Package: hddplot 0.59-1

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

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 are intended for didactic use. The package implements, and extends, methods described in J.H. Maindonald and C.J. Burden (2005) <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.

Authors:John Maindonald

hddplot_0.59-1.tar.gz
hddplot_0.59-1.zip(r-4.5)hddplot_0.59-1.zip(r-4.4)hddplot_0.59-1.zip(r-4.3)
hddplot_0.59-1.tgz(r-4.4-any)hddplot_0.59-1.tgz(r-4.3-any)
hddplot_0.59-1.tar.gz(r-4.5-noble)hddplot_0.59-1.tar.gz(r-4.4-noble)
hddplot_0.59-1.tgz(r-4.4-emscripten)hddplot_0.59-1.tgz(r-4.3-emscripten)
hddplot.pdf |hddplot.html✨
hddplot/json (API)

# Install 'hddplot' in R:

install.packages('hddplot', repos = c('https://jhmaindonald.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jhmaindonald/hddplot/issues

Datasets:

Golub - Golub data (7129 rows by 72 columns), after normalization
golubInfo - Classifying factors for the 72 columns of the Golub data set

On CRAN:

3.00 score 10 scripts 250 downloads 12 exports 8 dependencies

Last updated 1 years agofrom:d42bcfb84c. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 01 2024
R-4.5-win	OK	Nov 01 2024
R-4.5-linux	OK	Nov 01 2024
R-4.4-win	OK	Nov 01 2024
R-4.4-mac	OK	Nov 01 2024
R-4.3-win	OK	Nov 01 2024
R-4.3-mac	OK	Nov 01 2024

Exports:accTrainTest aovFbyrow cvdisc cvscores defectiveCVdisc divideUp orderFeatures pcp plotTrainTest qqthin scoreplot simulateScores

Dependencies:Biobase BiocGenerics generics lattice MASS Matrix multtest survival

Feature Selection Bias in Classification of High Dimensional Data

Rendered fromQUICKhddplot.Rnwusingknitr::knitron Nov 01 2024.

Last update: 2023-09-13
Started: 2023-09-13

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

Package: hddplot 0.59-1

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

Feature Selection Bias in Classification of High Dimensional Data

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)