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R/optimisation.R
In baseline: Baseline Correction of Spectra
Defines functions
overall.min
doOptim
Documented in
doOptim
overall.min
### optimisation.R: Classes, methods and functions for searching for
### optimal baselines.
### $Id: optimisation.R 179 2011-01-09 14:29:07Z bhm $
## The function mvrValstats has been copied from package pls (overlapping authors)
## because eval(parent.frame()) seems to break call to pls::msep.
###
### Common generic functions and default methods
###
## A function for performing the testing.
## It will have methods for the predictionTest subclasses, and the
## baselineAlgTest class.
#' @name runTest
#' @aliases runTest
#' @title Run a predictionTest or baselineAlgTest
#' @description
#' Runs the test defined in a \code{\linkS4class{predictionTest}} or \code{\linkS4class{baselineAlgTest}} object
#' @param object An object of class \code{\linkS4class{baselineAlgTest}}
#' or subclass of \code{\linkS4class{predictionTest}} (currently
#' \code{\linkS4class{PLSRTest}} or
#' \code{\linkS4class{ridgeRegressionTest}}). The object specify the test to be run.
#' @param X A \code{matrix}. The spectra to use in the test.
#' @param y A \code{vector} or \code{matrix}. The response(s) to use in the test
#' @param predictionTest A \code{\linkS4class{predictionTest}} object,
#' describing the prediction test to use for this baseline algorithm test.
#' @param postproc A \code{function}, used to postprocess the baseline corrected
#' spectra prior to prediction testing. The function should take a
#' matrix of spectra as its only argument, and return a matrix of
#' postprocessed spectra.
#' @param verbose \code{Logical}, specifying whether the test should print out progress information. Default is \code{FALSE}.
#' @param ... Other arguments. Currently only used by the \code{\linkS4class{baselineAlgTest}} method.
#' @section Methods:
#' \code{signature(object = "baselineAlgTest")}: Baseline corrects the spectra, optionally postprocesses them, and runs a prediction test on the corrected spectra.
#' \code{signature(object = "PLSRTest")}: Runs PLSR on the data and calculates the cross-validated RMSEP
#' \code{signature(object = "ridgeRegressionTest")}: Runs ridge regression on the data and calculates the GCV
#' @return
#' \code{runTest} returns an object of class.
#' \code{\linkS4class{predictionResult}} or \code{\linkS4class{baselineAlgResult}}.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlgTest}}, \code{\linkS4class{predictionTest}}, \code{\linkS4class{PLSRTest}}, \code{\linkS4class{ridgeRegressionTest}}
#' @keywords spectra baseline methods
#' @export runTest
setGeneric("runTest", function(object, X, y, ...) standardGeneric("runTest"))
## A function for extracting the quality measures from objects.
## It will have methods for predictionResult and baselineAlgResult.
#' @name qualMeas
#' @aliases qualMeas minQualMeas param.min qualMeasName
#' @title Extraction functions for "predictionResult" or "baselineAlgResult" objects.
#' @description Extract slots from objects of class
#' \code{\linkS4class{predictionResult}} or
#' \code{\linkS4class{baselineAlgResult}}.
#' @param object An object of class \code{\linkS4class{predictionResult}} or \code{\linkS4class{baselineAlgResult}}
#' @param MIN \code{List} or \code{vector} of parameter names to take the minimum over. Not used if \code{DEFAULT} is \code{"cond.min"}. See Details.
#' @param AVG \code{List} or \code{vector} of parameter names to take the average over. Not used if \code{DEFAULT} is \code{"avg"}. See Details.
#' @param DEFAULT Character string. The default way to calculate the minimum (or average) for all parameters. See Details.
#' @param ... Other arguments. Selection of subsets of parameter levels. See Details.
#' @details
#' The arguments to the \code{\linkS4class{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 \code{"all"} will return all levels of the corresponding parameter, and
#' substituting it with \code{"overall"} will return the level corresponding
#' to the overall minimum.
#' Minimum and average values for selected parameters can be chosen using
#' \code{MIN} and \code{AVG}, respectively, together with a vector of parameter names.
#'
#' \code{DEFAULT} specifies the action for each remaining parameters:
#' If \code{"all"} (default): returns all levels. If \code{"cond.min"}:
#' take minimum for each remaining parameter (MIN is not used). If
#' \code{"overall.min"}: set any remaining parameters to their value
#' corresponding to the overall min. If \code{"avg"}: take average for
#' each remaining parameter (AVG is not used).
#' @return
#' The \code{qualMeas} method for \code{\linkS4class{baselineAlgResult}} objects returns
#' the subsets or minimum values of the \code{qualMeas} slot of the object as specified
#' above. All other methods simply return the corresponding slot.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Function \code{\link{runTest}}, classes \code{\linkS4class{baselineAlgResult}} and \code{\linkS4class{predictionResult}}.
#' @keywords methods baseline spectra
#' @export qualMeas
NULL
#' @rdname qualMeas
#' @export
setGeneric("qualMeas", function(object, ...) standardGeneric("qualMeas"))
## A function to extract parameter settings
## It will have methods for predictionResult, baselineAlgTest,
## baselineAlgResult and baselineAlg.
#' @name param
#' @aliases param
#' @title Extract the "param" slot
#' @description Extracts the \code{param} slot of the object.
#' @param object An object of class \code{\linkS4class{baselineAlg}},
#' \code{\linkS4class{baselineAlgTest}},
#' \code{\linkS4class{baselineAlgResult}} or
#' \code{\linkS4class{predictionResult}}.
#' @return The \code{param} slot of the object. Usually a data frame, list or numeric.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Classes \code{\linkS4class{baselineAlg}}, \code{\linkS4class{baselineAlgTest}}, \code{\linkS4class{baselineAlgResult}}, \code{\linkS4class{predictionResult}}
#' @keywords baseline spectra methods
#' @export param
setGeneric("param", function(object) standardGeneric("param"))
###
### Classes and methods for prediction testing
###
## A virtual class for all prediction tests. It will have subclasses for each
## type of predictor (PLSR, RR, ...).
#' @name predictionTest
#' @aliases predictionTestClass
#' @title Class "predictionTest"
#' @description
#' A virtual class for all predictor test subclasses. Currently
#' subclasses \code{\linkS4class{PLSRTest}} and
#' \code{\linkS4class{ridgeRegressionTest}} are defined.
#' @section Objects from the Class:
#' A virtual Class: No objects may be created from it.
#' @section Methods:
#' No methods defined with class "predictionTest" in the signature.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland.
#' @seealso Subclasses \code{\linkS4class{PLSRTest}} and \code{\linkS4class{ridgeRegressionTest}}.
#' @keywords classes
#' @exportClass predictionTest
setClass("predictionTest")
#' @name predictionResult
#' @aliases predictionResult
#' @title Class "predictionResult"
#' @description
#' A class containing the result of running a
#' \code{\linkS4class{predictionTest}}.
#' @section Objects from the Class:
#' The normal way to create objects is by calling the method
#' \code{runTest} for any object of subclass of
#' \code{\linkS4class{predictionTest}}.
#' @section Slots:
#' \code{param}: Numeric vector. The regression parameter values tested.
#' \code{qualMeas}: Numeric vector. The quality measure values for each of the values of the \code{param} slot
#' \code{ind.min}: The index (into \code{qualMeas}) of the minimum quality measure value.
#' \code{minQualMeas}: The minimum quality measure value.
#' \code{param.min}: The value of the parameter value corresponding to the minimum quality measure value.
#' \code{qualMeasName}: The name of the quality measure.
#' \code{paramName}: The name of the regression parameter.
#' @section Methods:
#' ind.min: \code{signature(object = "predictionResult")}: Extract the \code{ind.min} slot
#' minQualMeas: \code{signature(object = "predictionResult")}: Extract the \code{minQualMeas} slot
#' param: \code{signature(object = "predictionResult")}: Extract the \code{param} slot
#' param.min: \code{signature(object = "predictionResult")}: Extract the \code{param.min} slot
#' paramName: \code{signature(object = "predictionResult")}: Extract the \code{paramName} slot
#' qualMeas: \code{signature(object = "predictionResult")}: Extract the \code{qualMeas} slot
#' qualMeasName: \code{signature(object = "predictionResult")}: Extract the \code{qualMeasName} slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Function \code{\link{runTest}}, class \code{\linkS4class{predictionTest}}, subclasses \code{\linkS4class{PLSRTest}} and \code{\linkS4class{ridgeRegressionTest}}.
#' @examples
#' showClass("predictionResult")
#' @keywords classes
#' @exportClass predictionResult
setClass("predictionResult",
representation(param = "numeric",
qualMeas = "numeric",
ind.min = "numeric", ##?
minQualMeas = "numeric", ##?
param.min = "numeric", ##?
qualMeasName = "character",
paramName = "character"
))
setMethod("param", "predictionResult", function(object) object@param)
# #' @rdname predictionResult
# #' @exportMethod qualMeas predictionResult
setMethod("qualMeas", "predictionResult", function(object, ...) object@qualMeas)
#' @name predictionResultMethods
#' @title Extraction methods specific for "predictionResult" objects
#'
#' @description Extract information from objects of class
#' \code{\linkS4class{predictionResult}}.
#'
#' @aliases ind.min paramName
#' @param object Object of class \code{\linkS4class{predictionResult}}
#' @return The corresponding slot of the object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{predictionResult}}
#' @keywords methods
#' @export
setGeneric("ind.min", function(object) standardGeneric("ind.min"))
setMethod("ind.min", "predictionResult", function(object) object@ind.min)
#' @rdname predictionResult
#' @export
setGeneric("minQualMeas", function(object) standardGeneric("minQualMeas"))
setMethod("minQualMeas", "predictionResult", function(object) object@minQualMeas)
#' @rdname predictionResult
#' @export
setGeneric("param.min", function(object) standardGeneric("param.min"))
setMethod("param.min", "predictionResult", function(object) object@param.min)
#' @rdname predictionResult
#' @export
setGeneric("qualMeasName", function(object) standardGeneric("qualMeasName"))
setMethod("qualMeasName", "predictionResult", function(object) object@qualMeasName)
#' @rdname predictionResultMethods
#' @export
setGeneric("paramName", function(object) standardGeneric("paramName"))
setMethod("paramName", "predictionResult", function(object) object@paramName)
##
## PLSR
##
## The class
#' @name PLSRTestClass
#' @aliases PLSRTestClass
#' @title Class "PLSRTest"
#' @description
#' A class describing a PLSR prediction test. To run the test, the "pls" package must be installed.
