Estimates the partial derivative function for each curve in an ice object.
See Goldstein et al (2013) for further details.
Usage
dice(ice_obj, DerivEstimator)
Arguments
ice_obj
Object of class ice. This function generates partial derivative
estimates for each row in ice_obj$ice_curves.
DerivEstimator
Optional function with a single argument y. Returns the estimated
partial derivative of a function sampled at the points (ice_obj$gridpts,y).
If omitted, the default (a) smooths (ice_obj$gridpts,y) using
supsmu and then (b) uses the D1tr function ("discrete
first derivative using simple difference ratios") found in the sfsmisc
package to estimate the derivative.
Value
A list of class dice with the following elements. Most are passed directly through
from ice_object and exist to enable various plotting facilities.
d_ice_curves
Matrix of dimension nrow(Xice) by length(gridpts).
Each row corresponds to an observation's d-ICE curve, estimated at the values of predictor in gridpts.
xj
The actual values of predictor observed in the data in the order
of Xice.
actual_deriv
Vector of length nrow(Xice) containing the estimated partial derivatives
at the value of the predictor actually found in Xice.
sd_deriv
Vector of length length(gridpts) with the cross-observation sd of partial derivative
estimates. For instance sd_deriv[1] equals sd(d_ice_curves[,1]).
logodds
Passed from ice_object. If TRUE, d_ice_curves are
estimated derivatives of the centered log-odds.
gridpts
Passed from ice_object.
predictor
Passed from ice_object.
xlab
Passed from ice_object.
nominal_axis
Passed from ice_object.
range_y
Passed from ice_object.
Xice
Passed from ice_object.
dpdp
The estimated partial derivative of the PDP.
References
Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E., Peeking
Inside the Black Box: Visualizing Statistical Learning With Plots of
Individual Conditional Expectation. (2014) Journal of Computational
and Graphical Statistics, in press
Martin Maechler et al. sfsmisc: Utilities from Seminar fuer Statistik ETH Zurich.
R package version 1.0-24.
See Also
plot.dice, print.dice, summary.dice
Examples
## Not run:
# same examples as for 'ice', but now create a derivative estimate as well.
require(ICEbox)
require(randomForest)
require(MASS) #has Boston Housing data, Pima
######## regression example
data(Boston) #Boston Housing data
X = Boston
y = X$medv
X$medv = NULL
## build a RF:
bhd_rf_mod = randomForest(X, y)
## Create an 'ice' object for the predictor "age":
bhd.ice = ice(object = bhd_rf_mod, X = X, y = y, predictor = "age", frac_to_build = .1)
# make a dice object:
bhd.dice = dice(bhd.ice)
#### classification example
data(Pima.te) #Pima Indians diabetes classification
y = Pima.te$type
X = Pima.te
X$type = NULL
## build a RF:
pima_rf = randomForest(x = X, y = y)
## Create an 'ice' object for the predictor "skin":
# For classification we plot the centered log-odds. If we pass a predict
# function that returns fitted probabilities, setting logodds = TRUE instructs
# the function to set each ice curve to the centered log-odds of the fitted
# probability.
pima.ice = ice(object = pima_rf, X = X, predictor = "skin", logodds = TRUE,
predictfcn = function(object, newdata){
predict(object, newdata, type = "prob")[, 2]
}
)
# make a dice object:
pima.dice = dice(pima.ice)
## End(Not run)