ice.aknnreg: ICE plot for the alpha-k-NN regression

In Compositional: Compositional Data Analysis

ICE plot for the \alpha-k-NN regression

Description

ICE plot for the \alpha-k-NN regression.

Usage

ice.aknnreg(y, x, a, k, apostasi = "euclidean", rann = FALSE,
ind = 1, frac = 0.2, qpos = 0.9)

Arguments

y	A numerical vector with the response values.
x	A numerical matrix with the predictor variables.
a	The value \alpha to consider.
k	The number of nearest neighbours to consider.
apostasi	The type of distance to use, either "euclidean" or "manhattan".
rann	If you have large scale datasets and want a faster k-NN search, you can use kd-trees implemented in the R package "RANN". In this case you must set this argument equal to TRUE. Note however, that in this case, the only available distance is by default "euclidean".
ind	Which variable to select?.
frac	Fraction of observations to use. The default value is 0.1.
qpos	A number between 0.8 and 1. This is used to place the legend of the figure better. You can play with it. In the worst case scenario the code is open and you tweak this argument as you prefer.

Details

This function implements the Individual Conditional Expecation plots of Goldstein et al. (2015). See the references for more details.

Value

A graph with several curves, one for each component. The horizontal axis contains the selected variable, whereas the vertical axis contains the locally smoothed predicted compositional lines.

Author(s)

Michail Tsagris.

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

References

https://christophm.github.io/interpretable-ml-book/ice.html

Goldstein, A., Kapelner, A., Bleich, J. and Pitkin, E. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics 24(1): 44-65.

See Also

ice.akernreg, ice.pprcomp

Examples

y <- as.matrix( iris[, 2:4] )
x <- iris[, 1]
ice <- ice.aknnreg(y, x, a = 0.6, k = 5, ind = 1)

Compositional documentation built on Oct. 23, 2023, 5:09 p.m.