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

## Documentation for package ‘hddplot’ version 0.57-2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Code demos. Use demo() to run them.

## Help Pages

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