Nothing
R/AllClasses.R
In kebabs: Kernel-Based Analysis Of Biological Sequences
##345678901234567890123456789012345678901234567890123456789012345678901234567890
###################################################
##
## Sequence kernel classes
##
###################################################
#' @rdname SequenceKernel-class
#' @name SequenceKernel-class
#' @aliases
#' class:SequenceKernel
#' SequenceKernel-class
#' SequenceKernel
#' @title Sequence Kernel Class
#'
#' @details
#' This class represents the parent class for all sequence kernels. It is an
#' abstract class and must not be instantiated.
#'
#' @slot .Data the kernel function is stored in this slot. It is executed
#' when the kernel matrix is created through invoking the kernel object.
#' @slot .userDefKernel indicates whether kernel is user defined or not.
# The boolean is set by default and reset by the \code{kebabs} kernels.
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SequenceKernel",
representation = representation
(
.Data = "function",
.userDefKernel = "logical"
),
prototype = prototype
(
.userDefKernel = TRUE
)
)
#' @rdname SpectrumKernel-class
#' @name SpectrumKernel-class
#' @aliases
#' class:SpectrumKernel
#' SpectrumKernel-class
#' SpectrumKernel
#' @title Spectrum Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the spectrum
#' kernel. The class is derived from \link{SequenceKernel}.
#'
#' @slot k length of the substrings considered by the kernel
#' @slot r exponent (for details see \link{spectrumKernel})
#' @slot annSpec when set the kernel evaluates annotation information
#' @slot distWeight distance weighting function or vector
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of kmers not their counts
#' @slot revComplement consider a kmer and its reverse complement
#' as the same feature
#' @slot mixCoef mixing coefficients for mixture kernel
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SpectrumKernel",
representation = representation
(
k = "numeric",
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical",
mixCoef = "numeric"
),
prototype = prototype
(
k = 3,
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE,
mixCoef = numeric(0)
),
contains="SequenceKernel"
)
#' @rdname MismatchKernel-class
#' @name MismatchKernel-class
#' @aliases
#' class:MismatchKernel
#' MismatchKernel-class
#' MismatchKernel
#' @title Mismatch Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the mismatch
#' kernel. The class is derived from \link{SequenceKernel}.
#'
#' @slot k length of the substrings considered by the kernel
#' @slot m maximum number of mismatches
#' @slot r exponent (for details see \link{mismatchKernel})
#' @slot annSpec not used for mismatch kernel
#' @slot distWeight not used for mismatch kernel
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of kmers not their counts
#' @slot revComplement not used for mismatch kernel
#' @slot mixCoef not used for mismatch kernel
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("MismatchKernel",
representation=representation
(
k = "numeric",
m = "numeric",
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical"
),
prototype = prototype
(
k = 3,
m = 1,
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE
),
contains="SequenceKernel"
)
#' @rdname MotifKernel-class
#' @name MotifKernel-class
#' @aliases
#' class:MotifKernel
#' MotifKernel-class
#' MotifKernel
#' @title Motif Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the motif
#' kernel. The class is derived from \link{SequenceKernel}. The motif
#' character vector is not stored in the kernel object.
#'
#' @slot r exponent (for details see \link{motifKernel})
#' @slot annSpec when set the kernel evaluates annotation information
#' @slot distWeight distance weighting function or vector
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of motifs not their counts
#' @slot revComplement consider a kmer and its reverse complement
#' as the same feature
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("MotifKernel",
representation = representation
(
## motifs stored only in environment
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical"
),
prototype = prototype
(
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE
),
contains="SequenceKernel"
)
#' @rdname GappyPairKernel-class
#' @name GappyPairKernel-class
#' @aliases
#' class:GappyPairKernel
#' GappyPairKernel-class
#' GappyPairKernel
#' @title Gappy Pair Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the gappy pair
#' kernel. The kernel considers adjacent pairs of kmers with up to m
#' irrelevant characters between the pair. The class is derived from
#' \link{SequenceKernel}.
#'
#' @slot k length of the substrings considered by the kernel
#' @slot m maximum number of irrelevant character between two kmers
#' @slot r exponent (for details see \link{gappyPairKernel})
#' @slot annSpec when set the kernel evaluates annotation information
#' @slot distWeight distance weighting function or vector
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of kmers not their counts
#' @slot revComplement consider a kmer and its reverse complement
#' as the same feature
#' @slot mixCoef mixing coefficients for mixture kernel
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("GappyPairKernel",
representation = representation
(
k = "numeric",
m = "numeric",
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical",
mixCoef = "numeric"
),
prototype = prototype
(
k = 3,
m = 1,
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE,
mixCoef = numeric(0)
),
contains="SequenceKernel"
)
#' @rdname SymmetricPairKernel-class
#' @name SymmetricPairKernel-class
#' @aliases
#' class:SymmetricPairKernel
#' SymmetricPairKernel-class
#' SymmetricPairKernel
#' @title Symmetric Pair Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the symmetric pair
#' kernel. The kernel does not compute similarity between single samples but
#' between two pairs of samples based on a regular sequence kernel for single
#' samples. The class is derived from \link{SequenceKernel}.
#'
#' @slot siKernel single instance kernel
#' @slot kernelType type of pair kernel
#' @slot r exponent (for details see \link{gappyPairKernel})
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SymmetricPairKernel",
representation = representation
(
siKernel = "ANY",
kernelType = "character",
r = "numeric"
),
prototype = prototype
(
siKernel = NULL,
kernelType = "arithMean",
r = 1
),
contains="SequenceKernel"
)
###################################################
##
## BioVector kernel classes
##
###################################################
## imported classes from S4Vectors
## setClassUnion("DataFrame_OR_NULL", c("NULL", "DataFrame"))
## setClassUnion("character_OR_NULL", c("NULL", "character"))
#' @rdname BioVector-class
#' @title BioVector, DNAVector, RNAVector and AAVector Classes
#' @name BioVector-class
#' @aliases
#' class:BioVector
#' BioVector-class
#'
#' @details
#' This class is the parent class for representing sets of biological
#' sequences with support of lowercase characters. The derived classes
#' \code{\linkS4class{DNAVector}}, \code{\linkS4class{RNAVector}} and
#' \code{\linkS4class{AAVector}} hold DNA-, RNA- or AA-sequences which can
#' contain also lowercase characters. In many cases repeat regions are coded
#' as lowercase characters and with the \code{\linkS4class{BioVector}} based
#' classes sequence analysis with and without repeat regions can be performed
#' from the same sequence set. Whenever lowercase is not needed please use the
#' \code{\linkS4class{XStringSet}} based classes as they provide much richer
#' functionality. The class \code{BioVector }is derived from "character" and
#' holds the sequence information as character vector. Interfaces for the
#' small set of functions needed in KeBABS are designed consistent with
#' \code{\linkS4class{XStringSet}}.
