResult"): Extract the qualMeas.ind.min slot

qualMeasName

signature(object = "baselineAlgResult"): Extract the qualMeasName slot

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Class baselineAlgTest, function runTest.

Examples

showClass("baselineAlgResult")

Class "baselineAlgTest"

Description

A class that describes a baseline algorithm test. The test is performed with the function runTest.

Objects from the Class

Objects can be created by calls of the form new("baselineAlgTest", ...).

Slots

algorithm:

A "baselineAlg" object. The baseline algorithm to test.

param:

A named list with parameter values to test. All combinations of parameters are tested.

extraArgs:

A named list of extra parameters to the baseline algorithm. These will be held fixed during the testing.

Methods

algorithm

signature(object = "baselineAlgTest"): Extract the algorithm slot

extraArgs

signature(object = "baselineAlgTest"): Extract the extraArgs slot ...

funcName

signature(object = "baselineAlgTest"): Extract the funcName slot ...

param

signature(object = "baselineAlgTest"): Extract the param slot

runTest

signature(object = "baselineAlgTest"): Run the test.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Classes baselineAlg, baselineAlgResult. Function runTest.

Examples

showClass("baselineAlgTest")

List of available baseline algorithms

Description

A list with descriptions of all baseline algorithms available through the optimisaiont framework and graphical user interface. The elements of the list are baselineAlg objects. The list is used by the code to extract names and information about the baseline algorithms.

Details

The list is not meant for usage by end-users, but is extendable and customizable, allowing for extra algorithms or removal of algoritms.

The names of the list must match the name slot of the elements.

Examples

## Get a list of all algorithms:
names(baselineAlgorithms)
## Show the descriptions
sapply(baselineAlgorithms, description)
## Add new algorithm
baseline.my.alg <- function(spectra, kappa=1, gamma=1){
   baseline  <- spectra-kappa+gamma
   corrected <- spectra-baseline
   list(baseline=baseline,corrected=corrected)
}

baselineAlgorithms$my.alg = new("baselineAlg",
     name = "my.alg",
     description = "A new baseline correction algorithm",
     funcName = "baseline.my.alg",
     param = data.frame(
        name = c("kappa","gamma"), # maxit
        integer = c(FALSE, FALSE),
        min = c(0, 0),
        incl.min = c(TRUE, TRUE),
        default = c(1, 1),
        max = c(Inf, 1),
        incl.max = c(FALSE, TRUE)
    ))

List of available baseline algorithms for GUI function

Description

A list with data.frames containing parameters, minimum and maximum values for GUIs, step lengths for sliders, default values and currently selected values, plus a short description of each parameter. The list is used by the GUIs, and is user customizable.

Details

The list is not meant for usage by end-users, but is extendable and customizable, allowing for extra algorithms, removal of algoritms or changing of parameter sets.

Examples

## Get a list of all algorithms:
names(baselineAlgorithmsGUI)
## Add new algorithm:
baselineAlgorithmsGUI$my.alg <- as.data.frame(matrix(c(0,20,1,1, 0,20,1,1), 2,4, byrow=TRUE))
dimnames(baselineAlgorithmsGUI$my.alg) <- list(par=c("kappa", "gamma"),
	val=c("min","max","step","default"))
baselineAlgorithmsGUI$my.alg$current <- c(1,1)
baselineAlgorithmsGUI$my.alg$name <- c("Subtractive constand", "Additive constant")

Baseline environment

Description

Methods to access the baseline environment.

Usage

baselineEnv()
getBaselineEnv(x, mode="any")
putBaselineEnv(x, value)

Arguments

Value

getBaseline retrieves an object.

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

See Also

The functions implementing the baseline algorithms: baseline.als, baseline.fillPeaks, baseline.irls, baseline.lowpass, baseline.medianWindow, baseline.modpolyfit, baseline.peakDetection, baseline.rfbaseline, baseline.rollingBall

Examples

putBaselineEnv('fish', '<==x-<')
getBaselineEnv('fish')

Interactive plotting tool

Description

An interactive plotting tool for dynamic visualization of baselines and their effect using the gWidgets2 package with GTK+ or Tcl/Tk.

