knnreg.tune: Tuning of the k-NN regression with Euclidean or...

In Directional: A Collection of Functions for Directional Data Analysis

Tuning of the k-NN regression with Euclidean or (hyper-)spherical response and or predictor variables

Description

Tuning of the k-NN regression with Euclidean or (hyper-)spherical response and or predictor variables. It estimates the percentage of correct classification via an m-fold cross valdiation. The bias is estimated as well using the algorithm suggested by Tibshirani and Tibshirani (2009) and is subtracted.

Usage

knnreg.tune(y, x, nfolds = 10, A = 10, ncores = 1, res = "eucl",
estim = "arithmetic", folds = NULL, seed = NULL, graph = FALSE)

Arguments

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)).
nfolds	How many folds to create?
A	The maximum number of nearest neighbours, set to 10 by default, starting from the 2nd nearest neighbor.
ncores	How many cores to use. This is taken into account only when the predictor variables are spherical.
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".
folds	Do you already have a list with the folds? If not, leave this NULL.
seed	You can specify your own seed number here or leave it NULL.
graph	If this is TRUE a graph with the results will appear.

Details

Tuning of the k-NN regression with Euclidean or (hyper-)spherical response and or predictor variables. It estimates the percentage of correct classification via an m-fold cross valdiation. The bias is estimated as well using the algorithm suggested by Tibshirani and Tibshirani (2009) and is subtracted. The sum of squares of prediction is used in the case of Euclidean responses. In the case of spherical responses the \sum_i \hat{y}_i^Ty_i is calculated.

Value

A list including:

crit	The value of the criterion to minimise/maximise for all values of the nearest neighbours.
best_k	The best value of the nearest neighbours.
performance	The bias corrected optimal value of the criterion, along wit the estimated bias. For the case of Euclidean reponse this will be higher than the crit and for the case or spherical responses it will be lower than crit.
runtime	The run time of the algorithm. A numeric vector. The first element is the user time, the second element is the system time and the third element is the elapsed time.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

See Also

knn.reg, spher.reg, dirknn.tune

Examples

y <- iris[, 1]
x <- iris[, 2:4]
x <- x/ sqrt( rowSums(x^2) )  ## Euclidean response and spherical predictors
knnreg.tune(y, x, A = 5, res = "eucl", estim = "arithmetic")

y <- iris[, 1:3]
y <- y/ sqrt( rowSums(y^2) )  ## Spherical response and Euclidean predictor
x <- iris[, 2]
knnreg.tune(y, x, A = 5, res = "spher", estim = "arithmetic")

Directional documentation built on Oct. 30, 2024, 9:15 a.m.