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hddplot (version 0.56)

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 serve didactic purposes.

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Version

Install

install.packages('hddplot')

Monthly Downloads

34

Version

0.56

License

GPL (>= 2)

Maintainer

John Maindonald

Last Published

December 5th, 2013

Functions in hddplot (0.56)

Generate linear discriminant scores from random data, after selection

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

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

Partition data into mutiple nearly equal subsets

hddplot-package

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

Classifying factors for the 72 columns of the Golub data set

convenience version of the singular value decomposition

Cross-validated accuracy, in linear discriminant calculations

Order features, based on their ability to discriminate

calculate aov F-statistic for each row of a matrix

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

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

Plot discriminant function scores, with various identification

defectiveCVdisc

defective accuracy assessments from linear discriminant calculations

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