Usage

baselineGUI(spectra, method='irls', labels, rev.x = FALSE)

Arguments

Details

Creates and updates a list containing current baseline and spectrum (baseline.result). Make sure a gWidget2 implementation is available, e.g gWidgets2RGtk2 or gWidgets2tcltk and a corresponding backend like GTK+ or Tcl/Tk. The GUI was developed using GTK which is an external dependency in Windows ans OS X.

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

Examples

data(milk)
## Not run: 
# Dependent on external software
baselineGUI(milk$spectra)

## End(Not run)

Customized baseline correction

Description

This function rescales spectrum abscissa by use of breaks and gaps before baseline correction. The effect is that the chosen baseline correction algorithm and paramters will have varying effects along the spectra, effectively giving local control of the amount of rigidity/flexibility of the estimated baseline.

Usage

custom.baseline(spectra, breaks, gaps, trans.win = NULL, just.plot = FALSE, method, ...)

Arguments

Details

This is an implementation of the customized baseline correction suggested by Liland et al. 2011 for local changes in baseline flexibility.

Value

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Kristian Hovde Liland et al.: Customized baseline correction

Examples

data(milk)
spectrum1  <- milk$spectra[1,1:10000,drop=FALSE]
ordinary   <- baseline(spectrum1, method="als", lambda=6, p=0.01)
customized <- custom.baseline(spectrum1, 2900, c(1,20), trans.win=100, 
	just.plot=FALSE, method="als", lambda=6, p=0.01)
## Not run: 
plot(1:10000,spectrum1, type='l')
lines(1:10000,getBaseline(ordinary), lty=2, col=2, lwd=2)
lines(1:10000,customized$baseline, lty=3, col=3, lwd=2)

## End(Not run)

Optimise several baseline algorithms on a data set

Description

Tests several baseline algorithms with one predictor for a given data set. The baseline algorithms are represented as a list of baselineAlgTest objects, and the predictor as a predictionTest object.

Usage

doOptim(baselineTests, X, y, predictionTest, postproc = NULL,
        tmpfile = "tmp.baseline", verbose = FALSE, cleanTmp = FALSE)

Arguments

Details

The function loops through the baseline algorithm tests in baselineTests, testing each of them with the given data and prediction test, and collects the results. The results of each baseline algorithm test is saved in a temporary file so that if the optimisation is interrupted, it can be re-run and will use the pre-calculated results. If cleanTmp is TRUE, the temporary files are deleted when the whole optimisation has finished.

Value

A list with components

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

baselineAlgTest,predictionTest

Extract the "funcName" slot.

Description

Extract the funcName slot from an object of class baselineAlg or baselineAlgTest

Usage

funcName(object)

Arguments

Value

The funcName slot of the object.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

baselineAlg, baselineAlgTest

Functions to extract the components of a "baseline" object

Description

The functions extract the baseline, spectra, corrected or call slot of a baseline object; usually the result of a call to baseline.

Usage

getBaseline(object)
getSpectra(object)
getCorrected(object)
getCall(object)

Arguments

Value

getCall returns the baseline call used to create the object. The other functions return a matrix with the original spectra, estimated baselines or corrected spectra.

Warning

In a future versoion, one of the slots might be removed from the class definition and calculated on the fly instead, in order to save space. Therefore, do use the extractor functions (getSpectra, getBaseline and getCorrected) instead of accessing the slots directly.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

The function baseline, the class baseline

Examples

data(milk)
bl <- baseline(milk$spectra[1:2,])
baseline  <- getBaseline(bl)
spectra   <- getSpectra(bl)
corrected <- getCorrected(bl)
call      <- getCall(bl)

Extraction methods specific for "predictionResult" objects

Description

Extract information from objects of class predictionResult.

Usage

ind.min(object)
paramName(object)

Arguments

Value

The corresponding slot of the object.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

predictionResult

MALDI-TOF mass spectra

Description

Matrix of 45 spectra of 21451 m/z values from MALDI-TOF on mixed milk samples.

Usage

data(milk)

Format

A data frame with 45 observations on the following 2 variables.

cow

a numeric vector

spectra

a matrix with 21451 columns

Details

cow is the concentration of cow milk in mixed samples of cow, goat, and ewe milk.