#'
#' @slot NAMES sequence names
#' @slot elementMetadata element metadata, which is applicable per element
#' and holds a DataFrame with one entry per sequence in each column.
#' KeBABS uses the column names "annotation" and "offset".
#' @slot metadata metadata applicable for the entire sequence set as list.
#' KeBABS stores the annotation character set as list element
#' named "annotationCharset".
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("BioVector",
representation=representation
(
NAMES = "character_OR_NULL",
elementMetadata = "DataFrame_OR_NULL",
metadata = "list"
),
prototype = prototype
(
NAMES = NULL,
elementMetadata = NULL,
metadata = list()
),
contains="character"
)
#' @rdname BioVector-class
#' @name DNAVector-class
#' @aliases
#' class:DNAVector
#' DNAVector-class
#'
#' @details
#' Instances of the \code{DNAVector} class are used for representing sets of
#' DNA sequences.
#'
setClass("DNAVector",
contains="BioVector"
)
#' @rdname BioVector-class
#' @name RNAVector-class
#' @aliases
#' class:RNAVector
#' RNAVector-class
#'
#' @details
#' Instances of the \code{RNAVector} class are used for representing sets of
#' RNA sequences.
#'
setClass("RNAVector",
contains="BioVector"
)
#' @rdname BioVector-class
#' @name AAVector-class
#' @aliases
#' class:AAVector
#' AAVector-class
#'
#' @details
#' Instances of the \code{AAVector} class are used for representing sets of
#' amino acid sequences.
#'
setClass("AAVector",
contains="BioVector"
)
###################################################
##
## Kernel matrix class
##
###################################################
#' @rdname KernelMatrix-class
#' @name KernelMatrix-class
#' @aliases
#' class:KernelMatrix
#' KernelMatrix-class
#' KernelMatrix
#' @title Kernel Matrix Class
#'
#' @details
#' Instances of this class are used in KeBABS for storing a kernel matrix.
#' The hidden data part ".Data" contains the matrix.
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("KernelMatrix",representation("matrix"),
prototype=structure(.Data=matrix()))
###################################################
##
## Explicit representation classes
##
###################################################
#' @rdname ExplicitRepresentation-class
#' @name ExplicitRepresentation
#' @aliases
#' class:ExplicitRepresentation
#' ExplicitRepresentation-class
#' ExplicitRepresentation
#' @title Explicit Representation Dense and Sparse Classes
#'
#' @details
#' In KeBABS this class is the virtual parent class for explicit representations
#' generated from a set of biological sequences for a given kernel. The derived
#' classes \code{\linkS4class{ExplicitRepresentationDense}} and
#' \code{\linkS4class{ExplicitRepresentationSparse}} are meant to hold explicit
#' representations in dense or sparse format. The kernel used to generate the
#' explicit representation is stored together with the data.
#'
#' @slot usedKernel kernel used for generating the explicit representation
#' @slot quadratic boolean indicating a quadratic explicit representation
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ExplicitRepresentation",
representation = representation
(
usedKernel = "ANY",
quadratic = "logical",
"VIRTUAL"
),
prototype = prototype
(
usedKernel = NULL,
quadratic = FALSE
)
)
#' @rdname ExplicitRepresentation-class
#' @aliases
#' class:ExplicitRepresentationDense
#' ExplicitRepresentationDense-class
#' ExplicitRepresentationDense
#'
#' @details
#' Instances of this class are used for storing explicit representations
#' in dense matrix format. This class is derived from
#' \code{\linkS4class{ExplicitRepresentation}}.
setClass("ExplicitRepresentationDense",
contains = c("ExplicitRepresentation", "matrix"))
#' @rdname ExplicitRepresentation-class
#' @aliases
#' class:ExplicitRepresentationSparse
#' ExplicitRepresentationSparse-class
#' ExplicitRepresentationSparse
#'
#' @details
#' Instances of this class are used for storing explicit representations
#' in sparse \code{\linkS4class{dgRMatrix}} format. This class is derived from
#' \code{\linkS4class{ExplicitRepresentation}}.
setClass("ExplicitRepresentationSparse",
contains = c("ExplicitRepresentation", "dgRMatrix"))
###################################################
##
## Prediction profile class
##
###################################################
#' @rdname PredictionProfile-class
#' @name PredictionProfile-class
#' @aliases
#' class:PredictionProfile
#' PredictionProfile-class
#' PredictionProfile
#' @title Prediction Profile Class
#'
#' @details
#' This class stores prediction profiles generated for a set of biological
#' sequences from a trained model. Prediction profiles show the relevance
#' of individual sequence positions for the prediction result.
#'
#' @slot sequences sequence information for the samples with profiles
#' @slot baselines baselines generated from the offset in the model spread\cr
#' to all sequence positions
#' @slot profiles prediction profile information stored as dense matrix with\cr
#' the rows as samples and the columns as positions in the sample
#' @slot kernel kernel used for training the model on which these prediction\cr
#' profiles are based
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("PredictionProfile",
representation=representation
(
sequences = "ANY",
baselines = "numeric",
profiles = "ANY",
kernel = "ANY"
),
prototype=prototype
(
sequences = NULL,
baselines = 0,
profiles = NULL,
kernel = NULL
)
)
###################################################
##
## ROCData class
##
###################################################
#' @rdname ROCData-class
#' @name ROCData-class
#' @aliases
#' class:ROCData
#' ROCData-class
#' ROCData
#' @title ROC Data Class
#'
#' @details
#' This class stores receiver operating characteristics (ROC) data.
