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hddplot (version 0.59)

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

Description

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) .

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Version

Install

install.packages('hddplot')

Monthly Downloads

146

Version

0.59

License

GPL (>= 2)

Maintainer

John Maindonald

Last Published

June 15th, 2018

Functions in hddplot (0.59)

Golub data (7129 rows by 72 columns), after normalization

defectiveCVdisc

defective accuracy assessments from linear discriminant calculations

Two subsets of data each take in turn the role of test set

hddplot-package

tools:::Rd_package_title("hddplot")

Order features, based on their ability to discriminate

Classifying factors for the 72 columns of the Golub data set

Partition data into mutiple nearly equal subsets

calculate aov F-statistic for each row of a matrix

Cross-validated accuracy, in linear discriminant calculations

For high-dimensional data with known groups, derive scores for plotting

a version of qqplot() that thins out points that overplot

Plot predictions for both a I/II train/test split, and the reverse

convenience version of the singular value decomposition

Generate linear discriminant scores from random data, after selection

Plot discriminant function scores, with various identification