References

Kristian Hovde Liland, Bjørn-Helge Mevik, Elling-Olav Rukke, Trygve Almøy, Morten Skaugen and Tomas Isaksson (2009) Quantitative whole spectrum analysis with MALDI-TOF MS, Part I: Measurement optimisation. Chemometrics and Intelligent Laboratory Systems, 96(2), 210–218.

Examples

data(milk)
## Not run: 
plot(milk$spectra[1,], type = "l")

## End(Not run)

Extraction methods for "baselineAlg" objects

Description

Extraction methods specifically for objects of class baselineAlg

Usage

name(object)
description(object)

Arguments

Value

The methods return the corresponding slot of the object.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

baselineAlg, funcName.

Visual tool for setting up optimization

Description

Set up optimization through a graphical user interface. Optionally collecting values directly from 'baselineGUI'. Retrieve optimisation parameters and results with getOptim and getOptimRes, respectively.

Usage

optimWizard(X, y, postproc, predictionTest, cvsegments)
getOptim()
getOptimRes()

Arguments

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

Examples

## Not run: 
# Computationally intensive
data(milk)
X <- milk$spectra[,-1]
y <- milk$spectra[,1]
optimWizard(X,y)

# Retrieve optimisation
myResults <- getOptimRes()

# After optimisation is complete
plotOptim(myResults)

## End(Not run)

Extract the minimum from a baseline optimisation

Description

Takes the result of an optimisation (a call to doOptim) and extracts the minimum quality measure value along with the parameters giving rise to the value.

Usage

overall.min(results)

Arguments

Value

A list with components

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

doOptim

Extract the "param" slot

Description

Extracts the param slot of the object.

Usage

param(object)

Arguments

Value

The param slot of the object. Usually a data frame, list or numeric.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Classes baselineAlg, baselineAlgTest, baselineAlgResult, predictionResult

Extraction methods for "baselineAlgResult" objects

Description

Extraction methods that are specific for objects of class baselineAlgResult

Usage

param.ind.min(object)
qualMeas.ind.min(object)

Arguments

Value

The corresponding slot

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Class baselineAlgResult

Plot method for "baseline" objects

Description

Plot the original spectrum, the estimated baseline, and the corrected spectrum. Optionally zoom and pan plot, either with arguments or interactively.

Usage

## S4 method for signature 'baseline'
plot(x, y, specNo = 1, grid = FALSE, labels = 1:n, rev.x = FALSE,
     zoom = NULL, ...)
plotBaseline(x, y, specNo = 1, grid = FALSE, labels = 1:n, rev.x = FALSE,
             zoom = list(xz = 1, yz = 1, xc = 0, yc = 0), ...)

Arguments

Details

The normal way to plot baseline objects is to use the plot method. The plotBaseline function is the underlying work-horse function, and is not meant for interactive use.

Note

Because the argument list of any plot method must start with x, y, and the plot method for the baseline class does not use the y argument, all arguments except x must be named explicitly. Positional matching will not work.

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

See Also

baseline, baseline, baselineGUI

Examples

data(milk)
bl <- baseline(milk$spectra[1,, drop=FALSE])
## Not run: 
  # Computationally intensive
  plot(bl)
  plot(bl, zoom = TRUE)
## End(Not run)

Plotting tool for result objects from optimization

Description

A graphical user interface for plotting optimisation results, either one algorithm at the time or comparing algorithms.

Usage

plotOptim(results)

Arguments

Details

plotOptim creates a user interface based on the supplied results. Curve and level plots from single algorithms or comparison of algorithms is avilable.

For single algorithms subsets, levels corresponding to local or global minima, and averages can be extracted for plotting. For comparison of algorithms levels corresponding to local or global minima can be used, or levels corresponding to the minimum when averaging over selected values of the regression parameter, e.g. selected components in PLSR.

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

Class "predictionResult"

Description

A class containing the result of running a predictionTest.

Objects from the Class

The normal way to create objects is by calling the method runTest for any object of subclass of predictionTest.