#' @section Objects from the Class:
#' Objects can be created by calls of the form \code{new("PLSRTest", ...)}.
#' @section Slots:
#' \code{ncomp}: Integer vector. The number of PLSR components to test.
#' \code{cvsegments}: A list of the segments to use in the cross-validation.
#' @section Extends:
#' Class \code{\linkS4class{predictionTest}}, directly.
#' @section Methods:
#' \code{signature(object = "PLSRTest")}: Run the test
#' @author Bjørn-Helge Mevik and Krisitan Hovde Liland.
#' @seealso The base class \code{\linkS4class{predictionTest}}. The \code{\link{runTest}} function. The \code{\link[pls:mvr]{plsr}} function from the "pls" package.
#' @examples
#' showClass("PLSRTest")
#' @keywords classes
#' @exportClass PLSRTest
setClass("PLSRTest", contains = "predictionTest",
representation(ncomp = "numeric", cvsegments = "list"))
## The prediction test method
# #' @rdname runTestMethods
# #' @exportMethod runTest PLSRTest
setMethod("runTest", "PLSRTest",
function(object, X, y) {
if(requireNamespace("pls", quietly = TRUE)){
ncomp <- object@ncomp
cvsegments <- object@cvsegments
## FIXME: Perhaps use mvrCv directly
res <- pls::plsr(y ~ X, ncomp = ncomp, validation = "CV",
segments = cvsegments)
msep <- drop(pls::MSEP(res, estimate = "adjCV")$val)
if (NCOL(y) == 1) {
rmsep <- sqrt(msep)
} else {
## For multiple responses, use the square root of the
## mean (over the responses) relative (to 0 components)
## MSEP:
rmsep <- sqrt(colMeans(msep / msep[,1]))
}
ind.min <- which.min(rmsep)
return(new("predictionResult",
param = 0:ncomp,
qualMeas = rmsep, ind.min = ind.min, minQualMeas = min(rmsep),
param.min = ind.min - 1,
paramName = "ncomp", qualMeasName = "RMSEP"))
} else {
warning('Package pls not installed')
return(list())
}
})
##
## Ridge regression
##
## The class
#' @name ridgeRegressionTestClass
#' @aliases ridgeRegressionTest-class
#' @title Class "ridgeRegressionTest"
#' @description A class describing a ridge regression test.
#' @section Objects from the Class:
#' Objects can be created by calls of the form \code{new("ridgeRegressionTest", ...)}.
#' @section Slots:
#' \code{lambda}: Numeric vector. The smoothing parameter values to test
#' @section Extends:
#' Class \code{\linkS4class{predictionTest}}, directly.
#' @section Methods:
#' runTest \code{signature(object = "ridgeRegressionTest")}: Run the test
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso The base class \code{\linkS4class{predictionTest}}. The \code{\link{runTest}} function.
#' @examples
#' showClass("ridgeRegressionTest")
#' @keywords classes
#' @exportClass ridgeRegressionTest
setClass("ridgeRegressionTest", contains = "predictionTest",
representation(lambda = "numeric"))
setMethod("runTest", "ridgeRegressionTest",
function(object, X, y) {
if(requireNamespace("MASS", quietly = TRUE)){
lambda <- object@lambda
res <- MASS::lm.ridge(y ~ X, lambda = lambda)
ind.min <- which.min(res$GCV)
return(new("predictionResult",
param = lambda,
qualMeas = res$GCV, ind.min = ind.min, minQualMeas = min(res$GCV),
param.min = res$lambda[ind.min],
paramName = "lambda", qualMeasName = "GCV"))
} else {
warning('Package MASS not installed')
return(list())
}
})
###
### Classes and methods for describing baseline algorithms
### FIXME: Put in separate file!
#' @name baselineAlg
#' @title 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).
#'
#'
#' @docType class
#' @note The goal is that the optimisation framework and the GUI code should
#' get all information about available baseline algorithms through a list of
#' \code{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 \code{baselineAlg} objects,
#' and expand the class accordingly.
#' @section Objects from the Class: Objects can be created by calls of the form
#' \code{new("baselineAlg", ...)}.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @keywords classes
#' @examples
#'
#' showClass("baselineAlg")
#' @exportClass baselineAlg
setClass("baselineAlg",
representation(name = "character",
description = "character",
funcName = "character",
param = "data.frame"
),
prototype(param = data.frame(
name = NA, integer = NA, min = NA, incl.min = NA,
default = NA, max = NA, incl.max = NA)[0,]
),
validity = function(object) {
if (!identical(names(object@param),
c("name","integer","min","incl.min","default","max","incl.max")))
return("The param slot does not have the correct coloumn names")
return(TRUE)
}
)
#' @name funcName
#' @title Extract the "funcName" slot.
#'
#' @description Extract the \code{funcName} slot from an object of class
#' \code{\linkS4class{baselineAlg}} or \code{\linkS4class{baselineAlgTest}}
#'
#'
#' @aliases funcName funcName-methods funcName,baselineAlg-method
#' funcName,baselineAlgTest-method
#' @param object An object of class \code{\linkS4class{baselineAlg}} or
#' \code{\linkS4class{baselineAlgTest}}
#' @return The \code{funcName} slot of the object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlg}},
#' \code{\linkS4class{baselineAlgTest}}
#' @keywords spectra baseline
#' @export
setGeneric("funcName", function(object) standardGeneric("funcName"))
setMethod("funcName", "baselineAlg", function(object) object@funcName)
#' @name baselineAlg
#' @title Extraction methods for "baselineAlg" objects
#'
#' @description Extraction methods specifically for objects of class
#' \code{\linkS4class{baselineAlg}}
#'
#' @aliases name name-methods name,baselineAlg-method description
#' description-methods description,baselineAlg-method
#' @param object Object of class \code{\linkS4class{baselineAlg}}
#' @return The methods return the corresponding slot of the object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlg}}, \code{\link{funcName}}.
#' @keywords baseline spectra
#' @export
setGeneric("name", function(object) standardGeneric("name"))
setMethod("name", "baselineAlg", function(object) object@name)
#' @rdname rdbaselineAlg
#' @export
setGeneric("description", function(object) standardGeneric("description"))
setMethod("description", "baselineAlg", function(object) object@description)
#' @rdname baselineAlg
#' @export
setMethod("param", "baselineAlg", function(object) object@param)
## A list with objects for each implemented algorithm:
#' List of available baseline algorithms
#'
#' A list with descriptions of all baseline algorithms available through the
#' optimisaiont framework and graphical user interface. The elements of the
#' list are \code{\linkS4class{baselineAlg}} objects. The list is used by the
#' code to extract names and information about the baseline algorithms.
#'
#' 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 \code{name} slot of the elements.
#'
#' @name baselineAlgorithms
#' @docType data
#' @keywords baseline
#' @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)
#' ))
#' @export
baselineAlgorithms <- list(
als = new("baselineAlg",
name = "als",
description = "Asymmetric Least Squares",
funcName = "baseline.als",
param = data.frame( # FIXME
name = c("lambda","p"), # maxit
integer = c(FALSE, FALSE),
min = c(0, 0),
incl.min = c(TRUE, TRUE),
default = c(6, 0.05),
max = c(Inf, 1),
incl.max = c(FALSE, TRUE)
)),
fillPeaks = new("baselineAlg",
name = "fillPeaks",
description = "Iterateive baseline correction algorithm based on mean suppression",
funcName = "baseline.fillPeaks",
param = data.frame( # FIXME
name = c("lambda","hwi","it", "int"),
integer = c(FALSE, TRUE, TRUE, TRUE),
min = c(0, 1, 1, 3),
incl.min = c(TRUE, TRUE, TRUE, TRUE),
default = c(NA, NA, NA, NA),
max = c(Inf, Inf, Inf, Inf),
incl.max = c(FALSE, FALSE, FALSE, FALSE)
)),
irls = new("baselineAlg",
name = "irls",
description = "Iterative Restricted Least Squares",
funcName = "baseline.irls",
param = data.frame( # FIXME
name = c("lambda1", "lambda2", "maxit", "wi"),
integer = c(FALSE, FALSE, TRUE, TRUE),
min = c(0, 0, 0, 0),
incl.min = c(TRUE, TRUE, TRUE, TRUE),
default = c(5, 9, 200, 0.05),
max = c(Inf, Inf, Inf, 1),
incl.max = c(FALSE, FALSE, FALSE, TRUE)
)),
## FIXME: Not tested, because baseline.lowpass gives strange results!
lowpass = new("baselineAlg",
name = "lowpass",
description = "Low-pass filter based on fast Fourier transform",
funcName = "baseline.lowpass",
param = data.frame( # FIXME
name = c("steep","half"),
integer = c(FALSE, TRUE),
min = c(0, 1),
incl.min = c(TRUE, TRUE),
default = c(2, 5),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
medianWindow = new("baselineAlg",
name = "medianWindow",
description = "Local medians",
funcName = "baseline.medianWindow",
param = data.frame( # FIXME
name = c("hwm","hws"), # end
integer = c(TRUE, TRUE),
min = c(0,1),
incl.min = c(TRUE, TRUE),
default = c(NA, NA),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
modpolyfit = new("baselineAlg",
name = "modpolyfit",
description = "Modified iterative polynomial fitting",
funcName = "baseline.modpolyfit",
param = data.frame( # FIXME
name = c("degree", "tol", "rep"), # t
integer = c(TRUE, FALSE, TRUE),
min = c(1, 0, 0),
incl.min = c(TRUE, TRUE, TRUE),
default = c(4, 1e-3, 100),
max = c(Inf, Inf, Inf),
incl.max = c(FALSE, FALSE, FALSE)
)),
## FIXME: Transformed parameters??
peakDetection = new("baselineAlg",
name = "peakDetection",
description = "Peak detection",
funcName = "baseline.peakDetection",
param = data.frame( # FIXME
name = c("left","right","lwin", "rwin", "snminimum", "mono", "multiplier"),
integer = c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE),
min = c(1,1,1,1,0,0,0),
incl.min = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE),
default = c(NA),
max = c(Inf,Inf,Inf,Inf,Inf,1,Inf),
incl.max = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)
)),
## FIXME: Perhaps easier to specify span than NoXP??
rfbaseline = new("baselineAlg",
name = "rfbaseline",
description = "Robust Baseline Estimation by Ruckstuhl etal",
funcName = "baseline.rfbaseline",
param = data.frame( # FIXME
name = c("NoXP","b"), # Lots of arguments!