#'
#' @slot AUC area under ROC curve
#' @slot TPR true positive rate for varying threshold
#' @slot FPR false positive rate for varying threshold
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ROCData",
representation=representation
(
AUC = "numeric",
TPR = "numeric",
FPR = "numeric"
),
prototype=prototype
(
AUC = numeric(0),
TPR = numeric(0),
FPR = numeric(0)
)
)
###################################################
##
## kebabs model classes
##
###################################################
#' @rdname SVMInformation-class
#' @name SVMInformation-class
#' @aliases
#' class:SVMInformation
#' SVMInformation-class
#' SVMInformation
#' @title SVM Information Class
#'
#' @details
#' Instances of this class store SVM related information.
#'
#' @slot availPackages installed SVM packages
#' @slot reqSVM user requested SVM implementation
#' @slot reqPackage user requested package
#' @slot reqSVMPar user requested SVM parameters
#' @slot reqKernel user requested kernel
#' @slot reqExplicit user requested indictor of expl. rep. processing
#' @slot reqExplicitType user requested expl. rep. type
#' @slot reqFeatureType user requested feature type
#' @slot selSVM selected SVM implementation
#' @slot selPackage selected package
#' @slot selSVMPar selected SVM parameters
#' @slot selKernel selected kernel
#' @slot selExplicit selected indictor of expl. rep. processing
#' @slot explicitKernel kernel for explicit representation
#' @slot featureWeights indicator for feature weights
#' @slot weightLimit cutoff value for feature weights
#' @slot probModel indicator for probability model
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SVMInformation",
representation = representation
(
availPackages = "character",
reqSVM = "character",
reqPackage = "character",
reqSVMPar = "list",
reqKernel = "ANY",
reqExplicit = "character",
reqExplicitType = "character",
reqFeatureType = "character",
selSVM = "character",
selPackage = "character",
selSVMPar = "list",
selKernel = "ANY",
selExplicit = "logical",
explicitKernel = "character",
featureWeights = "character",
weightLimit = "numeric",
probModel = "logical"
),
prototype = prototype
(
availPackages = character(0),
reqSVM = character(0),
reqPackage = character(0),
reqSVMPar = list(),
reqKernel = NULL,
reqExplicit = character(0),
reqExplicitType = character(0),
reqFeatureType = "linear",
selSVM = character(0),
selPackage = character(0),
selSVMPar = list(),
selKernel = NULL,
selExplicit = FALSE,
explicitKernel = character(0),
featureWeights = "auto",
weightLimit = 0,
probModel = FALSE
),
)
#' @rdname ControlInformation-class
#' @name ControlInformation-class
#' @aliases
#' class:ControlInformation
#' ControlInformation-class
#' ControlInformation
#' @title KeBABS Control Information Class
#'
#' @details
#' Instances of this class store control information for the
#' KeBABS meta-SVM.
#'
#' @slot classification indicator for classification task
#' @slot multiclassType type of multiclass SVM
#' @slot featureWeights feature weights control information
#' @slot selMethod selected processing method
#' @slot onlyDense indicator that only dense processing can be performed
#' @slot sparse indicator for sparse processing
#' @slot runtimeWarning indicator for runtime warning
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ControlInformation",
representation = representation
(
classification = "logical",
multiclassType = "character",
featureWeights = "logical",
selMethod = "character",
onlyDense = "logical",
sparse = "logical",
runtimeWarning = "logical"
),
prototype = prototype
(
classification = FALSE,
multiclassType = character(0),
featureWeights = FALSE,
selMethod = character(0),
onlyDense = FALSE,
sparse = FALSE,
runtimeWarning = TRUE
),
)
#' @rdname CrossValidationResult-class
#' @name CrossValidationResult-class
#' @aliases
#' class:CrossValidationResult
#' CrossValidationResult-class
#' CrossValidationResult
#' @title Cross Validation Result Class
#'
#' @details
#' Instances of this class store the result of cross validation.
#'
#' @slot cross number of folds for cross validation
#' @slot noCross number of CV runs
#' @slot groupBy group assignment of samples
#' @slot perfParameters collected performance parameters
#' @slot outerCV flag indicating outer CV
#' @slot folds folds used in CV
#' @slot cvError cross validation error
#' @slot foldErrors fold errors
#' @slot noSV number of support vectors
#' @slot ACC cross validation accuracy
#' @slot BACC cross validation balanced accuracy
#' @slot MCC cross validation Matthews correlation coefficient
#' @slot AUC cross validation area under the ROC curve
#' @slot foldACC fold accuracy
#' @slot foldBACC fold balanced accuracy
#' @slot foldMCC fold Matthews correlation coefficient
#' @slot foldAUC fold area under the ROC curve
#' @slot sumAlphas sum of alphas
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("CrossValidationResult",
representation = representation
(
cross = "numeric",
noCross = "numeric",
groupBy = "ANY",
perfParameters = "character",
outerCV = "logical",
folds = "list",
cvError = "numeric",
foldErrors = "matrix",
ACC = "numeric",
BACC = "numeric",
MCC = "numeric",
AUC = "numeric",
foldACC = "matrix",
foldBACC = "matrix",
foldMCC = "matrix",
foldAUC = "matrix",
noSV = "numeric",
sumAlphas = "numeric"
),
prototype = prototype
(
cross = numeric(0),
noCross = numeric(0),
groupBy = NULL,
perfParameters = character(0),
outerCV = FALSE,
folds = list(),
cvError = numeric(0),
foldErrors = matrix(0),
ACC = numeric(0),
BACC = numeric(0),
MCC = numeric(0),
AUC = numeric(0),
foldACC = matrix(0),
foldBACC = matrix(0),
foldMCC = matrix(0),
foldAUC = matrix(0),
noSV = numeric(0),
sumAlphas = numeric(0)
),
)
#' @rdname ModelSelectionResult-class
#' @name ModelSelectionResult-class
#' @aliases
#' class:ModelSelectionResult
#' ModelSelectionResult-class
#' ModelSelectionResult
#' @title Model Selection Result Class
#'
#' @details
#' Instances of this class store the result of grid search or
#' model selection.
