Biocomb: Feature Selection and Classification with the Embedded Validation Procedures for Biomedical Data Analysis
Version 0.3

Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification algorithms with the embedded validation procedures. The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves. Provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves (RCC) to estimate the classifier performance under unequal misclassification costs problem. There exists the special function to deal with missing values, including different imputing schemes.

Browse man pages Browse package API and functions Browse package files

AuthorNatalia Novoselova,Junxi Wang,Frank Pessler,Frank Klawonn
Date of publication2017-03-22 16:29:07 UTC
MaintainerNatalia Novoselova <novos65@mail.ru>
LicenseGPL (>= 3)
Version0.3
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("Biocomb")

Man pages

Biocomb-package: Tools for Data Mining
CalcGene: Calculate HUM value
CalcROC: Calculate ROC points
Calculate3D: Plot the 3D-ROC curve
CalculateHUM_Ex: Calculate HUM value
CalculateHUM_Plot: Plot 2D-ROC curve
CalculateHUM_ROC: Compute the points for ROC curve
CalculateHUM_seq: Calculate HUM value
chi2.algorithm: Select the subset of features
classifier.loop: Classification and classifier validation
compute.auc.permutation: Calculates the p-values
compute.auc.random: Calculates the p-values
compute.aucs: Ranks the features
cost.curve: Plots the RCC curve for two-class problem
datasetF6: simulated data
data_test: simulated data
generate.data.miss: Generate the dataset with missing values
input_miss: Process the dataset with missing values
leukemia72: desease data
leukemia72_2: desease data
leukemia_miss: desease data
pauc: Calculates the p-values
pauclog: Calculates the p-values
plotClass.result: Plots the results of classifier validation schemes
plotRoc.curves: Plots the ROC curve for two-class problem
ProcessData: Select the subset of features
select.cfs: Select the subset of features
select.fast.filter: Select the subset of features
select.forward.Corr: Select the subset of features
select.forward.wrapper: Select the subset of features
select.inf.chi2: Ranks the features
select.inf.gain: Ranks the features
select.inf.symm: Ranks the features
select.process: Feature ranking and feature selection
select.relief: Ranks the features

Functions

Biocomb Man page
Biocomb-package Man page
CalcDist Source code
CalcGain Source code
CalcGene Man page Source code
CalcROC Man page Source code
Calculate3D Man page Source code
CalculateHUM_Ex Man page Source code
CalculateHUM_Plot Man page Source code
CalculateHUM_ROC Man page Source code
CalculateHUM_seq Man page Source code
ProcessData Man page Source code
ProcessData1 Source code
Sub.filename Source code
aac.value Source code
bh.correction Source code
char.to.numeric Source code
check_incons Source code
chi2.algorithm Man page Source code
classifier.loop Man page Source code
compute.auc.permutation Man page Source code
compute.auc.random Man page Source code
compute.aucs Man page Source code
compute.confusion.matrix Source code
compute.permutation.table Source code
cost.curve Man page Source code
datRCC Source code
data_test Man page
datasetF6 Man page
do.numeric Source code
forward_path Source code
fun1_chi Source code
fun2_chi Source code
fun3_chi Source code
fun4_chi Source code
general.fun Source code
generate.data.miss Man page Source code
input_miss Man page Source code
leukemia72 Man page
leukemia72_2 Man page
leukemia_miss Man page
multi.random.aucs.lab Source code
pareto.front Source code
pauc Man page Source code
pauclog Man page Source code
plotClass.result Man page Source code
plotRoc.curves Man page Source code
select.cfs Man page Source code
select.fast.filter Man page Source code
select.forward.Corr Man page Source code
select.forward.wrapper Man page Source code
select.inf.chi2 Man page Source code
select.inf.gain Man page Source code
select.inf.symm Man page Source code
select.process Man page Source code
select.relief Man page Source code
wrongly.classified Source code Source code

Files

src
src/CorrF.cpp
src/CHI2.cpp
src/Biocomb_init.c
src/HUMseq.cpp
src/RcppExports.cpp
NAMESPACE
data
data/datasetF6.txt.gz
data/leukemia72.txt.gz
data/leukemia_miss.txt.gz
data/data_test.txt.gz
data/leukemia72_2.txt.gz
R
R/select.feature.info.R
R/plotClass.result.R
R/class_cross.R
R/ROC_Plot.R
R/Sub.filename.R
R/CalculateHUM_Ex.R
R/input_miss.R
R/RcppExports.R
R/CalculateHUM_ROC.R
R/CalculateHUM_seq.R
R/plotRoc.curves.R
R/compute.auc.pvalues.R
R/pauc.R
R/char.to.numeric.R
R/rcc.aac.R
MD5
DESCRIPTION
man
man/datasetF6.Rd
man/compute.auc.permutation.Rd
man/select.forward.Corr.Rd
man/select.inf.symm.Rd
man/select.inf.gain.Rd
man/CalcROC.Rd
man/pauclog.Rd
man/Calculate3D.Rd
man/select.cfs.Rd
man/generate.data.miss.Rd
man/input_miss.Rd
man/ProcessData.Rd
man/leukemia72_2.Rd
man/leukemia72.Rd
man/select.inf.chi2.Rd
man/chi2.algorithm.Rd
man/data_test.Rd
man/CalculateHUM_Ex.Rd
man/select.forward.wrapper.Rd
man/plotRoc.curves.Rd
man/select.fast.filter.Rd
man/CalculateHUM_seq.Rd
man/leukemia_miss.Rd
man/CalculateHUM_ROC.Rd
man/select.process.Rd
man/compute.auc.random.Rd
man/plotClass.result.Rd
man/pauc.Rd
man/select.relief.Rd
man/classifier.loop.Rd
man/cost.curve.Rd
man/CalculateHUM_Plot.Rd
man/Biocomb-package.Rd
man/CalcGene.Rd
man/compute.aucs.Rd
Biocomb documentation built on May 29, 2017, 6:11 p.m.