integer = c(TRUE, FALSE),
min = c(3, 0),
incl.min = c(TRUE, FALSE),
default = c(NA, 3.5),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
rollingBall = new("baselineAlg",
name = "rollingBall",
description = "Rolling Ball",
funcName = "baseline.rollingBall",
param = data.frame( # FIXME
name = c("wm","ws"),
integer = c(TRUE, TRUE),
min = c(2, 2),
incl.min = c(TRUE, TRUE),
default = c(NA, NA),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
shirley = new("baselineAlg",
name = "shirley",
description = "A shirley baseline correction algorithm",
funcName = "baseline.shirley",
param = data.frame(
name = c("maxit", "err"), # maxit
integer = c(FALSE, FALSE),
min = c(1, 1e-8),
incl.min = c(TRUE, TRUE),
default = c(50, 1e-6),
max = c(Inf, 1e-3),
incl.max = c(FALSE, FALSE)
)),
TAP = new("baselineAlg",
name = "TAP",
description = "Kintetic analysis of derivative data",
funcName = "baseline.TAP",
param = data.frame(
name = c("interval", "tol"),
integer = c(TRUE, FALSE),
min = c(1, 1e-12),
incl.min = c(TRUE, TRUE),
default = c(15, 1e-3),
max = c(Inf, 1),
incl.max = c(FALSE, FALSE)
))
) ## End of baselineAlgorithms <- list(
###
### Classes and methods for baseline algorithm tests
###
#' Class "baselineAlgTest"
#'
#' A class that describes a baseline algorithm test. The test is performed
#' with the function \code{runTest}.
#'
#'
#' @name baselineAlgTest
#' @docType class
#' @section Objects from the Class: Objects can be created by calls of the form
#' \code{new("baselineAlgTest", ...)}.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Classes \code{\linkS4class{baselineAlg}},
#' \code{\linkS4class{baselineAlgResult}}. Function \code{runTest}.
#' @keywords classes
#' @examples
#'
#' showClass("baselineAlgTest")
#' @exportClass baselineAlgTest
setClass("baselineAlgTest",
representation(algorithm = "baselineAlg",
param = "list",
extraArgs = "list"),
prototype(extraArgs = list()))
#' Class "baselineAlgResult"
#'
#' A class describing the result of a baseline algorithm test
#'
#'
#' @name baselineAlgResult
#' @docType class
#' @section Objects from the Class: Objects are typically created by running
#' \code{runTest} on a \code{\linkS4class{baselineAlgTest}} object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Class \code{\linkS4class{baselineAlgTest}}, function
#' \code{runTest}.
#' @keywords classes
#' @examples
#'
#' showClass("baselineAlgResult")
#' @exportClass baselineAlgResult
setClass("baselineAlgResult",
representation(param = "list",
qualMeas = "matrix", ## or multidimensional array?
qualMeas.ind.min = "numeric",
minQualMeas = "numeric", ##?
param.ind.min = "numeric",
param.min = "list",
qualMeasName = "character"
))
## Accessor functions:
#' @name algorithm
#' @aliases algorithm extraArgs
#' @title Extraction methods for "baselineAlgTest" objects
#' @description
#' Extraction methods specifically for objects of class \code{\linkS4class{baselineAlgTest}}
#' @param object Object of class \code{\linkS4class{baselineAlgTest}}
#' @return The corresponding slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland.
#' @seealso \code{\linkS4class{baselineAlgTest}}
#' @keywords baseline spectra
#' @export
setGeneric("algorithm", function(object) standardGeneric("algorithm"))
setMethod("algorithm", "baselineAlgTest", function(object) object@algorithm)
## One class for all algorithms
#' @name baselineAlgTestMethods
#' @title Extraction methods for "baselineAlgTest" objects
#'
#' @description Extraction methods specifically for objects of class
#' \code{\linkS4class{baselineAlgTest}}
#'
#'
#' @aliases algorithm algorithm-methods algorithm,baselineAlgTest-method
#' extraArgs extraArgs-methods extraArgs,baselineAlgTest-method
#' @param object Object of class \code{\linkS4class{baselineAlgTest}}
#' @return The corresponding slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlgTest}}
#' @keywords baseline spectra
#' @export
setGeneric("extraArgs", function(object) standardGeneric("extraArgs"))
setMethod("extraArgs", "baselineAlgTest", function(object) object@extraArgs)
#' @name baselineAlgResultMethods
#' @title Extraction methods for "baselineAlgResult" objects
#'
#' @description Extraction methods that are specific for objects of class
#' \code{\linkS4class{baselineAlgResult}}
#'
#' @aliases param.ind.min qualMeas.ind.min
#' @param object Object of class \code{\linkS4class{baselineAlgResult}}
#' @return The corresponding slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Class \code{\linkS4class{baselineAlgResult}}
#' @keywords baseline spectra
#' @export
setGeneric("qualMeas.ind.min", function(object) standardGeneric("qualMeas.ind.min"))
setMethod("qualMeas.ind.min", "baselineAlgResult", function(object) object@qualMeas.ind.min)
#' @rdname baselineAlgResultMethods
#' @export
setGeneric("param.ind.min", function(object) standardGeneric("param.ind.min"))
setMethod("param.ind.min", "baselineAlgResult", function(object) object@param.ind.min)
setMethod("param", "baselineAlgTest", function(object) object@param)
setMethod("funcName", "baselineAlgTest",
function(object) funcName(object@algorithm))
setMethod("param", "baselineAlgResult", function(object) object@param)
setMethod("minQualMeas", "baselineAlgResult", function(object) object@minQualMeas)
setMethod("param.min", "baselineAlgResult", function(object) object@param.min)
setMethod("qualMeasName", "baselineAlgResult", function(object) object@qualMeasName)
## A method for doing the algorithm testing
# #' @rdname baselineAlgTest
# #' @export
setMethod("runTest", "baselineAlgTest",
function(object, X, y, predictionTest, postproc, verbose = FALSE) {
## Build a data frame with all parameter combinations:
params <- expand.grid(object@param, KEEP.OUT.ATTRS = FALSE)
nparams <- nrow(params)
## Variables to accumulate results:
qualMeas <- list()
param.min <- minQualMeas <- regpar.ind.min <- numeric(nparams)
if (verbose) cat("Looping through the", nparams,
"baseline parameter settings\n")
for (i in seq_len(nparams)) {
if (verbose) cat(i, ": correcting... ", sep = "")
## Do the baseline correction
bl <- do.call("baseline",
c(list(spectra = X, method = name(object@algorithm)),
params[i,, drop=FALSE], object@extraArgs))
Xcorr <- getCorrected(bl)
## Perform any post processing:
if (!missing(postproc) && !is.null(postproc)) {
if (verbose) cat("postprocessing... ")
Xcorr <- postproc(Xcorr)
}
## Test the predictor on the baseline corrected data
if (verbose) cat("prediction testing...")
res <- runTest(predictionTest, Xcorr, y)
qualMeas[[i]] <- qualMeas(res)
regpar.ind.min[i] <- ind.min(res)
minQualMeas[i] <- minQualMeas(res)
param.min[i] <- param.min(res)
if (verbose) cat("\n")
}
qualMeas <- do.call("cbind", qualMeas)
## Find and return the best results
best.i <- which.min(minQualMeas)
## Join the regression parameter and the baseline parameters:
param <- c(list(param(res)), object@param)
names(param)[1] <- paramName(res)
param.ind.min <-
c(regpar.ind.min[best.i],
unlist(expand.grid(lapply(object@param, seq_along))[best.i,, drop=FALSE]))
names(param.ind.min)[1] <- paramName(res)
param.min <- c(param.min[best.i], as.list(params[best.i,, drop=FALSE]))
names(param.min)[1] <- paramName(res)
return(new("baselineAlgResult",
param = param,
qualMeas = qualMeas,
qualMeas.ind.min = c(regpar.ind.min[best.i], best.i),
minQualMeas = minQualMeas[best.i],
param.ind.min = param.ind.min,
param.min = param.min,
qualMeasName = qualMeasName(res)
))
})
###
### Method for extracting test result values from a baseline algorithm
### test
###
# #' @rdname baselineAlgResultMethods
# #' @export
setMethod("qualMeas", "baselineAlgResult",
function(object, ..., MIN, AVG, DEFAULT = c("all", "cond.min", "overall.min", "avg")) {
DEFAULT <- match.arg(DEFAULT)
## Extract needed information from object:
res <- object@qualMeas
params <- object@param
param.ind.min <- object@param.ind.min
## Collect the named parameters:
setparams <- list(...)
## 0) Substitute for any setparams == "overall"
atoverall <- names(setparams)[setparams == "overall"]
if (length(atoverall) > 0) {
setparams[atoverall] <- param.ind.min[atoverall]
}
## 0b) Substitute for any setparams == "all"
useall <- names(setparams)[setparams == "all"]
if (length(useall) > 0) {
setparams[useall] <- lapply(params[useall], seq_along)
}
## Figure out which parameters to take (conditional) min over:
MINns <- AVGns <- character()
if (DEFAULT == "cond.min") {
## Take min over all remaining parameters
if(missing(AVG)) {
MINns <- setdiff(names(params), names(setparams))
} else {
MINns <- setdiff(names(params), c(names(setparams), AVG))
}
## Figure out which parameters to take average over:
} else {
if (DEFAULT == "avg") {
## Take average over all remaining parameters
if(missing(MIN)) {
AVGns <- setdiff(names(params), names(setparams))
} else {
AVGns <- setdiff(names(params), c(names(setparams), MIN)) }
}
}
## Figure out which parameters to take (conditional) min over:
if (!missing(MIN) && length(MINns)==0) {
## Take min over parameters specified in MIN
MIN <- substitute(MIN)
wasList <- is.call(MIN)
MINns <- sapply(MIN, as.character) # Convert to char, if
# needed; also make it a vector
## If MIN was a list, the first element will be "list":
if (wasList) MINns <- MINns[-1]
## Sanity check:
if (!all(MINns %in% names(params)))
stop("Non-existing parameter(s) specified in 'MIN'.")
}
## Figure out which parameters to take average over:
if (!missing(AVG) && length(AVGns)==0) {
## Take average over parameters specified in AVG
AVG <- substitute(AVG)
wasList <- is.call(AVG)
AVGns <- sapply(AVG, as.character) # Convert to char, if
# needed; also make it a vector
## If AVG was a list, the first element will be "list":
if (wasList) AVGns <- AVGns[-1]
## Sanity check:
if (!all(AVGns %in% names(params)))
stop("Non-existing parameter(s) specified in 'AVG'.")
