k-NN regression | R Documentation |

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

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

`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". |

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

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.

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

```
knnreg.tune, spher.reg, spml.reg
```

```
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")
```

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