mgcViz (version 0.2.1)

plot.ALE1D: Plot 1D Accumulated Local Effects (ALE)

Description

Plot 1D Accumulated Local Effects (ALE)

Usage

# S3 method for ALE1D
plot(x, trans = identity, maxpo = 10000, nsim = 0, ...)

Value

An objects of class plotSmooth.

Arguments

x

a 1D ALE effects, produced by the ALE function

trans

monotonic function to apply to the ALE effect, before plotting. Monotonicity is not checked.

maxpo

maximum number of rug lines that will be used by l_rug. If number of datapoints > maxpo, then a subsample of maxpo points will be taken.

nsim

number of ALE effect curves to be simulated from the posterior distribution. These can be plotted using the l_simLine layer. See Examples section below.

...

currently not used.

Author

Matteo Fasiolo and Christian Capezza, with some internal code having been adapted from the ALEPlot package of Dan Apley.

References

Apley, D.W., and Zhu, J, 2016. Visualizing the effects of predictor variables in black box supervised learning models. arXiv preprint arXiv:1612.08468.

Examples

library(mgcViz)
# Here x1 and x2 are very correlated, but only 
# x1 has influence of the response
set.seed(4141)
n <- 1000
X <- rmvn(n, c(0, 0), matrix(c(1, 0.9, 0.9, 1), 2, 2))
y <- X[ , 1] + 0.2 * X[ , 1]^2 + rnorm(n, 0, 0.8)
dat <- data.frame(y = y, x1 = X[ , 1], x2 = X[ , 2])
fit <- gam(y ~ te(x1, x2), data = dat)

# Marginal plot suggests that E(y) depends on x2, but
# this is due to the correlation between x1 and x2...
plot(dat$x2, fit$fitted.values)

# ... in fact ALE effect of x2 is flat ...
plot(ALE(fit, "x2")) + l_ciPoly() + l_fitLine() + l_rug()

# ... while ALE effect of x1 is strong.
plot(ALE(fit, "x1", center = 2), nsim = 20) + 
  l_simLine() + l_fitLine()