}
## If DEFAULT is "overall.min", set any remaining parameters to their
## value corresponding to the overall min:
if (DEFAULT == "overall.min") {
nms <- setdiff(names(params), c(names(setparams), MINns, AVGns))
for (nm in nms) {
setparams[[nm]] <- param.ind.min[nm]
}
}
## 1) Convert the results into an array
newdim <- sapply(params, length)
dim(res) <- newdim
dimnames(res) <- params
## 2) Reorder the results into the order given in params
## Internal function for applying without dropping dimensions
kapply <- function(x, MARGIN, FUN, lab){
dims <- dim(x)
command <- "x["
if(MARGIN==1){
command <- "x[1"
dimnames(x)[[1]][1] <- lab
} else {
command <- "x["
}
for(i in 2:length(dims)){
if(MARGIN==i){
command <- paste(command,", 1", sep="")
dimnames(x)[[i]][1] <- lab
} else {
command <- paste(command,", ", sep="")
}
}
command <- paste(command, ", drop=FALSE]", sep="")
y <- eval(parse(text=command))
y[] <- apply(x, setdiff(1:length(dims),MARGIN), FUN)
y
}
## Internal function for subsetting without collapsing
ksubset <- function(x, setparams){
dn <- names(dimnames(x))
sn <- names(setparams)
dims <- dim(x)
command <- "x["
if(sn[1]%in%dn){
command <- paste("x[setparams$", sn[1], sep="")
} else {
command <- "x["
}
for(i in 2:length(dims)){
if(sn[i]%in%dn){
command <- paste(command, ", setparams$", sn[i], sep="")
} else {
command <- paste(command,", ", sep="")
}
}
command <- paste(command, ", drop=FALSE]", sep="")
eval(parse(text=command))
}
## 3) Extract the desired sub-array:
if(length(setparams)>0)
res <- ksubset(res, setparams)
res.names <- names(dimnames(res))
## 4) Take the min over any parameters in MINns
if( length(MINns) > 0){
for( i in 1:length(MINns)){
res <- kapply(res, which(res.names==MINns[i]), min, 'min')
}
}
## 5) Take the mean over any parameters in AVGns
if( length(AVGns) > 0){
for( i in 1:length(AVGns)){
res <- kapply(res, which(res.names==AVGns[i]), mean, 'avg')
}
}
return(res)
})
###
### Function for testing several baseline algorithms
###
#' @name doOptim
#' @title 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
#' \code{\linkS4class{baselineAlgTest}} objects, and the predictor as a
#' \code{\linkS4class{predictionTest}} object.
#'
#' @details The function loops through the baseline algorithm tests in
#' \code{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
#' \code{cleanTmp} is \code{TRUE}, the temporary files are deleted when the
#' whole optimisation has finished.
#'
#' @aliases doOptim mvrValstats
#' @param baselineTests a list of \code{\linkS4class{baselineAlgTest}} objects.
#' The baseline algorithms and parameter values to test
#' @param X A matrix. The spectra to use in the test
#' @param y A vector or matrix. The response(s) to use in the test
#' @param predictionTest A \code{\linkS4class{predictionTest}} object. The
#' predictor and parameter values to use in the test
#' @param postproc A function, used to postprocess the baseline corrected
#' spectra prior to prediction testing. The function should take a matrix of
#' spectra as its only argument, and return a matrix of postprocessed spectra
#' @param tmpfile The basename of the files used to store intermediate
#' calculations for checkpointing. Defaults to \code{"tmp.baseline"}
#' @param verbose Logical, specifying whether the test should print out
#' progress information. Default is \code{FALSE}
#' @param cleanTmp Logical, specifying whether the intermediate files should be
#' deleted when the optimisation has finished. Default is \code{FALSE}
#' @return A list with components \item{baselineTests}{The \code{baselineTests}
#' argument} \item{results}{A list with the \code{baselineAlgResult} objects
#' for each baseline test} \item{minQualMeas}{The minimum quality measure
#' value} \item{baselineAlg.min}{The name of the baseline algorithm giving the
#' minimum quality measure value} \item{param.min}{A list with the parameter
#' values corresponding to the minimum quality measure value}
#' @examples
#' data(milk)
#' X <- milk$spectra[,-1]
#' y <- milk$spectra[,1]
#' opt <- doOptim(
#' list(ALS = new("baselineAlgTest",
#' algorithm = baselineAlgorithms$als,
#' param=list(lambda=6:7,p=0.5))),
#' X, y,
#' new("PLSRTest",
#' ncomp = 10,
#' cvsegments = cvsegments(N=45, k=5)))
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso
#' \code{\linkS4class{baselineAlgTest}},\code{\linkS4class{predictionTest}}
#' @keywords baseline spectra
#' @export
doOptim <- function(baselineTests, X, y, predictionTest,
postproc = NULL, tmpfile = "tmp.baseline",
verbose = FALSE, cleanTmp = FALSE) {
nalgs <- length(baselineTests)
## Variables to collect return values:
results <- param.min <- list()
savefiles <- character(nalgs)
minQualMeas <- numeric(nalgs)
if (verbose) cat("Looping through the", nalgs, "baseline algorithm tests\n")
for (i in seq_len(nalgs)) {
fname <- funcName(baselineTests[[i]])
blname <- names(baselineTests)[i]
if (is.null(blname) || !nzchar(blname))
blname <- name(algorithm(baselineTests[[i]]))
if (verbose) cat(i, ": ", blname, ":\n", sep = "")
savefiles[i] <- paste(tmpfile, blname, "RData", sep = ".")
if (file.exists(savefiles[i])) {
load(savefiles[i])
if (verbose) cat(" Loaded pre-calculated results from file", savefiles[i], "\n")
} else {
res <- runTest(baselineTests[[i]], X, y, predictionTest, postproc, verbose)
save(res, file = savefiles[i])
}
results[[i]] <- res
minQualMeas[i] <- minQualMeas(res)
param.min[[i]] <- param.min(res)
}
## Optionally, delete temporary files:
if (isTRUE(cleanTmp)) unlink(savefiles)
## Find and return the best results
best.i <- which.min(minQualMeas)
minQualMeas <- minQualMeas[best.i]
param.min <- param.min[[best.i]]
names(results) <- names(baselineTests)
return(list(baselineTests = baselineTests,
results = results,
minQualMeas = minQualMeas,
baselineAlg.min = name(algorithm(baselineTests[[best.i]])),
param.min = param.min))
}
## Function to extract minimum from optimisation:
#' @name overall.min
#' @title Extract the minimum from a baseline optimisation
#'
#' @description Takes the result of an optimisation (a call to \code{\link{doOptim}}) and
#' extracts the minimum quality measure value along with the parameters giving
#' rise to the value.
#'
#'
#' @param results Result of call to \code{\link{doOptim}}
#' @return A list with components \item{qualMeas}{The minimum quality measure
#' value} \item{algorithm}{The name of the baseline algorithm corresponding to
#' the minimum} \item{param}{A list with the parameter values corresponding to
#' the minimum quality measure value}
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\link{doOptim}}
#' @keywords baseline spectra
#' @export
overall.min <- function(results) {
with(results, list(qualMeas = minQualMeas, algorithm = baselineAlg.min,
param = param.min))
}
## The function mvrValstats has been copied (2013-08-10) from package pls (overlapping authors)
## because eval(parent.frame()) seems to break call to pls::msep.
mvrValstats <- function (object, estimate, newdata, ncomp = 1:object$ncomp,
comps, intercept = cumulative, se = FALSE, ...)
{
cumulative <- missing(comps) || is.null(comps)
if (any(estimate == "CV")) {
if (!cumulative)
stop("Cross-validation is not supported when `comps' is specified")
if (is.null(object$validation))
stop("`object' has no `validation' component")
}
nestimates <- length(estimate)
nresp <- dim(fitted(object))[2]
respnames <- dimnames(fitted(object))[[2]]
SSE <- array(dim = c(nestimates, nresp, if (cumulative) 1 +
length(ncomp) else 2), dimnames = list(estimate = estimate,
response = respnames, model = if (cumulative) {
c("(Intercept)", paste(ncomp, "comps"))
} else {
c("(Intercept)", paste("(Intercept), Comp", paste(comps,
collapse = ", ")))
}))
SST <- array(dim = c(nestimates, nresp), dimnames = list(estimate = estimate,
response = respnames))
nobj <- numeric(nestimates)
names(nobj) <- estimate
for (i in seq(along = estimate)) {
switch(estimate[i], train = {
resp <- as.matrix(model.response(model.frame(object)))
nobj[i] <- nrow(resp)
if (inherits(object$na.action, "exclude")) {
resp <- napredict(object$na.action, resp)
}
res <- if (cumulative) residuals(object, ...)[, ,
ncomp, drop = FALSE] else resp - predict(object,
comps = comps, ...)
SST[i, ] <- apply(resp, 2, var, na.rm = TRUE) * (nobj[i] -
1)
SSE[i, , ] <- cbind(SST[i, ], colSums(res^2, na.rm = TRUE))
}, test = {
if (missing(newdata)) stop("Missing `newdata'.")
newdata <- model.frame(formula(object), data = newdata)
resp <- as.matrix(model.response(newdata))
pred <- if (cumulative) predict(object, ncomp = ncomp,
newdata = newdata, ...) else predict(object,
comps = comps, newdata = newdata, ...)
nobj[i] <- nrow(newdata)
SST[i, ] <- apply(resp, 2, var) * (nobj[i] - 1)
SSE[i, , ] <- cbind(colSums(sweep(resp, 2, object$Ymeans)^2),
colSums((pred - c(resp))^2))
}, CV = {
resp <- as.matrix(model.response(model.frame(object)))
nobj[i] <- nrow(resp)
SST[i, ] <- apply(resp, 2, var) * (nobj[i] - 1)
SSE[i, , ] <- cbind(object$validation$PRESS0, object$validation$PRESS[,
ncomp, drop = FALSE])
})
}
if (cumulative)
comps <- ncomp
if (intercept)
comps <- c(0, comps)
else SSE <- SSE[, , -1, drop = FALSE]
return(list(SSE = SSE, SST = SST, nobj = nobj, comps = comps,
cumulative = cumulative))
}
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Defines functions overall.min doOptim
Documented in doOptim overall.min
### optimisation.R: Classes, methods and functions for searching for
### optimal baselines.
### $Id: optimisation.R 179 2011-01-09 14:29:07Z bhm $
## The function mvrValstats has been copied from package pls (overlapping authors)
## because eval(parent.frame()) seems to break call to pls::msep.
###
### Common generic functions and default methods
###
## A function for performing the testing.
## It will have methods for the predictionTest subclasses, and the
## baselineAlgTest class.
#' @name runTest
#' @aliases runTest
#' @title Run a predictionTest or baselineAlgTest
#' @description
#' Runs the test defined in a \code{\linkS4class{predictionTest}} or \code{\linkS4class{baselineAlgTest}} object
#' @param object An object of class \code{\linkS4class{baselineAlgTest}}
#' or subclass of \code{\linkS4class{predictionTest}} (currently
#' \code{\linkS4class{PLSRTest}} or
#' \code{\linkS4class{ridgeRegressionTest}}). The object specify the test to be run.
