Use known groups in high-dimensional data to derive scores for plots

Documentation for package ‘hddplot’ version 0.52

hddplot-package	For high-dimensional data with known groups, derive scores for plotting
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 for plotting
defectiveCVdisc	defective accuracy assessments from linear discriminant calculations
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	For high-dimensional data with known groups, derive scores for plotting
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 reverse
qqthin	a version of qqplot() that thins out points that overplot
scoreplot	Plot discriminant function scores, with various identification
simulateScores	Generate linear discriminant scores from random data, after selection