Compositional (version 5.5)

ICE plot for projection pursuit regression with compositional predictor variables: ICE plot for projection pursuit regression with compositional predictor variables

Description

ICE plot for projection pursuit regression with compositional predictor variables.

Usage

ice.pprcomp(model, x, k = 1, frac = 0.1, type = "log")

Arguments

model

The ppr model, the outcome of the pprcomp function.

x

A matrix with the compositional data. No zero values are allowed.

k

Which variable to select?.

frac

Fraction of observations to use. The default value is 0.1.

type

Either "alr" or "log" corresponding to the additive log-ratio transformation or the simple logarithm applied to the compositional data.

Value

A graph with several curves. The horizontal axis contains the selected variable, whereas the vertical axis contains the centered predicted values. The black curves are the effects for each observation and the blue line is their average effect.

Details

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

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.

Friedman, J. H. and Stuetzle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823. doi: 10.2307/2287576.

See Also

pprcomp, pprcomp.tune, ice.kernreg, alfa.pcr, lc.reg, comp.ppr

Examples

# NOT RUN {
x <- as.matrix( iris[, 2:4] )
x <- x/ rowSums(x)
y <- iris[, 1]
model <- pprcomp(y, x)
ice <- ice.pprcomp(model, x, k = 1)
# }