R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Linear Kernel

linearKernel

R Documentation

Linear Kernel

Description

Create a dense or sparse kernel matrix from an explicit representation

Usage

linearKernel(x, y = NULL, selx = integer(0), sely = integer(0),
  sparse = FALSE, triangular = TRUE, diag = TRUE, lowerLimit = 0)

Arguments

`x`	a dense or sparse explicit representation. `x` must be a sparse explicit representation when a sparse kernel matrix should be returned by the function (see parameter `sparse`).
`y`	a dense or sparse explicit representation. If `x` is dense, `y` must be dense. If `x` is sparse, `y` must be sparse.
`selx`	a numeric or character vector for defining a subset of `x`. Default=integer(0)
`sely`	a numeric or character vector for defining a subset of `y`. Default=integer(0)
`sparse`	boolean indicating whether returned kernel matrix should be sparse or dense. For value `FALSE` a dense kernel matrix of class `KernelMatrix` is returned. If set to `TRUE` the kernel matrix is returned as sparse matrix of class `dgCMatrix`. In case of a symmetric matrix either the lower triangular part or the full matrix can be returned. Please note that a sparse kernel matrix currently can not be used for SVM based learning in `kebabs`. Default=FALSE
`triangular`	boolean indicating whether just the lower triangular or the full sparse matrix should be returned. This parameter is only relevant for a sparse symmetric kernel matrix. Default=TRUE
`diag`	boolean indicating whether the diagonal should be included in a sparse triangular matrix. This parameter is only relevant when parameter `sparse` and `triangular` are set to `TRUE`. Default=TRUE
`lowerLimit`	a numeric value for a similarity threshold. The parameter is relevant for sparse kernel matrices only. If set to a value larger than 0 only similarity values larger than this threshold will be included in the sparse kernel matrix. Default=0

Value

linearKernel: kernel matrix as class KernelMatrix or sparse kernel matrix of class dgCMatrix dependent on parameter sparse

Author(s)

Johannes Palme <kebabs@bioinf.jku.at>

References

http://www.bioinf.jku.at/software/kebabs

J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics, 31(15):2574-2576, 2015. DOI: 10.1093/bioinformatics/btv176.

Examples

## load sequence data and change sample names
data(TFBS)
names(enhancerFB) <- paste("S", 1:length(enhancerFB), sep="_")

## create the kernel object for dimers with normalization
speck <- spectrumKernel(k=5)

## generate sparse explicit representation
ers <- getExRep(enhancerFB, speck)

## compute dense kernel matrix (as currently used in SVM based learning)
km <- linearKernel(ers)
km[1:5, 1:5]

## compute sparse kernel matrix
## because it is symmetric just the lower diagonal
## is computed to save storage
km <- linearKernel(ers, sparse=TRUE)
km[1:5, 1:5]

## compute full sparse kernel matrix
km <- linearKernel(ers, sparse=TRUE, triangular=FALSE)
km[1:5, 1:5]

## compute triangular sparse kernel matrix without diagonal
km <- linearKernel(ers, sparse=TRUE, triangular=TRUE, diag=FALSE)
km[1:5, 1:5]

## plot histogram of similarity values
hist(as(km, "numeric"), breaks=30)

## compute sparse kernel matrix with similarities above 0.5 only
km <- linearKernel(ers, sparse=TRUE, lowerLimit=0.5)
km[1:5, 1:5]

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(kebabs)
Loading required package: Biostrings
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
Loading required package: kernlab

Attaching package: 'kernlab'

The following object is masked from 'package:Biostrings':

    type

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/kebabs/LinearKernel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: linearKernel
> ### Title: Linear Kernel
> ### Aliases: linearKernel
> ### Keywords: kernel, linearKernel
> 
> ### ** Examples
> 
> ## load sequence data and change sample names
> data(TFBS)
> names(enhancerFB) <- paste("S", 1:length(enhancerFB), sep="_")
> 
> ## create the kernel object for dimers with normalization
> speck <- spectrumKernel(k=5)
> 
> ## generate sparse explicit representation
> ers <- getExRep(enhancerFB, speck)
> 
> ## compute dense kernel matrix (as currently used in SVM based learning)
> km <- linearKernel(ers)
> km[1:5, 1:5]
An object of class "KernelMatrix"
          S_1       S_2       S_3       S_4       S_5
S_1 1.0000000 0.4412064 0.4766687 0.5432537 0.5484077
S_2 0.4412064 1.0000000 0.4611439 0.5275230 0.4248695
S_3 0.4766687 0.4611439 1.0000000 0.5359756 0.4403322
S_4 0.5432537 0.5275230 0.5359756 1.0000000 0.5575523
S_5 0.5484077 0.4248695 0.4403322 0.5575523 1.0000000
> 
> ## compute sparse kernel matrix
> ## because it is symmetric just the lower diagonal
> ## is computed to save storage
> km <- linearKernel(ers, sparse=TRUE)
> km[1:5, 1:5]
5 x 5 sparse Matrix of class "dgCMatrix"
          S_1       S_2       S_3       S_4 S_5
S_1 1.0000000 .         .         .           .
S_2 0.4412064 1.0000000 .         .           .
S_3 0.4766687 0.4611439 1.0000000 .           .
S_4 0.5432537 0.5275230 0.5359756 1.0000000   .
S_5 0.5484077 0.4248695 0.4403322 0.5575523   1
> 
> ## compute full sparse kernel matrix
> km <- linearKernel(ers, sparse=TRUE, triangular=FALSE)
> km[1:5, 1:5]
5 x 5 sparse Matrix of class "dgCMatrix"
          S_1       S_2       S_3       S_4       S_5
S_1 1.0000000 0.4412064 0.4766687 0.5432537 0.5484077
S_2 0.4412064 1.0000000 0.4611439 0.5275230 0.4248695
S_3 0.4766687 0.4611439 1.0000000 0.5359756 0.4403322
S_4 0.5432537 0.5275230 0.5359756 1.0000000 0.5575523
S_5 0.5484077 0.4248695 0.4403322 0.5575523 1.0000000
> 
> ## compute triangular sparse kernel matrix without diagonal
> km <- linearKernel(ers, sparse=TRUE, triangular=TRUE, diag=FALSE)
> km[1:5, 1:5]
5 x 5 sparse Matrix of class "dgCMatrix"
          S_1       S_2       S_3       S_4 S_5
S_1 .         .         .         .           .
S_2 0.4412064 .         .         .           .
S_3 0.4766687 0.4611439 .         .           .
S_4 0.5432537 0.5275230 0.5359756 .           .
S_5 0.5484077 0.4248695 0.4403322 0.5575523   .
> 
> ## plot histogram of similarity values
> hist(as(km, "numeric"), breaks=30)
> 
> ## compute sparse kernel matrix with similarities above 0.5 only
> km <- linearKernel(ers, sparse=TRUE, lowerLimit=0.5)
> km[1:5, 1:5]
5 x 5 sparse Matrix of class "dgCMatrix"
          S_1      S_2       S_3       S_4 S_5
S_1 1.0000000 .        .         .           .
S_2 .         1.000000 .         .           .
S_3 .         .        1.0000000 .           .
S_4 0.5432537 0.527523 0.5359756 1.0000000   .
S_5 0.5484077 .        .         0.5575523   1
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>