#' @param X A \code{matrix}. The spectra to use in the test.
#' @param y A \code{vector} or \code{matrix}. The response(s) to use in the test
#' @param predictionTest A \code{\linkS4class{predictionTest}} object,
#' describing the prediction test to use for this baseline algorithm test.
#' @param postproc A \code{function}, used to postprocess the baseline corrected
#' spectra prior to prediction testing. The function should take a
#' matrix of spectra as its only argument, and return a matrix of
#' postprocessed spectra.
#' @param verbose \code{Logical}, specifying whether the test should print out progress information. Default is \code{FALSE}.
#' @param ... Other arguments. Currently only used by the \code{\linkS4class{baselineAlgTest}} method.
#' @section Methods:
#' \code{signature(object = "baselineAlgTest")}: Baseline corrects the spectra, optionally postprocesses them, and runs a prediction test on the corrected spectra.
#' \code{signature(object = "PLSRTest")}: Runs PLSR on the data and calculates the cross-validated RMSEP
#' \code{signature(object = "ridgeRegressionTest")}: Runs ridge regression on the data and calculates the GCV
#' @return
#' \code{runTest} returns an object of class.
#' \code{\linkS4class{predictionResult}} or \code{\linkS4class{baselineAlgResult}}.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlgTest}}, \code{\linkS4class{predictionTest}}, \code{\linkS4class{PLSRTest}}, \code{\linkS4class{ridgeRegressionTest}}
#' @keywords spectra baseline methods
#' @export runTest
setGeneric("runTest", function(object, X, y, ...) standardGeneric("runTest"))
## A function for extracting the quality measures from objects.
## It will have methods for predictionResult and baselineAlgResult.
#' @name qualMeas
#' @aliases qualMeas minQualMeas param.min qualMeasName
#' @title Extraction functions for "predictionResult" or "baselineAlgResult" objects.
#' @description Extract slots from objects of class
#' \code{\linkS4class{predictionResult}} or
#' \code{\linkS4class{baselineAlgResult}}.
#' @param object An object of class \code{\linkS4class{predictionResult}} or \code{\linkS4class{baselineAlgResult}}
#' @param MIN \code{List} or \code{vector} of parameter names to take the minimum over. Not used if \code{DEFAULT} is \code{"cond.min"}. See Details.
#' @param AVG \code{List} or \code{vector} of parameter names to take the average over. Not used if \code{DEFAULT} is \code{"avg"}. See Details.
#' @param DEFAULT Character string. The default way to calculate the minimum (or average) for all parameters. See Details.
#' @param ... Other arguments. Selection of subsets of parameter levels. See Details.
#' @details
#' The arguments to the \code{\linkS4class{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 \code{"all"} will return all levels of the corresponding parameter, and
#' substituting it with \code{"overall"} will return the level corresponding
#' to the overall minimum.
#' Minimum and average values for selected parameters can be chosen using
#' \code{MIN} and \code{AVG}, respectively, together with a vector of parameter names.
#'
#' \code{DEFAULT} specifies the action for each remaining parameters:
#' If \code{"all"} (default): returns all levels. If \code{"cond.min"}:
#' take minimum for each remaining parameter (MIN is not used). If
#' \code{"overall.min"}: set any remaining parameters to their value
#' corresponding to the overall min. If \code{"avg"}: take average for
#' each remaining parameter (AVG is not used).
#' @return
#' The \code{qualMeas} method for \code{\linkS4class{baselineAlgResult}} objects returns
#' the subsets or minimum values of the \code{qualMeas} slot of the object as specified
#' above. All other methods simply return the corresponding slot.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Function \code{\link{runTest}}, classes \code{\linkS4class{baselineAlgResult}} and \code{\linkS4class{predictionResult}}.
#' @keywords methods baseline spectra
#' @export qualMeas
NULL
#' @rdname qualMeas
#' @export
setGeneric("qualMeas", function(object, ...) standardGeneric("qualMeas"))
## A function to extract parameter settings
## It will have methods for predictionResult, baselineAlgTest,
## baselineAlgResult and baselineAlg.
#' @name param
#' @aliases param
#' @title Extract the "param" slot
#' @description Extracts the \code{param} slot of the object.
#' @param object An object of class \code{\linkS4class{baselineAlg}},
#' \code{\linkS4class{baselineAlgTest}},
#' \code{\linkS4class{baselineAlgResult}} or
#' \code{\linkS4class{predictionResult}}.
#' @return The \code{param} slot of the object. Usually a data frame, list or numeric.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Classes \code{\linkS4class{baselineAlg}}, \code{\linkS4class{baselineAlgTest}}, \code{\linkS4class{baselineAlgResult}}, \code{\linkS4class{predictionResult}}
#' @keywords baseline spectra methods
#' @export param
setGeneric("param", function(object) standardGeneric("param"))
###
### Classes and methods for prediction testing
###
## A virtual class for all prediction tests. It will have subclasses for each
## type of predictor (PLSR, RR, ...).
#' @name predictionTest
#' @aliases predictionTestClass
#' @title Class "predictionTest"
#' @description
#' A virtual class for all predictor test subclasses. Currently
#' subclasses \code{\linkS4class{PLSRTest}} and
#' \code{\linkS4class{ridgeRegressionTest}} are defined.
#' @section Objects from the Class:
#' A virtual Class: No objects may be created from it.
#' @section Methods:
#' No methods defined with class "predictionTest" in the signature.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland.
#' @seealso Subclasses \code{\linkS4class{PLSRTest}} and \code{\linkS4class{ridgeRegressionTest}}.
#' @keywords classes
#' @exportClass predictionTest
setClass("predictionTest")
#' @name predictionResult
#' @aliases predictionResult
#' @title Class "predictionResult"
#' @description
#' A class containing the result of running a
#' \code{\linkS4class{predictionTest}}.
#' @section Objects from the Class:
#' The normal way to create objects is by calling the method
#' \code{runTest} for any object of subclass of
#' \code{\linkS4class{predictionTest}}.
#' @section Slots:
#' \code{param}: Numeric vector. The regression parameter values tested.
#' \code{qualMeas}: Numeric vector. The quality measure values for each of the values of the \code{param} slot
#' \code{ind.min}: The index (into \code{qualMeas}) of the minimum quality measure value.
#' \code{minQualMeas}: The minimum quality measure value.
#' \code{param.min}: The value of the parameter value corresponding to the minimum quality measure value.
#' \code{qualMeasName}: The name of the quality measure.
#' \code{paramName}: The name of the regression parameter.
#' @section Methods:
#' ind.min: \code{signature(object = "predictionResult")}: Extract the \code{ind.min} slot
#' minQualMeas: \code{signature(object = "predictionResult")}: Extract the \code{minQualMeas} slot
#' param: \code{signature(object = "predictionResult")}: Extract the \code{param} slot
#' param.min: \code{signature(object = "predictionResult")}: Extract the \code{param.min} slot
#' paramName: \code{signature(object = "predictionResult")}: Extract the \code{paramName} slot
#' qualMeas: \code{signature(object = "predictionResult")}: Extract the \code{qualMeas} slot
#' qualMeasName: \code{signature(object = "predictionResult")}: Extract the \code{qualMeasName} slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Function \code{\link{runTest}}, class \code{\linkS4class{predictionTest}}, subclasses \code{\linkS4class{PLSRTest}} and \code{\linkS4class{ridgeRegressionTest}}.
#' @examples
#' showClass("predictionResult")
#' @keywords classes
#' @exportClass predictionResult
setClass("predictionResult",
representation(param = "numeric",
qualMeas = "numeric",
ind.min = "numeric", ##?
minQualMeas = "numeric", ##?
param.min = "numeric", ##?
qualMeasName = "character",
paramName = "character"
))
setMethod("param", "predictionResult", function(object) object@param)
# #' @rdname predictionResult
# #' @exportMethod qualMeas predictionResult
setMethod("qualMeas", "predictionResult", function(object, ...) object@qualMeas)
#' @name predictionResultMethods
#' @title Extraction methods specific for "predictionResult" objects
#'
#' @description Extract information from objects of class
#' \code{\linkS4class{predictionResult}}.
#'
#' @aliases ind.min paramName
#' @param object Object of class \code{\linkS4class{predictionResult}}
#' @return The corresponding slot of the object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{predictionResult}}
#' @keywords methods
#' @export
setGeneric("ind.min", function(object) standardGeneric("ind.min"))
setMethod("ind.min", "predictionResult", function(object) object@ind.min)
#' @rdname predictionResult
#' @export
setGeneric("minQualMeas", function(object) standardGeneric("minQualMeas"))
setMethod("minQualMeas", "predictionResult", function(object) object@minQualMeas)
#' @rdname predictionResult
#' @export
setGeneric("param.min", function(object) standardGeneric("param.min"))
setMethod("param.min", "predictionResult", function(object) object@param.min)
#' @rdname predictionResult
#' @export
setGeneric("qualMeasName", function(object) standardGeneric("qualMeasName"))
setMethod("qualMeasName", "predictionResult", function(object) object@qualMeasName)
#' @rdname predictionResultMethods
#' @export
setGeneric("paramName", function(object) standardGeneric("paramName"))
setMethod("paramName", "predictionResult", function(object) object@paramName)
##
## PLSR
##
## The class
#' @name PLSRTestClass
#' @aliases PLSRTestClass
#' @title Class "PLSRTest"
#' @description
#' A class describing a PLSR prediction test. To run the test, the "pls" package must be installed.
#' @section Objects from the Class:
#' Objects can be created by calls of the form \code{new("PLSRTest", ...)}.