Slots

param:

Numeric vector. The regression parameter values tested.

qualMeas:

Numeric vector. The quality measure values for each of the values of the param slot

ind.min:

The index (into qualMeas) of the minimum quality measure value

minQualMeas:

The minimum quality measure value

param.min:

The value of the parameter value corresponding to the minimum quality measure value

qualMeasName:

The name of the quality measure

paramName:

The name of the regression parameter

Methods

ind.min

signature(object = "predictionResult"): Extract the ind.min slot

minQualMeas

signature(object = "predictionResult"): Extract the minQualMeas slot

param

signature(object = "predictionResult"): Extract the param slot

param.min

signature(object = "predictionResult"): Extract the param.min slot

paramName

signature(object = "predictionResult"): Extract the paramName slot

qualMeas

signature(object = "predictionResult"): Extract the qualMeas slot

qualMeasName

signature(object = "predictionResult"): Extract the qualMeasName slot

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Function runTest, class predictionTest, subclasses PLSRTest and ridgeRegressionTest

Examples

showClass("predictionResult")

Class "predictionTest"

Description

A virtual class for all predictor test subclasses. Currently subclasses PLSRTest and ridgeRegressionTest are defined.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "predictionTest" in the signature.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Subclasses PLSRTest and ridgeRegressionTest.

Extraction functions for "predictionResult" or "baselineAlgResult" objects

Description

Extract slots from objects of class predictionResult or baselineAlgResult.

Usage

qualMeas(object, ...)
## S4 method for signature 'predictionResult'
qualMeas(object, ...)
## S4 method for signature 'baselineAlgResult'
qualMeas(object, ..., MIN, AVG,
  DEFAULT = c("all", "cond.min", "overall.min", "avg"))
minQualMeas(object)
param.min(object)
qualMeasName(object)

Arguments

Details

The arguments to the baselineAlgResult method are interpreted in the following way:

Subsets of parameters levels can be selected by supplying their names and specifying the level indices as vectors. Substituting a vector with "all" will return all levels of the corresponding parameter, and substituting it with "overall" will return the level corresponding to the overall minimum. Minimum and average values for selected parameters can be chosen using MIN and AVG, respectively, together with a vector of parameter names.

DEFAULT specifies the action for each remaining parameters: If "all" (default): returns all levels. If "cond.min": take minimum for each remaining parameter (MIN is not used). If "overall.min": set any remaining parameters to their value corresponding to the overall min. If "avg": take average for each remaining parameter (AVG is not used).

Value

The qualMeas method for baselineAlgResult objects returns the subsets or minimum values of the qualMeas slot of the object as specified above. All other methods simply return the corresponding slot.

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

Function runTest, classes baselineAlgResult and predictionResult

Class "ridgeRegressionTest"

Description

A class describing a ridge regression test.

Objects from the Class

Objects can be created by calls of the form new("ridgeRegressionTest", ...).

Slots

lambda:

Numeric vector. The smoothing parameter values to test

Extends

Class predictionTest, directly.

Methods

runTest

signature(object = "ridgeRegressionTest"): Run the test

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

The base class predictionTest. The runTest function.

Examples

showClass("ridgeRegressionTest")

Run a predictionTest or baselineAlgTest

Description

Runs the test defined in a predictionTest or baselineAlgTest object

Usage

runTest(object, X, y, ...)
## S4 method for signature 'PLSRTest'
runTest(object, X, y)
## S4 method for signature 'ridgeRegressionTest'
runTest(object, X, y)
## S4 method for signature 'baselineAlgTest'
runTest(object, X, y, predictionTest, postproc, verbose = FALSE)

Arguments

Value

runTest returns an object of class predictionResult or baselineAlgResult.

Methods

signature(object = "baselineAlgTest")

Baseline corrects the spectra, optionally postprocesses them, and runs a prediction test on the corrected spectra.

signature(object = "PLSRTest")

Runs PLSR on the data and calculates the cross-validated RMSEP

signature(object = "ridgeRegressionTest")

Runs ridge regression on the data and calculates the GCV

Author(s)

Bjørn-Helge Mevik and Kristian Hovde Liland

See Also

baselineAlgTest, predictionTest, PLSRTest, ridgeRegressionTest