ICE plot for univariate kernel regression: ICE plot for univariate kernel regression
Description
ICE plot for univariate kernel regression.
Usage
ice.kernreg(y, x, h, type = "gauss", k = 1, frac = 0.1)
Arguments
y
A numerical vector with the response values.
x
A numerical matrix with the predictor variables.
h
The bandwidth value to consider.
type
The type of kernel to use, "gauss" or "laplace".
k
Which variable to select?.
frac
Fraction of observations to use. The default value is 0.1.
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.
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.