#' @section Slots:
#' \code{ncomp}: Integer vector. The number of PLSR components to test.
#' \code{cvsegments}: A list of the segments to use in the cross-validation.
#' @section Extends:
#' Class \code{\linkS4class{predictionTest}}, directly.
#' @section Methods:
#' \code{signature(object = "PLSRTest")}: Run the test
#' @author Bjørn-Helge Mevik and Krisitan Hovde Liland.
#' @seealso The base class \code{\linkS4class{predictionTest}}. The \code{\link{runTest}} function. The \code{\link[pls:mvr]{plsr}} function from the "pls" package.
#' @examples
#' showClass("PLSRTest")
#' @keywords classes
#' @exportClass PLSRTest
setClass("PLSRTest", contains = "predictionTest",
representation(ncomp = "numeric", cvsegments = "list"))
## The prediction test method
# #' @rdname runTestMethods
# #' @exportMethod runTest PLSRTest
setMethod("runTest", "PLSRTest",
function(object, X, y) {
if(requireNamespace("pls", quietly = TRUE)){
ncomp <- object@ncomp
cvsegments <- object@cvsegments
## FIXME: Perhaps use mvrCv directly
res <- pls::plsr(y ~ X, ncomp = ncomp, validation = "CV",
segments = cvsegments)
msep <- drop(pls::MSEP(res, estimate = "adjCV")$val)
if (NCOL(y) == 1) {
rmsep <- sqrt(msep)
} else {
## For multiple responses, use the square root of the
## mean (over the responses) relative (to 0 components)
## MSEP:
rmsep <- sqrt(colMeans(msep / msep[,1]))
}
ind.min <- which.min(rmsep)
return(new("predictionResult",
param = 0:ncomp,
qualMeas = rmsep, ind.min = ind.min, minQualMeas = min(rmsep),
param.min = ind.min - 1,
paramName = "ncomp", qualMeasName = "RMSEP"))
} else {
warning('Package pls not installed')
return(list())
}
})
##
## Ridge regression
##
## The class
#' @name ridgeRegressionTestClass
#' @aliases ridgeRegressionTest-class
#' @title Class "ridgeRegressionTest"
#' @description A class describing a ridge regression test.
#' @section Objects from the Class:
#' Objects can be created by calls of the form \code{new("ridgeRegressionTest", ...)}.
#' @section Slots:
#' \code{lambda}: Numeric vector. The smoothing parameter values to test
#' @section Extends:
#' Class \code{\linkS4class{predictionTest}}, directly.
#' @section Methods:
#' runTest \code{signature(object = "ridgeRegressionTest")}: Run the test
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso The base class \code{\linkS4class{predictionTest}}. The \code{\link{runTest}} function.
#' @examples
#' showClass("ridgeRegressionTest")
#' @keywords classes
#' @exportClass ridgeRegressionTest
setClass("ridgeRegressionTest", contains = "predictionTest",
representation(lambda = "numeric"))
setMethod("runTest", "ridgeRegressionTest",
function(object, X, y) {
if(requireNamespace("MASS", quietly = TRUE)){
lambda <- object@lambda
res <- MASS::lm.ridge(y ~ X, lambda = lambda)
ind.min <- which.min(res$GCV)
return(new("predictionResult",
param = lambda,
qualMeas = res$GCV, ind.min = ind.min, minQualMeas = min(res$GCV),
param.min = res$lambda[ind.min],
paramName = "lambda", qualMeasName = "GCV"))
} else {
warning('Package MASS not installed')
return(list())
}
})
###
### Classes and methods for describing baseline algorithms
### FIXME: Put in separate file!
#' @name baselineAlg
#' @title 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).
#'
#'
#' @docType class
#' @note The goal is that the optimisation framework and the GUI code should
#' get all information about available baseline algorithms through a list of
#' \code{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 \code{baselineAlg} objects,
#' and expand the class accordingly.
#' @section Objects from the Class: Objects can be created by calls of the form
#' \code{new("baselineAlg", ...)}.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @keywords classes
#' @examples
#'
#' showClass("baselineAlg")
#' @exportClass baselineAlg
setClass("baselineAlg",
representation(name = "character",
description = "character",
funcName = "character",
param = "data.frame"
),
prototype(param = data.frame(
name = NA, integer = NA, min = NA, incl.min = NA,
default = NA, max = NA, incl.max = NA)[0,]
),
validity = function(object) {
if (!identical(names(object@param),
c("name","integer","min","incl.min","default","max","incl.max")))
return("The param slot does not have the correct coloumn names")
return(TRUE)
}
)
#' @name funcName
#' @title Extract the "funcName" slot.
#'
#' @description Extract the \code{funcName} slot from an object of class
#' \code{\linkS4class{baselineAlg}} or \code{\linkS4class{baselineAlgTest}}
#'
#'
#' @aliases funcName funcName-methods funcName,baselineAlg-method
#' funcName,baselineAlgTest-method
#' @param object An object of class \code{\linkS4class{baselineAlg}} or
#' \code{\linkS4class{baselineAlgTest}}
#' @return The \code{funcName} slot of the object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlg}},
#' \code{\linkS4class{baselineAlgTest}}
#' @keywords spectra baseline
#' @export
setGeneric("funcName", function(object) standardGeneric("funcName"))
setMethod("funcName", "baselineAlg", function(object) object@funcName)
#' @name baselineAlg
#' @title Extraction methods for "baselineAlg" objects
#'
#' @description Extraction methods specifically for objects of class
#' \code{\linkS4class{baselineAlg}}
#'
#' @aliases name name-methods name,baselineAlg-method description
#' description-methods description,baselineAlg-method
#' @param object Object of class \code{\linkS4class{baselineAlg}}
#' @return The methods return the corresponding slot of the object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlg}}, \code{\link{funcName}}.
#' @keywords baseline spectra
#' @export
setGeneric("name", function(object) standardGeneric("name"))
setMethod("name", "baselineAlg", function(object) object@name)
#' @rdname rdbaselineAlg
#' @export
setGeneric("description", function(object) standardGeneric("description"))
setMethod("description", "baselineAlg", function(object) object@description)
#' @rdname baselineAlg
#' @export
setMethod("param", "baselineAlg", function(object) object@param)
## A list with objects for each implemented algorithm:
#' List of available baseline algorithms
#'
#' A list with descriptions of all baseline algorithms available through the
#' optimisaiont framework and graphical user interface. The elements of the
#' list are \code{\linkS4class{baselineAlg}} objects. The list is used by the
#' code to extract names and information about the baseline algorithms.
#'
#' 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 \code{name} slot of the elements.
#'
#' @name baselineAlgorithms
#' @docType data
#' @keywords baseline
#' @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)
#' ))
#' @export
baselineAlgorithms <- list(
als = new("baselineAlg",
name = "als",
description = "Asymmetric Least Squares",
funcName = "baseline.als",
param = data.frame( # FIXME
name = c("lambda","p"), # maxit
integer = c(FALSE, FALSE),
min = c(0, 0),
incl.min = c(TRUE, TRUE),
default = c(6, 0.05),
max = c(Inf, 1),
incl.max = c(FALSE, TRUE)
)),
fillPeaks = new("baselineAlg",
name = "fillPeaks",
description = "Iterateive baseline correction algorithm based on mean suppression",
funcName = "baseline.fillPeaks",
param = data.frame( # FIXME
name = c("lambda","hwi","it", "int"),
integer = c(FALSE, TRUE, TRUE, TRUE),
min = c(0, 1, 1, 3),
incl.min = c(TRUE, TRUE, TRUE, TRUE),
default = c(NA, NA, NA, NA),
max = c(Inf, Inf, Inf, Inf),
incl.max = c(FALSE, FALSE, FALSE, FALSE)
)),
irls = new("baselineAlg",
name = "irls",
description = "Iterative Restricted Least Squares",
funcName = "baseline.irls",
param = data.frame( # FIXME
name = c("lambda1", "lambda2", "maxit", "wi"),
integer = c(FALSE, FALSE, TRUE, TRUE),
min = c(0, 0, 0, 0),
incl.min = c(TRUE, TRUE, TRUE, TRUE),
default = c(5, 9, 200, 0.05),
max = c(Inf, Inf, Inf, 1),
incl.max = c(FALSE, FALSE, FALSE, TRUE)
)),
## FIXME: Not tested, because baseline.lowpass gives strange results!
lowpass = new("baselineAlg",
name = "lowpass",
description = "Low-pass filter based on fast Fourier transform",
funcName = "baseline.lowpass",
param = data.frame( # FIXME
name = c("steep","half"),
integer = c(FALSE, TRUE),
min = c(0, 1),
incl.min = c(TRUE, TRUE),
default = c(2, 5),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
medianWindow = new("baselineAlg",
name = "medianWindow",
description = "Local medians",
funcName = "baseline.medianWindow",
param = data.frame( # FIXME
name = c("hwm","hws"), # end
integer = c(TRUE, TRUE),
min = c(0,1),
incl.min = c(TRUE, TRUE),
default = c(NA, NA),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
modpolyfit = new("baselineAlg",
name = "modpolyfit",
description = "Modified iterative polynomial fitting",
funcName = "baseline.modpolyfit",
param = data.frame( # FIXME
name = c("degree", "tol", "rep"), # t
integer = c(TRUE, FALSE, TRUE),
min = c(1, 0, 0),
incl.min = c(TRUE, TRUE, TRUE),
default = c(4, 1e-3, 100),
max = c(Inf, Inf, Inf),
incl.max = c(FALSE, FALSE, FALSE)
)),
## FIXME: Transformed parameters??
peakDetection = new("baselineAlg",
name = "peakDetection",
description = "Peak detection",
funcName = "baseline.peakDetection",
param = data.frame( # FIXME
name = c("left","right","lwin", "rwin", "snminimum", "mono", "multiplier"),
integer = c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE),
min = c(1,1,1,1,0,0,0),
incl.min = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE),
default = c(NA),
max = c(Inf,Inf,Inf,Inf,Inf,1,Inf),
incl.max = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)
)),
## FIXME: Perhaps easier to specify span than NoXP??
rfbaseline = new("baselineAlg",
name = "rfbaseline",
description = "Robust Baseline Estimation by Ruckstuhl etal",
funcName = "baseline.rfbaseline",
param = data.frame( # FIXME
name = c("NoXP","b"), # Lots of arguments!
