Linear Programming Discriminant Analysis

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Documentation for package ‘lpda’ version 1.0.1

Help Pages

bestPC Choosing the best number of Principal Components (PCs) for lpda-pca model.
bestVariability Choosing the best explained variability for lpda-pca model.
CVktest CVktest evaluates the error rate classification with crossvalidation
CVloo CVloo evaluates the error rate classification with leave one out procedure
lpda Computing discriminating hyperplane for two groups
lpda.fit lpda.fit computes the discriminating hyperplane for two groups
lpda.pca lpda.pca computes a PCA to the original data and selects the desired PCs when Variability is supplied
lpdaCV lpdaCV evaluates the error rate classification with a crossvalidation procedure
palmdates Spectrometry and composition chemical of Spanish and Arabian palm dates
PCA Principal Component Analysis
plot.lpda Plot method for lpda classification
predict.lpda Predict method for lpda classification
RNAseq Simulated RNA-Seq dataset example
stand stand center and scale a data matrix
stand2 stand2 center and scale a data matrix with the parameters of another one