#'
#' @slot cross number of folds for cross validation
#' @slot noCross number of CV runs
#' @slot groupBy group assignment of samples
#' @slot nestedCross number of folds for outer CV
#' @slot noNestedCross number of runs of outer CV
#' @slot perfParameters collected performance parameters
#' @slot perfObjective performance criterion for grid search / model selection
#' @slot gridRows rows in grid search (i.e. kernels)
#' @slot gridCols columns in grid search
#' @slot gridErrors grid errors
#' @slot gridACC grid accuracy
#' @slot gridBACC grid balanced accuracy
#' @slot gridMCC grid Matthews correlation coefficient
#' @slot gridAUC grid area under the ROC curve
#' @slot gridNoSV grid number of support vectors
#' @slot gridSumAlphas grid sum of alphas
#' @slot smallestCVError smallest CV error
#' @slot selGridRow grid row of best result
#' @slot selGridCol grid col of best result
#' @slot fullModel full model for best result
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ModelSelectionResult",
representation = representation
(
cross = "numeric",
noCross = "numeric",
groupBy = "ANY",
nestedCross = "numeric",
noNestedCross = "numeric",
perfParameters = "character",
perfObjective = "character",
gridRows = "list",
gridCols = "ANY",
gridErrors = "ANY",
gridACC = "ANY",
gridBACC = "ANY",
gridMCC = "ANY",
gridAUC = "ANY",
gridNoSV = "ANY",
gridSumAlphas = "ANY",
smallestCVError = "numeric",
selGridRow = "ANY",
selGridCol = "ANY",
fullModel = "ANY"
),
prototype = prototype
(
cross = numeric(0),
noCross = numeric(0),
groupBy = NULL,
nestedCross = numeric(0),
noNestedCross = numeric(0),
perfParameters = character(0),
perfObjective = character(0),
gridRows = list(),
gridCols = NULL,
gridErrors = NULL,
gridACC = NULL,
gridBACC = NULL,
gridMCC = NULL,
gridAUC = NULL,
gridNoSV = NULL,
gridSumAlphas = NULL,
smallestCVError = Inf,
selGridRow = NULL,
selGridCol = NULL,
fullModel = NULL
),
)
#' @rdname KBModel-class
#' @name KBModel-class
#' @aliases
#' class:KBModel
#' KBModel-class
#' KBModel
#' @title KeBABS Model Class
#'
#' @details
#' Instances of this class represent a model object for the KeBABS
#' meta-SVM.
#'
#' @slot call invocation string of KeBABS meta-SVM
#' @slot numSequences number of sequences used for training
#' @slot sel index subset of samples used for training
#' @slot y vector of target values
#' @slot levels levels of target
#' @slot numClasses number of classes
#' @slot classNames class labels
#' @slot classWeights class weights
#' @slot SV support vectors
#' @slot svIndex support vector indices
#' @slot alphaIndex list of SVM indices per SVM
#' @slot trainingFeatures feature names used in training
#' @slot featureWeights feature Weights
#' @slot b model offset
#' @slot probA fitted logistic function parameter A
#' @slot probB fitted logistic function parameter A
#' @slot sigma scale of Laplacian fitted to regression residuals
#' @slot cvResult cross validation result of class
#' \code{\linkS4class{CrossValidationResult}}
#' @slot modelSelResult model selection / grid search result of class
#' \code{\linkS4class{ModelSelectionResult}}
#' @slot ctlInfo KeBABS control info of class
#' \code{\linkS4class{ControlInformation}}
#' @slot svmInfo info about requested / used SVM of class
#' \code{\linkS4class{SVMInformation}}
#' @slot svmModel original model returned from SVM
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("KBModel",
representation = representation
(
call = "character", # invocation string
numSequences = "numeric", # number of sequences
sel = "numeric", # selected subset of sequences
y = "ANY", # target values
levels = "ANY", # levels of target
numClasses = "numeric", # number of classes
classNames = "ANY", # class labels
classWeights = "numeric", # class weights
SV = "ANY", # support vectors
svIndex = "ANY", # support vector indices
alphaIndex = "ANY", # list of SV indices per SVM
trainingFeatures = "character_OR_NULL", # feature names of training
featureWeights = "ANY", # feature weights
b = "numeric", # b (bias)
probA = "numeric", # fitted logistic function param A
probB = "numeric", # fitted logistic function param B
sigma = "numeric", # for regression only - scale of
# laplacian fitted to residuals
cvResult = "CrossValidationResult", # cross validation result
modelSelResult = "ANY", # model selection result
ctlInfo = "ControlInformation", # kb control info
svmInfo = "SVMInformation", # info about requested/used svm
svmModel = "ANY" # model object of ksvm, LiblineaR,
# libsvm via e1071, psvm,
# rvm, lssvm, penalizedSVM
),
prototype = prototype
(
call = character(0),
numSequences = 0,
sel = NULL,
y = NULL,
levels = NULL,
numClasses = 0,
classNames = NULL,
classWeights = numeric(0),
SV = NULL,
svIndex = numeric(0),
alphaIndex = numeric(0),
trainingFeatures = character(0),
featureWeights = NULL,
b = 0,
probA = as.numeric(NA),
probB = as.numeric(NA),
sigma = as.numeric(NA),
cvResult = NULL,
modelSelResult = NULL,
ctlInfo = NULL,
svmInfo = NULL,
svmModel = NULL
),
)
setClass("KebabsInfo",
representation=representation
(
supportedPkgs = "character",
allowedSeqClasses = "character",
allowedSeqSetClasses = "character",
allowedSeqTypes = "numeric",
classifierMap = "matrix",
svmParMap = "matrix",
modelParMap = "matrix",
kebabsDebug = "logical"
),
prototype=prototype
(
supportedPkgs = character(0),
allowedSeqClasses = character(0),
allowedSeqSetClasses = character(0),
allowedSeqTypes = numeric(0),
classifierMap = matrix(0),
svmParMap = matrix(0),
modelParMap = matrix(0),
kebabsDebug = FALSE
),
)
Try the kebabs package in your browser
Any scripts or data that you put into this service are public.
kebabs documentation built on Nov. 8, 2020, 7:38 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
##345678901234567890123456789012345678901234567890123456789012345678901234567890
###################################################
##
## Sequence kernel classes
##
###################################################
#' @rdname SequenceKernel-class
#' @name SequenceKernel-class
#' @aliases
#' class:SequenceKernel
#' SequenceKernel-class
#' SequenceKernel
#' @title Sequence Kernel Class
#'
#' @details
#' This class represents the parent class for all sequence kernels. It is an
#' abstract class and must not be instantiated.