integer = c(TRUE, FALSE),
min = c(3, 0),
incl.min = c(TRUE, FALSE),
default = c(NA, 3.5),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
rollingBall = new("baselineAlg",
name = "rollingBall",
description = "Rolling Ball",
funcName = "baseline.rollingBall",
param = data.frame( # FIXME
name = c("wm","ws"),
integer = c(TRUE, TRUE),
min = c(2, 2),
incl.min = c(TRUE, TRUE),
default = c(NA, NA),
max = c(Inf, Inf),
incl.max = c(FALSE, FALSE)
)),
shirley = new("baselineAlg",
name = "shirley",
description = "A shirley baseline correction algorithm",
funcName = "baseline.shirley",
param = data.frame(
name = c("maxit", "err"), # maxit
integer = c(FALSE, FALSE),
min = c(1, 1e-8),
incl.min = c(TRUE, TRUE),
default = c(50, 1e-6),
max = c(Inf, 1e-3),
incl.max = c(FALSE, FALSE)
)),
TAP = new("baselineAlg",
name = "TAP",
description = "Kintetic analysis of derivative data",
funcName = "baseline.TAP",
param = data.frame(
name = c("interval", "tol"),
integer = c(TRUE, FALSE),
min = c(1, 1e-12),
incl.min = c(TRUE, TRUE),
default = c(15, 1e-3),
max = c(Inf, 1),
incl.max = c(FALSE, FALSE)
))
) ## End of baselineAlgorithms <- list(
###
### Classes and methods for baseline algorithm tests
###
#' Class "baselineAlgTest"
#'
#' A class that describes a baseline algorithm test. The test is performed
#' with the function \code{runTest}.
#'
#'
#' @name baselineAlgTest
#' @docType class
#' @section Objects from the Class: Objects can be created by calls of the form
#' \code{new("baselineAlgTest", ...)}.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Classes \code{\linkS4class{baselineAlg}},
#' \code{\linkS4class{baselineAlgResult}}. Function \code{runTest}.
#' @keywords classes
#' @examples
#'
#' showClass("baselineAlgTest")
#' @exportClass baselineAlgTest
setClass("baselineAlgTest",
representation(algorithm = "baselineAlg",
param = "list",
extraArgs = "list"),
prototype(extraArgs = list()))
#' Class "baselineAlgResult"
#'
#' A class describing the result of a baseline algorithm test
#'
#'
#' @name baselineAlgResult
#' @docType class
#' @section Objects from the Class: Objects are typically created by running
#' \code{runTest} on a \code{\linkS4class{baselineAlgTest}} object.
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Class \code{\linkS4class{baselineAlgTest}}, function
#' \code{runTest}.
#' @keywords classes
#' @examples
#'
#' showClass("baselineAlgResult")
#' @exportClass baselineAlgResult
setClass("baselineAlgResult",
representation(param = "list",
qualMeas = "matrix", ## or multidimensional array?
qualMeas.ind.min = "numeric",
minQualMeas = "numeric", ##?
param.ind.min = "numeric",
param.min = "list",
qualMeasName = "character"
))
## Accessor functions:
#' @name algorithm
#' @aliases algorithm extraArgs
#' @title Extraction methods for "baselineAlgTest" objects
#' @description
#' Extraction methods specifically for objects of class \code{\linkS4class{baselineAlgTest}}
#' @param object Object of class \code{\linkS4class{baselineAlgTest}}
#' @return The corresponding slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland.
#' @seealso \code{\linkS4class{baselineAlgTest}}
#' @keywords baseline spectra
#' @export
setGeneric("algorithm", function(object) standardGeneric("algorithm"))
setMethod("algorithm", "baselineAlgTest", function(object) object@algorithm)
## One class for all algorithms
#' @name baselineAlgTestMethods
#' @title Extraction methods for "baselineAlgTest" objects
#'
#' @description Extraction methods specifically for objects of class
#' \code{\linkS4class{baselineAlgTest}}
#'
#'
#' @aliases algorithm algorithm-methods algorithm,baselineAlgTest-method
#' extraArgs extraArgs-methods extraArgs,baselineAlgTest-method
#' @param object Object of class \code{\linkS4class{baselineAlgTest}}
#' @return The corresponding slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\linkS4class{baselineAlgTest}}
#' @keywords baseline spectra
#' @export
setGeneric("extraArgs", function(object) standardGeneric("extraArgs"))
setMethod("extraArgs", "baselineAlgTest", function(object) object@extraArgs)
#' @name baselineAlgResultMethods
#' @title Extraction methods for "baselineAlgResult" objects
#'
#' @description Extraction methods that are specific for objects of class
#' \code{\linkS4class{baselineAlgResult}}
#'
#' @aliases param.ind.min qualMeas.ind.min
#' @param object Object of class \code{\linkS4class{baselineAlgResult}}
#' @return The corresponding slot
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso Class \code{\linkS4class{baselineAlgResult}}
#' @keywords baseline spectra
#' @export
setGeneric("qualMeas.ind.min", function(object) standardGeneric("qualMeas.ind.min"))
setMethod("qualMeas.ind.min", "baselineAlgResult", function(object) object@qualMeas.ind.min)
#' @rdname baselineAlgResultMethods
#' @export
setGeneric("param.ind.min", function(object) standardGeneric("param.ind.min"))
setMethod("param.ind.min", "baselineAlgResult", function(object) object@param.ind.min)
setMethod("param", "baselineAlgTest", function(object) object@param)
setMethod("funcName", "baselineAlgTest",
function(object) funcName(object@algorithm))
setMethod("param", "baselineAlgResult", function(object) object@param)
setMethod("minQualMeas", "baselineAlgResult", function(object) object@minQualMeas)
setMethod("param.min", "baselineAlgResult", function(object) object@param.min)
setMethod("qualMeasName", "baselineAlgResult", function(object) object@qualMeasName)
## A method for doing the algorithm testing
# #' @rdname baselineAlgTest
# #' @export
setMethod("runTest", "baselineAlgTest",
function(object, X, y, predictionTest, postproc, verbose = FALSE) {
## Build a data frame with all parameter combinations:
params <- expand.grid(object@param, KEEP.OUT.ATTRS = FALSE)
nparams <- nrow(params)
## Variables to accumulate results:
qualMeas <- list()
param.min <- minQualMeas <- regpar.ind.min <- numeric(nparams)
if (verbose) cat("Looping through the", nparams,
"baseline parameter settings\n")
for (i in seq_len(nparams)) {
if (verbose) cat(i, ": correcting... ", sep = "")
## Do the baseline correction
bl <- do.call("baseline",
c(list(spectra = X, method = name(object@algorithm)),
params[i,, drop=FALSE], object@extraArgs))
Xcorr <- getCorrected(bl)
## Perform any post processing:
if (!missing(postproc) && !is.null(postproc)) {
if (verbose) cat("postprocessing... ")
Xcorr <- postproc(Xcorr)
}
## Test the predictor on the baseline corrected data
if (verbose) cat("prediction testing...")
res <- runTest(predictionTest, Xcorr, y)
qualMeas[[i]] <- qualMeas(res)
regpar.ind.min[i] <- ind.min(res)
minQualMeas[i] <- minQualMeas(res)
param.min[i] <- param.min(res)
if (verbose) cat("\n")
}
qualMeas <- do.call("cbind", qualMeas)
## Find and return the best results
best.i <- which.min(minQualMeas)
## Join the regression parameter and the baseline parameters:
param <- c(list(param(res)), object@param)
names(param)[1] <- paramName(res)
param.ind.min <-
c(regpar.ind.min[best.i],
unlist(expand.grid(lapply(object@param, seq_along))[best.i,, drop=FALSE]))
names(param.ind.min)[1] <- paramName(res)
param.min <- c(param.min[best.i], as.list(params[best.i,, drop=FALSE]))
names(param.min)[1] <- paramName(res)
return(new("baselineAlgResult",
param = param,
qualMeas = qualMeas,
qualMeas.ind.min = c(regpar.ind.min[best.i], best.i),
minQualMeas = minQualMeas[best.i],
param.ind.min = param.ind.min,
param.min = param.min,
qualMeasName = qualMeasName(res)
))
})
###
### Method for extracting test result values from a baseline algorithm
### test
###
# #' @rdname baselineAlgResultMethods
# #' @export
setMethod("qualMeas", "baselineAlgResult",
function(object, ..., MIN, AVG, DEFAULT = c("all", "cond.min", "overall.min", "avg")) {
DEFAULT <- match.arg(DEFAULT)
## Extract needed information from object:
res <- object@qualMeas
params <- object@param
param.ind.min <- object@param.ind.min
## Collect the named parameters:
setparams <- list(...)
## 0) Substitute for any setparams == "overall"
atoverall <- names(setparams)[setparams == "overall"]
if (length(atoverall) > 0) {
setparams[atoverall] <- param.ind.min[atoverall]
}
## 0b) Substitute for any setparams == "all"
useall <- names(setparams)[setparams == "all"]
if (length(useall) > 0) {
setparams[useall] <- lapply(params[useall], seq_along)
}
## Figure out which parameters to take (conditional) min over:
MINns <- AVGns <- character()
if (DEFAULT == "cond.min") {
## Take min over all remaining parameters
if(missing(AVG)) {
MINns <- setdiff(names(params), names(setparams))
} else {
MINns <- setdiff(names(params), c(names(setparams), AVG))
}
## Figure out which parameters to take average over:
} else {
if (DEFAULT == "avg") {
## Take average over all remaining parameters
if(missing(MIN)) {
AVGns <- setdiff(names(params), names(setparams))
} else {
AVGns <- setdiff(names(params), c(names(setparams), MIN)) }
}
}
## Figure out which parameters to take (conditional) min over:
if (!missing(MIN) && length(MINns)==0) {
## Take min over parameters specified in MIN
MIN <- substitute(MIN)
wasList <- is.call(MIN)
MINns <- sapply(MIN, as.character) # Convert to char, if
# needed; also make it a vector
## If MIN was a list, the first element will be "list":
if (wasList) MINns <- MINns[-1]
## Sanity check:
if (!all(MINns %in% names(params)))
stop("Non-existing parameter(s) specified in 'MIN'.")
}
## Figure out which parameters to take average over:
if (!missing(AVG) && length(AVGns)==0) {
## Take average over parameters specified in AVG
AVG <- substitute(AVG)
wasList <- is.call(AVG)
AVGns <- sapply(AVG, as.character) # Convert to char, if
# needed; also make it a vector
## If AVG was a list, the first element will be "list":
if (wasList) AVGns <- AVGns[-1]
## Sanity check:
if (!all(AVGns %in% names(params)))
stop("Non-existing parameter(s) specified in 'AVG'.")
