erboost.perf {erboost}

erboost performance

Description

Estimates the optimal number of boosting iterations for a erboost object and optionally plots various performance measures

Usage

erboost.perf(object, 
         plot.it = TRUE, 
         oobag.curve = FALSE, 
         overlay = TRUE, 
         method)

Arguments

object

a erboost.object created from an initial call to erboost.

plot.it

an indicator of whether or not to plot the performance measures. Setting plot.it=TRUE creates two plots. The first plot plots object$train.error (in black) and object$valid.error (in red) versus the iteration number. The scale of the error measurement, shown on the left vertical axis, depends on the distribution argument used in the initial call to erboost.

oobag.curve

indicates whether to plot the out-of-bag performance measures in a second plot.

overlay

if TRUE and oobag.curve=TRUE then a right y-axis is added to the training and test error plot and the estimated cumulative improvement in the loss function is plotted versus the iteration number.

method

indicate the method used to estimate the optimal number of boosting iterations. method="OOB" computes the out-of-bag estimate and method="test" uses the test (or validation) dataset to compute an out-of-sample estimate. method="cv" extracts the optimal number of iterations using cross-validation if erboost was called with cv.folds>1

Value

erboost.perf returns the estimated optimal number of iterations. The method of computation depends on the method argument.

Author(s)

Yi Yang [e-mail] and Hui Zou [e-mail]

References

Yang, Y. and Zou, H. (2015), “Nonparametric Multiple Expectile Regression via ER-Boost,” Journal of Statistical Computation and Simulation, 84(1), 84-95.

G. Ridgeway (1999). “The state of boosting,” Computing Science and Statistics 31:172-181.

https://cran.r-project.org/package=gbm

See Also

erboost, erboost.object