#'
#' @slot .Data the kernel function is stored in this slot. It is executed
#' when the kernel matrix is created through invoking the kernel object.
#' @slot .userDefKernel indicates whether kernel is user defined or not.
# The boolean is set by default and reset by the \code{kebabs} kernels.
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SequenceKernel",
representation = representation
(
.Data = "function",
.userDefKernel = "logical"
),
prototype = prototype
(
.userDefKernel = TRUE
)
)
#' @rdname SpectrumKernel-class
#' @name SpectrumKernel-class
#' @aliases
#' class:SpectrumKernel
#' SpectrumKernel-class
#' SpectrumKernel
#' @title Spectrum Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the spectrum
#' kernel. The class is derived from \link{SequenceKernel}.
#'
#' @slot k length of the substrings considered by the kernel
#' @slot r exponent (for details see \link{spectrumKernel})
#' @slot annSpec when set the kernel evaluates annotation information
#' @slot distWeight distance weighting function or vector
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of kmers not their counts
#' @slot revComplement consider a kmer and its reverse complement
#' as the same feature
#' @slot mixCoef mixing coefficients for mixture kernel
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SpectrumKernel",
representation = representation
(
k = "numeric",
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical",
mixCoef = "numeric"
),
prototype = prototype
(
k = 3,
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE,
mixCoef = numeric(0)
),
contains="SequenceKernel"
)
#' @rdname MismatchKernel-class
#' @name MismatchKernel-class
#' @aliases
#' class:MismatchKernel
#' MismatchKernel-class
#' MismatchKernel
#' @title Mismatch Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the mismatch
#' kernel. The class is derived from \link{SequenceKernel}.
#'
#' @slot k length of the substrings considered by the kernel
#' @slot m maximum number of mismatches
#' @slot r exponent (for details see \link{mismatchKernel})
#' @slot annSpec not used for mismatch kernel
#' @slot distWeight not used for mismatch kernel
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of kmers not their counts
#' @slot revComplement not used for mismatch kernel
#' @slot mixCoef not used for mismatch kernel
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("MismatchKernel",
representation=representation
(
k = "numeric",
m = "numeric",
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical"
),
prototype = prototype
(
k = 3,
m = 1,
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE
),
contains="SequenceKernel"
)
#' @rdname MotifKernel-class
#' @name MotifKernel-class
#' @aliases
#' class:MotifKernel
#' MotifKernel-class
#' MotifKernel
#' @title Motif Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the motif
#' kernel. The class is derived from \link{SequenceKernel}. The motif
#' character vector is not stored in the kernel object.
#'
#' @slot r exponent (for details see \link{motifKernel})
#' @slot annSpec when set the kernel evaluates annotation information
#' @slot distWeight distance weighting function or vector
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of motifs not their counts
#' @slot revComplement consider a kmer and its reverse complement
#' as the same feature
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("MotifKernel",
representation = representation
(
## motifs stored only in environment
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical"
),
prototype = prototype
(
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE
),
contains="SequenceKernel"
)
#' @rdname GappyPairKernel-class
#' @name GappyPairKernel-class
#' @aliases
#' class:GappyPairKernel
#' GappyPairKernel-class
#' GappyPairKernel
#' @title Gappy Pair Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the gappy pair
#' kernel. The kernel considers adjacent pairs of kmers with up to m
#' irrelevant characters between the pair. The class is derived from
#' \link{SequenceKernel}.
#'
#' @slot k length of the substrings considered by the kernel
#' @slot m maximum number of irrelevant character between two kmers
#' @slot r exponent (for details see \link{gappyPairKernel})
#' @slot annSpec when set the kernel evaluates annotation information
#' @slot distWeight distance weighting function or vector
#' @slot normalized data generated with this kernel object is normalized
#' @slot exact use exact character set for evaluation
#' @slot ignoreLower ignore lower case characters in the sequence
#' @slot presence consider only the presence of kmers not their counts
#' @slot revComplement consider a kmer and its reverse complement
#' as the same feature
#' @slot mixCoef mixing coefficients for mixture kernel
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("GappyPairKernel",
representation = representation
(
k = "numeric",
m = "numeric",
r = "numeric",
annSpec = "logical",
distWeight = "ANY",
normalized = "logical",
exact = "logical",
ignoreLower = "logical",
presence = "logical",
revComplement = "logical",
mixCoef = "numeric"
),
prototype = prototype
(
k = 3,
m = 1,
r = 1,
annSpec = FALSE,
distWeight = numeric(0),
normalized = TRUE,
exact = TRUE,
ignoreLower = TRUE,
presence = FALSE,
revComplement = FALSE,
mixCoef = numeric(0)
),
contains="SequenceKernel"
)
#' @rdname SymmetricPairKernel-class
#' @name SymmetricPairKernel-class
#' @aliases
#' class:SymmetricPairKernel
#' SymmetricPairKernel-class
#' SymmetricPairKernel
#' @title Symmetric Pair Kernel Class
#'
#' @details
#' Instances of this class represent a kernel object for the symmetric pair
#' kernel. The kernel does not compute similarity between single samples but
#' between two pairs of samples based on a regular sequence kernel for single
#' samples. The class is derived from \link{SequenceKernel}.
#'
#' @slot siKernel single instance kernel
#' @slot kernelType type of pair kernel
#' @slot r exponent (for details see \link{gappyPairKernel})
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SymmetricPairKernel",
representation = representation
(
siKernel = "ANY",
kernelType = "character",
r = "numeric"
),
prototype = prototype
(
siKernel = NULL,
kernelType = "arithMean",
r = 1
),
contains="SequenceKernel"
)
###################################################
##
## BioVector kernel classes
##
###################################################
## imported classes from S4Vectors
## setClassUnion("DataFrame_OR_NULL", c("NULL", "DataFrame"))
## setClassUnion("character_OR_NULL", c("NULL", "character"))
#' @rdname BioVector-class
#' @title BioVector, DNAVector, RNAVector and AAVector Classes
#' @name BioVector-class
#' @aliases
#' class:BioVector
#' BioVector-class
#'
#' @details
#' This class is the parent class for representing sets of biological
#' sequences with support of lowercase characters. The derived classes
#' \code{\linkS4class{DNAVector}}, \code{\linkS4class{RNAVector}} and
#' \code{\linkS4class{AAVector}} hold DNA-, RNA- or AA-sequences which can
#' contain also lowercase characters. In many cases repeat regions are coded
#' as lowercase characters and with the \code{\linkS4class{BioVector}} based
#' classes sequence analysis with and without repeat regions can be performed
#' from the same sequence set. Whenever lowercase is not needed please use the
#' \code{\linkS4class{XStringSet}} based classes as they provide much richer
#' functionality. The class \code{BioVector }is derived from "character" and
#' holds the sequence information as character vector. Interfaces for the
#' small set of functions needed in KeBABS are designed consistent with
#' \code{\linkS4class{XStringSet}}.
