hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots
Version 0.57-2

Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included that serve didactic purposes.

AuthorJohn Maindonald
Date of publication2016-11-03 23:09:33
MaintainerJohn Maindonald <john.maindonald@anu.edu.au>
LicenseGPL (>= 2)
Version0.57-2
URL http://www.maths.anu.edu.au/~johnm
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("hddplot")

Popular man pages

aovFbyrow: calculate aov F-statistic for each row of a matrix
cvdisc: Cross-validated accuracy, in linear discriminant calculations
cvscores: For high-dimensional data with known groups, derive scores...
Golub: Golub data (7129 rows by 72 columns), after normalization
golubInfo: Classifying factors for the 72 columns of the Golub data set
hddplot.package: For high-dimensional data with known groups, derive scores...
orderFeatures: Order features, based on their ability to discriminate
See all...

All man pages Function index File listing

Man pages

accTrainTest: Two subsets of data each take in turn the role of test set
aovFbyrow: calculate aov F-statistic for each row of a matrix
cvdisc: Cross-validated accuracy, in linear discriminant calculations
cvscores: For high-dimensional data with known groups, derive scores...
defectiveCVdisc: defective accuracy assessments from linear discriminant...
divideUp: Partition data into mutiple nearly equal subsets
Golub: Golub data (7129 rows by 72 columns), after normalization
golubInfo: Classifying factors for the 72 columns of the Golub data set
hddplot.package: For high-dimensional data with known groups, derive scores...
orderFeatures: Order features, based on their ability to discriminate
pcp: convenience version of the singular value decomposition
plotTrainTest: Plot predictions for both a I/II train/test split, and the...
qqthin: a version of qqplot() that thins out points that overplot
scoreplot: Plot discriminant function scores, with various...
simulateScores: Generate linear discriminant scores from random data, after...

Functions

Golub Man page
accTrainTest Man page
aovFbyrow Man page
cvdisc Man page
cvscores Man page
defectiveCVdisc Man page
divideUp Man page
golubInfo Man page
hddplot Man page
hddplot-package Man page
orderFeatures Man page
pcp Man page
plotTrainTest Man page
qqthin Man page
scoreplot Man page Source code
simulateScores Man page Source code

Files

inst
inst/CITATION
inst/doc
inst/doc/QUICKhddplot.pdf
inst/doc/QUICKhddplot.R
inst/doc/hddplot.pdf
inst/doc/QUICKhddplot.Rnw
NAMESPACE
demo
demo/classifyRandom.R
demo/00Index
demo/biasedPlots.R
demo/CVscoreplot.R
data
data/golubInfo.rda
data/Golub.rda
data/datalist
R
R/aovFbyrow.R
R/cvdisc.R
R/defectiveCVdisc.R
R/qqthin.R
R/scoreplot.R
R/orderFeatures.R
R/plotTrainTest.R
R/divideUp.R
R/simulateScores.R
R/pcp.R
R/accTrainTest.R
R/cvscores.R
vignettes
vignettes/QUICKhddplot.Rnw
MD5
README
build
build/vignette.rds
DESCRIPTION
man
man/golubInfo.Rd
man/orderFeatures.Rd
man/cvdisc.Rd
man/pcp.Rd
man/defectiveCVdisc.Rd
man/simulateScores.Rd
man/Golub.Rd
man/hddplot.package.Rd
man/scoreplot.Rd
man/plotTrainTest.Rd
man/aovFbyrow.Rd
man/cvscores.Rd
man/accTrainTest.Rd
man/divideUp.Rd
man/qqthin.Rd
hddplot documentation built on May 20, 2017, 12:55 a.m.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.