internal functions {rknn} | R Documentation |
Some internal and under-development functions
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)
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. |
eta |
Coverage Probability. |
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. |
train |
A data matrix. |
test |
A data matrix. |
y |
A vector of responses. |
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. |
seed |
An integer seed. |
criterion |
either uses mean_accuracy or mean_support for best. |
obj |
A linear model. |
Shengqiao Li<lishengqiao@yahoo.com>