#'
#' @slot NAMES sequence names
#' @slot elementMetadata element metadata, which is applicable per element
#' and holds a DataFrame with one entry per sequence in each column.
#' KeBABS uses the column names "annotation" and "offset".
#' @slot metadata metadata applicable for the entire sequence set as list.
#' KeBABS stores the annotation character set as list element
#' named "annotationCharset".
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("BioVector",
representation=representation
(
NAMES = "character_OR_NULL",
elementMetadata = "DataFrame_OR_NULL",
metadata = "list"
),
prototype = prototype
(
NAMES = NULL,
elementMetadata = NULL,
metadata = list()
),
contains="character"
)
#' @rdname BioVector-class
#' @name DNAVector-class
#' @aliases
#' class:DNAVector
#' DNAVector-class
#'
#' @details
#' Instances of the \code{DNAVector} class are used for representing sets of
#' DNA sequences.
#'
setClass("DNAVector",
contains="BioVector"
)
#' @rdname BioVector-class
#' @name RNAVector-class
#' @aliases
#' class:RNAVector
#' RNAVector-class
#'
#' @details
#' Instances of the \code{RNAVector} class are used for representing sets of
#' RNA sequences.
#'
setClass("RNAVector",
contains="BioVector"
)
#' @rdname BioVector-class
#' @name AAVector-class
#' @aliases
#' class:AAVector
#' AAVector-class
#'
#' @details
#' Instances of the \code{AAVector} class are used for representing sets of
#' amino acid sequences.
#'
setClass("AAVector",
contains="BioVector"
)
###################################################
##
## Kernel matrix class
##
###################################################
#' @rdname KernelMatrix-class
#' @name KernelMatrix-class
#' @aliases
#' class:KernelMatrix
#' KernelMatrix-class
#' KernelMatrix
#' @title Kernel Matrix Class
#'
#' @details
#' Instances of this class are used in KeBABS for storing a kernel matrix.
#' The hidden data part ".Data" contains the matrix.
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("KernelMatrix",representation("matrix"),
prototype=structure(.Data=matrix()))
###################################################
##
## Explicit representation classes
##
###################################################
#' @rdname ExplicitRepresentation-class
#' @name ExplicitRepresentation
#' @aliases
#' class:ExplicitRepresentation
#' ExplicitRepresentation-class
#' ExplicitRepresentation
#' @title Explicit Representation Dense and Sparse Classes
#'
#' @details
#' In KeBABS this class is the virtual parent class for explicit representations
#' generated from a set of biological sequences for a given kernel. The derived
#' classes \code{\linkS4class{ExplicitRepresentationDense}} and
#' \code{\linkS4class{ExplicitRepresentationSparse}} are meant to hold explicit
#' representations in dense or sparse format. The kernel used to generate the
#' explicit representation is stored together with the data.
#'
#' @slot usedKernel kernel used for generating the explicit representation
#' @slot quadratic boolean indicating a quadratic explicit representation
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ExplicitRepresentation",
representation = representation
(
usedKernel = "ANY",
quadratic = "logical",
"VIRTUAL"
),
prototype = prototype
(
usedKernel = NULL,
quadratic = FALSE
)
)
#' @rdname ExplicitRepresentation-class
#' @aliases
#' class:ExplicitRepresentationDense
#' ExplicitRepresentationDense-class
#' ExplicitRepresentationDense
#'
#' @details
#' Instances of this class are used for storing explicit representations
#' in dense matrix format. This class is derived from
#' \code{\linkS4class{ExplicitRepresentation}}.
setClass("ExplicitRepresentationDense",
contains = c("ExplicitRepresentation", "matrix"))
#' @rdname ExplicitRepresentation-class
#' @aliases
#' class:ExplicitRepresentationSparse
#' ExplicitRepresentationSparse-class
#' ExplicitRepresentationSparse
#'
#' @details
#' Instances of this class are used for storing explicit representations
#' in sparse \code{\linkS4class{dgRMatrix}} format. This class is derived from
#' \code{\linkS4class{ExplicitRepresentation}}.
setClass("ExplicitRepresentationSparse",
contains = c("ExplicitRepresentation", "dgRMatrix"))
###################################################
##
## Prediction profile class
##
###################################################
#' @rdname PredictionProfile-class
#' @name PredictionProfile-class
#' @aliases
#' class:PredictionProfile
#' PredictionProfile-class
#' PredictionProfile
#' @title Prediction Profile Class
#'
#' @details
#' This class stores prediction profiles generated for a set of biological
#' sequences from a trained model. Prediction profiles show the relevance
#' of individual sequence positions for the prediction result.
#'
#' @slot sequences sequence information for the samples with profiles
#' @slot baselines baselines generated from the offset in the model spread\cr
#' to all sequence positions
#' @slot profiles prediction profile information stored as dense matrix with\cr
#' the rows as samples and the columns as positions in the sample
#' @slot kernel kernel used for training the model on which these prediction\cr
#' profiles are based
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("PredictionProfile",
representation=representation
(
sequences = "ANY",
baselines = "numeric",
profiles = "ANY",
kernel = "ANY"
),
prototype=prototype
(
sequences = NULL,
baselines = 0,
profiles = NULL,
kernel = NULL
)
)
###################################################
##
## ROCData class
##
###################################################
#' @rdname ROCData-class
#' @name ROCData-class
#' @aliases
#' class:ROCData
#' ROCData-class
#' ROCData
#' @title ROC Data Class
#'
#' @details
#' This class stores receiver operating characteristics (ROC) data.
