dice function

Creates an object of class dice.

Estimates the partial derivative function for each curve in an ice object. See Goldstein et al (2013) for further details.

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.

Returns

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)