Usage
rbyb(p, m, eta) rbyp(p, m, eta) rbyv(p, m, nu) rbyz(p, m) rbyz.sim(p, m, nsim=1000) rbyz.geo(p, m=floor(sqrt(p)), rmax=p) rbylambda(p, m, lambda=1) knn(train, test, cl, k=1) knn.cv (train, cl, k=1) knn.reg(train, test = NULL, y, k = 3) pressresid(obj)
Arguments
m
Number of elements in a subset to be drawn.
p
Total number of available features.
mtry
Number of features to be drawn for each KNN.
nu
mean mutiplicity of a feature
rmax
number of series terms for independent geometric approximation
nsim
number of simulations for geometric simulation.
lambda
mean number of silient features.
samples
A vector of indice for a set of observations.
cl
A factor for classification labels.
k
Number of nearest neighbors.
cl
A vector of class labels.
K
Number of folds for cross-validation.
pk
A real number between 0 and to indicate the proportion of the feature set to be kept in each step.
r
Number of KNN to be generated.
criterion
either uses mean_accuracy or mean_support for best.