#'
#' @slot AUC area under ROC curve
#' @slot TPR true positive rate for varying threshold
#' @slot FPR false positive rate for varying threshold
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ROCData",
representation=representation
(
AUC = "numeric",
TPR = "numeric",
FPR = "numeric"
),
prototype=prototype
(
AUC = numeric(0),
TPR = numeric(0),
FPR = numeric(0)
)
)
###################################################
##
## kebabs model classes
##
###################################################
#' @rdname SVMInformation-class
#' @name SVMInformation-class
#' @aliases
#' class:SVMInformation
#' SVMInformation-class
#' SVMInformation
#' @title SVM Information Class
#'
#' @details
#' Instances of this class store SVM related information.
#'
#' @slot availPackages installed SVM packages
#' @slot reqSVM user requested SVM implementation
#' @slot reqPackage user requested package
#' @slot reqSVMPar user requested SVM parameters
#' @slot reqKernel user requested kernel
#' @slot reqExplicit user requested indictor of expl. rep. processing
#' @slot reqExplicitType user requested expl. rep. type
#' @slot reqFeatureType user requested feature type
#' @slot selSVM selected SVM implementation
#' @slot selPackage selected package
#' @slot selSVMPar selected SVM parameters
#' @slot selKernel selected kernel
#' @slot selExplicit selected indictor of expl. rep. processing
#' @slot explicitKernel kernel for explicit representation
#' @slot featureWeights indicator for feature weights
#' @slot weightLimit cutoff value for feature weights
#' @slot probModel indicator for probability model
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("SVMInformation",
representation = representation
(
availPackages = "character",
reqSVM = "character",
reqPackage = "character",
reqSVMPar = "list",
reqKernel = "ANY",
reqExplicit = "character",
reqExplicitType = "character",
reqFeatureType = "character",
selSVM = "character",
selPackage = "character",
selSVMPar = "list",
selKernel = "ANY",
selExplicit = "logical",
explicitKernel = "character",
featureWeights = "character",
weightLimit = "numeric",
probModel = "logical"
),
prototype = prototype
(
availPackages = character(0),
reqSVM = character(0),
reqPackage = character(0),
reqSVMPar = list(),
reqKernel = NULL,
reqExplicit = character(0),
reqExplicitType = character(0),
reqFeatureType = "linear",
selSVM = character(0),
selPackage = character(0),
selSVMPar = list(),
selKernel = NULL,
selExplicit = FALSE,
explicitKernel = character(0),
featureWeights = "auto",
weightLimit = 0,
probModel = FALSE
),
)
#' @rdname ControlInformation-class
#' @name ControlInformation-class
#' @aliases
#' class:ControlInformation
#' ControlInformation-class
#' ControlInformation
#' @title KeBABS Control Information Class
#'
#' @details
#' Instances of this class store control information for the
#' KeBABS meta-SVM.
#'
#' @slot classification indicator for classification task
#' @slot multiclassType type of multiclass SVM
#' @slot featureWeights feature weights control information
#' @slot selMethod selected processing method
#' @slot onlyDense indicator that only dense processing can be performed
#' @slot sparse indicator for sparse processing
#' @slot runtimeWarning indicator for runtime warning
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ControlInformation",
representation = representation
(
classification = "logical",
multiclassType = "character",
featureWeights = "logical",
selMethod = "character",
onlyDense = "logical",
sparse = "logical",
runtimeWarning = "logical"
),
prototype = prototype
(
classification = FALSE,
multiclassType = character(0),
featureWeights = FALSE,
selMethod = character(0),
onlyDense = FALSE,
sparse = FALSE,
runtimeWarning = TRUE
),
)
#' @rdname CrossValidationResult-class
#' @name CrossValidationResult-class
#' @aliases
#' class:CrossValidationResult
#' CrossValidationResult-class
#' CrossValidationResult
#' @title Cross Validation Result Class
#'
#' @details
#' Instances of this class store the result of cross validation.
#'
#' @slot cross number of folds for cross validation
#' @slot noCross number of CV runs
#' @slot groupBy group assignment of samples
#' @slot perfParameters collected performance parameters
#' @slot outerCV flag indicating outer CV
#' @slot folds folds used in CV
#' @slot cvError cross validation error
#' @slot foldErrors fold errors
#' @slot noSV number of support vectors
#' @slot ACC cross validation accuracy
#' @slot BACC cross validation balanced accuracy
#' @slot MCC cross validation Matthews correlation coefficient
#' @slot AUC cross validation area under the ROC curve
#' @slot foldACC fold accuracy
#' @slot foldBACC fold balanced accuracy
#' @slot foldMCC fold Matthews correlation coefficient
#' @slot foldAUC fold area under the ROC curve
#' @slot sumAlphas sum of alphas
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("CrossValidationResult",
representation = representation
(
cross = "numeric",
noCross = "numeric",
groupBy = "ANY",
perfParameters = "character",
outerCV = "logical",
folds = "list",
cvError = "numeric",
foldErrors = "matrix",
ACC = "numeric",
BACC = "numeric",
MCC = "numeric",
AUC = "numeric",
foldACC = "matrix",
foldBACC = "matrix",
foldMCC = "matrix",
foldAUC = "matrix",
noSV = "numeric",
sumAlphas = "numeric"
),
prototype = prototype
(
cross = numeric(0),
noCross = numeric(0),
groupBy = NULL,
perfParameters = character(0),
outerCV = FALSE,
folds = list(),
cvError = numeric(0),
foldErrors = matrix(0),
ACC = numeric(0),
BACC = numeric(0),
MCC = numeric(0),
AUC = numeric(0),
foldACC = matrix(0),
foldBACC = matrix(0),
foldMCC = matrix(0),
foldAUC = matrix(0),
noSV = numeric(0),
sumAlphas = numeric(0)
),
)
#' @rdname ModelSelectionResult-class
#' @name ModelSelectionResult-class
#' @aliases
#' class:ModelSelectionResult
#' ModelSelectionResult-class
#' ModelSelectionResult
#' @title Model Selection Result Class
#'
#' @details
#' Instances of this class store the result of grid search or
#' model selection.
