Directional (version 6.8)

k-NN regression: k-NN regression with Euclidean or (hyper-)spherical response and or predictor variables

Description

k-NN regression with Euclidean or (hyper-)spherical response and or predictor variables.

Usage

knn.reg(xnew, y, x, k = 5, res = "eucl", estim = "arithmetic")

Value

A list with as many elements as the number of values of k. Each element in the list contains a matrix (or a vector in the case of Euclidean data) with the predicted response values.

Arguments

xnew

The new data, new predictor variables values. A matrix with either euclidean (univariate or multivariate) or (hyper-)spherical data. If you have a circular response, say u, transform it to a unit vector via (cos(u), sin(u)). If xnew = x, you will get the fitted values.

y

The currently available data, the response variables values. A matrix with either euclidean (univariate or multivariate) or (hyper-)spherical data. If you have a circular response, say u, transform it to a unit vector via (cos(u), sin(u)).

x

The currently available data, the predictor variables values. A matrix with either euclidean (univariate or multivariate) or (hyper-)spherical data. If you have a circular response, say u, transform it to a unit vector via (cos(u), sin(u)).

k

The number of nearest neighbours, set to 5 by default. This can also be a vector with many values.

res

The type of the response variable. If it is Euclidean, set this argument equal to "res". If it is a unit vector set it to res="spher".

estim

Once the k observations whith the smallest distance are discovered, what should the prediction be? The arithmetic average of the corresponding y values be used estim="arithmetic" or their harmonic average estim="harmonic".

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

This function covers a broad range of data, Euclidean and spherical, along with their combinations.

See Also

knnreg.tune, spher.reg, spml.reg

Examples

y <- iris[, 1]
x <- as.matrix(iris[, 2:4])
x <- x/ sqrt( rowSums(x^2) )  ## Euclidean response
a <- knn.reg(x, y, x, k = 5, res = "eucl", estim = "arithmetic")

y <- iris[, 2:4]
y <- y / sqrt( rowSums(y^2) ) ## Spherical response
x <- iris[, 1]
a <- knn.reg(x, y, x, k = 5, res = "spher", estim = "arithmetic")