}
## If DEFAULT is "overall.min", set any remaining parameters to their
## value corresponding to the overall min:
if (DEFAULT == "overall.min") {
nms <- setdiff(names(params), c(names(setparams), MINns, AVGns))
for (nm in nms) {
setparams[[nm]] <- param.ind.min[nm]
}
}
## 1) Convert the results into an array
newdim <- sapply(params, length)
dim(res) <- newdim
dimnames(res) <- params
## 2) Reorder the results into the order given in params
## Internal function for applying without dropping dimensions
kapply <- function(x, MARGIN, FUN, lab){
dims <- dim(x)
command <- "x["
if(MARGIN==1){
command <- "x[1"
dimnames(x)[[1]][1] <- lab
} else {
command <- "x["
}
for(i in 2:length(dims)){
if(MARGIN==i){
command <- paste(command,", 1", sep="")
dimnames(x)[[i]][1] <- lab
} else {
command <- paste(command,", ", sep="")
}
}
command <- paste(command, ", drop=FALSE]", sep="")
y <- eval(parse(text=command))
y[] <- apply(x, setdiff(1:length(dims),MARGIN), FUN)
y
}
## Internal function for subsetting without collapsing
ksubset <- function(x, setparams){
dn <- names(dimnames(x))
sn <- names(setparams)
dims <- dim(x)
command <- "x["
if(sn[1]%in%dn){
command <- paste("x[setparams$", sn[1], sep="")
} else {
command <- "x["
}
for(i in 2:length(dims)){
if(sn[i]%in%dn){
command <- paste(command, ", setparams$", sn[i], sep="")
} else {
command <- paste(command,", ", sep="")
}
}
command <- paste(command, ", drop=FALSE]", sep="")
eval(parse(text=command))
}
## 3) Extract the desired sub-array:
if(length(setparams)>0)
res <- ksubset(res, setparams)
res.names <- names(dimnames(res))
## 4) Take the min over any parameters in MINns
if( length(MINns) > 0){
for( i in 1:length(MINns)){
res <- kapply(res, which(res.names==MINns[i]), min, 'min')
}
}
## 5) Take the mean over any parameters in AVGns
if( length(AVGns) > 0){
for( i in 1:length(AVGns)){
res <- kapply(res, which(res.names==AVGns[i]), mean, 'avg')
}
}
return(res)
})
###
### Function for testing several baseline algorithms
###
#' @name doOptim
#' @title 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
#' \code{\linkS4class{baselineAlgTest}} objects, and the predictor as a
#' \code{\linkS4class{predictionTest}} object.
#'
#' @details The function loops through the baseline algorithm tests in
#' \code{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
#' \code{cleanTmp} is \code{TRUE}, the temporary files are deleted when the
#' whole optimisation has finished.
#'
#' @aliases doOptim mvrValstats
#' @param baselineTests a list of \code{\linkS4class{baselineAlgTest}} objects.
#' The baseline algorithms and parameter values to test
#' @param X A matrix. The spectra to use in the test
#' @param y A vector or matrix. The response(s) to use in the test
#' @param predictionTest A \code{\linkS4class{predictionTest}} object. The
#' predictor and parameter values to use in the test
#' @param postproc A function, used to postprocess the baseline corrected
#' spectra prior to prediction testing. The function should take a matrix of
#' spectra as its only argument, and return a matrix of postprocessed spectra
#' @param tmpfile The basename of the files used to store intermediate
#' calculations for checkpointing. Defaults to \code{"tmp.baseline"}
#' @param verbose Logical, specifying whether the test should print out
#' progress information. Default is \code{FALSE}
#' @param cleanTmp Logical, specifying whether the intermediate files should be
#' deleted when the optimisation has finished. Default is \code{FALSE}
#' @return A list with components \item{baselineTests}{The \code{baselineTests}
#' argument} \item{results}{A list with the \code{baselineAlgResult} objects
#' for each baseline test} \item{minQualMeas}{The minimum quality measure
#' value} \item{baselineAlg.min}{The name of the baseline algorithm giving the
#' minimum quality measure value} \item{param.min}{A list with the parameter
#' values corresponding to the minimum quality measure value}
#' @examples
#' data(milk)
#' X <- milk$spectra[,-1]
#' y <- milk$spectra[,1]
#' opt <- doOptim(
#' list(ALS = new("baselineAlgTest",
#' algorithm = baselineAlgorithms$als,
#' param=list(lambda=6:7,p=0.5))),
#' X, y,
#' new("PLSRTest",
#' ncomp = 10,
#' cvsegments = cvsegments(N=45, k=5)))
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso
#' \code{\linkS4class{baselineAlgTest}},\code{\linkS4class{predictionTest}}
#' @keywords baseline spectra
#' @export
doOptim <- function(baselineTests, X, y, predictionTest,
postproc = NULL, tmpfile = "tmp.baseline",
verbose = FALSE, cleanTmp = FALSE) {
nalgs <- length(baselineTests)
## Variables to collect return values:
results <- param.min <- list()
savefiles <- character(nalgs)
minQualMeas <- numeric(nalgs)
if (verbose) cat("Looping through the", nalgs, "baseline algorithm tests\n")
for (i in seq_len(nalgs)) {
fname <- funcName(baselineTests[[i]])
blname <- names(baselineTests)[i]
if (is.null(blname) || !nzchar(blname))
blname <- name(algorithm(baselineTests[[i]]))
if (verbose) cat(i, ": ", blname, ":\n", sep = "")
savefiles[i] <- paste(tmpfile, blname, "RData", sep = ".")
if (file.exists(savefiles[i])) {
load(savefiles[i])
if (verbose) cat(" Loaded pre-calculated results from file", savefiles[i], "\n")
} else {
res <- runTest(baselineTests[[i]], X, y, predictionTest, postproc, verbose)
save(res, file = savefiles[i])
}
results[[i]] <- res
minQualMeas[i] <- minQualMeas(res)
param.min[[i]] <- param.min(res)
}
## Optionally, delete temporary files:
if (isTRUE(cleanTmp)) unlink(savefiles)
## Find and return the best results
best.i <- which.min(minQualMeas)
minQualMeas <- minQualMeas[best.i]
param.min <- param.min[[best.i]]
names(results) <- names(baselineTests)
return(list(baselineTests = baselineTests,
results = results,
minQualMeas = minQualMeas,
baselineAlg.min = name(algorithm(baselineTests[[best.i]])),
param.min = param.min))
}
## Function to extract minimum from optimisation:
#' @name overall.min
#' @title Extract the minimum from a baseline optimisation
#'
#' @description Takes the result of an optimisation (a call to \code{\link{doOptim}}) and
#' extracts the minimum quality measure value along with the parameters giving
#' rise to the value.
#'
#'
#' @param results Result of call to \code{\link{doOptim}}
#' @return A list with components \item{qualMeas}{The minimum quality measure
#' value} \item{algorithm}{The name of the baseline algorithm corresponding to
#' the minimum} \item{param}{A list with the parameter values corresponding to
#' the minimum quality measure value}
#' @author Bjørn-Helge Mevik and Kristian Hovde Liland
#' @seealso \code{\link{doOptim}}
#' @keywords baseline spectra
#' @export
overall.min <- function(results) {
with(results, list(qualMeas = minQualMeas, algorithm = baselineAlg.min,
param = param.min))
}
## The function mvrValstats has been copied (2013-08-10) from package pls (overlapping authors)
## because eval(parent.frame()) seems to break call to pls::msep.
mvrValstats <- function (object, estimate, newdata, ncomp = 1:object$ncomp,
comps, intercept = cumulative, se = FALSE, ...)
{
cumulative <- missing(comps) || is.null(comps)
if (any(estimate == "CV")) {
if (!cumulative)
stop("Cross-validation is not supported when `comps' is specified")
if (is.null(object$validation))
stop("`object' has no `validation' component")
}
nestimates <- length(estimate)
nresp <- dim(fitted(object))[2]
respnames <- dimnames(fitted(object))[[2]]
SSE <- array(dim = c(nestimates, nresp, if (cumulative) 1 +
length(ncomp) else 2), dimnames = list(estimate = estimate,
response = respnames, model = if (cumulative) {
c("(Intercept)", paste(ncomp, "comps"))
} else {
c("(Intercept)", paste("(Intercept), Comp", paste(comps,
collapse = ", ")))
}))
SST <- array(dim = c(nestimates, nresp), dimnames = list(estimate = estimate,
response = respnames))
nobj <- numeric(nestimates)
names(nobj) <- estimate
for (i in seq(along = estimate)) {
switch(estimate[i], train = {
resp <- as.matrix(model.response(model.frame(object)))
nobj[i] <- nrow(resp)
if (inherits(object$na.action, "exclude")) {
resp <- napredict(object$na.action, resp)
}
res <- if (cumulative) residuals(object, ...)[, ,
ncomp, drop = FALSE] else resp - predict(object,
comps = comps, ...)
SST[i, ] <- apply(resp, 2, var, na.rm = TRUE) * (nobj[i] -
1)
SSE[i, , ] <- cbind(SST[i, ], colSums(res^2, na.rm = TRUE))
}, test = {
if (missing(newdata)) stop("Missing `newdata'.")
newdata <- model.frame(formula(object), data = newdata)
resp <- as.matrix(model.response(newdata))
pred <- if (cumulative) predict(object, ncomp = ncomp,
newdata = newdata, ...) else predict(object,
comps = comps, newdata = newdata, ...)
nobj[i] <- nrow(newdata)
SST[i, ] <- apply(resp, 2, var) * (nobj[i] - 1)
SSE[i, , ] <- cbind(colSums(sweep(resp, 2, object$Ymeans)^2),
colSums((pred - c(resp))^2))
}, CV = {
resp <- as.matrix(model.response(model.frame(object)))
nobj[i] <- nrow(resp)
SST[i, ] <- apply(resp, 2, var) * (nobj[i] - 1)
SSE[i, , ] <- cbind(object$validation$PRESS0, object$validation$PRESS[,
ncomp, drop = FALSE])
})
}
if (cumulative)
comps <- ncomp
if (intercept)
comps <- c(0, comps)
else SSE <- SSE[, , -1, drop = FALSE]
return(list(SSE = SSE, SST = SST, nobj = nobj, comps = comps,
cumulative = cumulative))
}
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