#'
#' @slot cross number of folds for cross validation
#' @slot noCross number of CV runs
#' @slot groupBy group assignment of samples
#' @slot nestedCross number of folds for outer CV
#' @slot noNestedCross number of runs of outer CV
#' @slot perfParameters collected performance parameters
#' @slot perfObjective performance criterion for grid search / model selection
#' @slot gridRows rows in grid search (i.e. kernels)
#' @slot gridCols columns in grid search
#' @slot gridErrors grid errors
#' @slot gridACC grid accuracy
#' @slot gridBACC grid balanced accuracy
#' @slot gridMCC grid Matthews correlation coefficient
#' @slot gridAUC grid area under the ROC curve
#' @slot gridNoSV grid number of support vectors
#' @slot gridSumAlphas grid sum of alphas
#' @slot smallestCVError smallest CV error
#' @slot selGridRow grid row of best result
#' @slot selGridCol grid col of best result
#' @slot fullModel full model for best result
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("ModelSelectionResult",
representation = representation
(
cross = "numeric",
noCross = "numeric",
groupBy = "ANY",
nestedCross = "numeric",
noNestedCross = "numeric",
perfParameters = "character",
perfObjective = "character",
gridRows = "list",
gridCols = "ANY",
gridErrors = "ANY",
gridACC = "ANY",
gridBACC = "ANY",
gridMCC = "ANY",
gridAUC = "ANY",
gridNoSV = "ANY",
gridSumAlphas = "ANY",
smallestCVError = "numeric",
selGridRow = "ANY",
selGridCol = "ANY",
fullModel = "ANY"
),
prototype = prototype
(
cross = numeric(0),
noCross = numeric(0),
groupBy = NULL,
nestedCross = numeric(0),
noNestedCross = numeric(0),
perfParameters = character(0),
perfObjective = character(0),
gridRows = list(),
gridCols = NULL,
gridErrors = NULL,
gridACC = NULL,
gridBACC = NULL,
gridMCC = NULL,
gridAUC = NULL,
gridNoSV = NULL,
gridSumAlphas = NULL,
smallestCVError = Inf,
selGridRow = NULL,
selGridCol = NULL,
fullModel = NULL
),
)
#' @rdname KBModel-class
#' @name KBModel-class
#' @aliases
#' class:KBModel
#' KBModel-class
#' KBModel
#' @title KeBABS Model Class
#'
#' @details
#' Instances of this class represent a model object for the KeBABS
#' meta-SVM.
#'
#' @slot call invocation string of KeBABS meta-SVM
#' @slot numSequences number of sequences used for training
#' @slot sel index subset of samples used for training
#' @slot y vector of target values
#' @slot levels levels of target
#' @slot numClasses number of classes
#' @slot classNames class labels
#' @slot classWeights class weights
#' @slot SV support vectors
#' @slot svIndex support vector indices
#' @slot alphaIndex list of SVM indices per SVM
#' @slot trainingFeatures feature names used in training
#' @slot featureWeights feature Weights
#' @slot b model offset
#' @slot probA fitted logistic function parameter A
#' @slot probB fitted logistic function parameter A
#' @slot sigma scale of Laplacian fitted to regression residuals
#' @slot cvResult cross validation result of class
#' \code{\linkS4class{CrossValidationResult}}
#' @slot modelSelResult model selection / grid search result of class
#' \code{\linkS4class{ModelSelectionResult}}
#' @slot ctlInfo KeBABS control info of class
#' \code{\linkS4class{ControlInformation}}
#' @slot svmInfo info about requested / used SVM of class
#' \code{\linkS4class{SVMInformation}}
#' @slot svmModel original model returned from SVM
#'
#' @author Johannes Palme <kebabs@@bioinf.jku.at>
#' @references
#' \url{http://www.bioinf.jku.at/software/kebabs}\cr\cr
#' J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
#' for kernel-based analysis of biological sequences.
#' \emph{Bioinformatics}, 31(15):2574-2576, 2015.
#' DOI: \href{http://dx.doi.org/10.1093/bioinformatics/btv176}{10.1093/bioinformatics/btv176}.
setClass("KBModel",
representation = representation
(
call = "character", # invocation string
numSequences = "numeric", # number of sequences
sel = "numeric", # selected subset of sequences
y = "ANY", # target values
levels = "ANY", # levels of target
numClasses = "numeric", # number of classes
classNames = "ANY", # class labels
classWeights = "numeric", # class weights
SV = "ANY", # support vectors
svIndex = "ANY", # support vector indices
alphaIndex = "ANY", # list of SV indices per SVM
trainingFeatures = "character_OR_NULL", # feature names of training
featureWeights = "ANY", # feature weights
b = "numeric", # b (bias)
probA = "numeric", # fitted logistic function param A
probB = "numeric", # fitted logistic function param B
sigma = "numeric", # for regression only - scale of
# laplacian fitted to residuals
cvResult = "CrossValidationResult", # cross validation result
modelSelResult = "ANY", # model selection result
ctlInfo = "ControlInformation", # kb control info
svmInfo = "SVMInformation", # info about requested/used svm
svmModel = "ANY" # model object of ksvm, LiblineaR,
# libsvm via e1071, psvm,
# rvm, lssvm, penalizedSVM
),
prototype = prototype
(
call = character(0),
numSequences = 0,
sel = NULL,
y = NULL,
levels = NULL,
numClasses = 0,
classNames = NULL,
classWeights = numeric(0),
SV = NULL,
svIndex = numeric(0),
alphaIndex = numeric(0),
trainingFeatures = character(0),
featureWeights = NULL,
b = 0,
probA = as.numeric(NA),
probB = as.numeric(NA),
sigma = as.numeric(NA),
cvResult = NULL,
modelSelResult = NULL,
ctlInfo = NULL,
svmInfo = NULL,
svmModel = NULL
),
)
setClass("KebabsInfo",
representation=representation
(
supportedPkgs = "character",
allowedSeqClasses = "character",
allowedSeqSetClasses = "character",
allowedSeqTypes = "numeric",
classifierMap = "matrix",
svmParMap = "matrix",
modelParMap = "matrix",
kebabsDebug = "logical"
),
prototype=prototype
(
supportedPkgs = character(0),
allowedSeqClasses = character(0),
allowedSeqSetClasses = character(0),
allowedSeqTypes = numeric(0),
classifierMap = matrix(0),
svmParMap = matrix(0),
modelParMap = matrix(0),
kebabsDebug = FALSE
),
)
Try the kebabs package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.