RPChoose: Chooses projection

In RPEnsemble: Random Projection Ensemble Classification

Description

Chooses a the best projection from a set of size B2 based on a test error estimate, then classifies the training and test sets using the chosen projection.

Usage

RPChoose(XTrain, YTrain, XTest, d, B2 = 10, base = "LDA", k = c(3,5), 
projmethod = "Haar", estmethod = "training", ...)

Arguments

XTrain	An n by p matrix containing the training data feature vectors
YTrain	A vector of length n of the classes (either 1 or 2) of the training data
XTest	An n.test by p matrix of the test data
d	The lower dimension of the image space of the projections
B2	The block size
base	The base classifier one of "knn","LDA","QDA" or "other"
k	The options for k if base is "knn"
projmethod	Either "Haar", "Gaussian" or "axis"
estmethod	Method for estimating the test errors to choose the projection: either training error "training" or leave-one-out "loo"
...	Optional further arguments if base = "other"

Details

Randomly projects the the data B2 times. Chooses the projection yielding the smallest estimate of the test error. Classifies the training set (via the same method as estmethod) and test set using the chosen projection.

Value

Returns a vector of length n + n.test: the first n entries are the estimated classes of the training set, the last n.test are the estimated classes of the test set.

Note

Resubstitution method unsuitable for the k-nearest neighbour classifier.

Author(s)

Timothy I. Cannings and Richard J. Samworth

References

Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959–1035

See Also

RPParallel, RPChooseSS, lda, qda, knn

Examples

set.seed(100)
Train <- RPModel(1, 50, 100, 0.5)
Test <- RPModel(1, 100, 100, 0.5)
Choose.out5 <- RPChoose(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, 
d = 2, B2 = 5, base = "QDA", projmethod = "Haar", estmethod = "loo")
Choose.out10 <- RPChoose(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, 
d = 2, B2 = 10, base = "QDA", projmethod = "Haar", estmethod = "loo")
sum(Choose.out5[1:50] != Train$y)
sum(Choose.out10[1:50] != Train$y)
sum(Choose.out5[51:150] != Test$y)
sum(Choose.out10[51:150] != Test$y)

Example output

Loading required package: distr
Loading required package: startupmsg
:startupmsg>  Utilities for Start-Up Messages (version 0.9.5)
:startupmsg> 
:startupmsg>  For more information see ?"startupmsg",
:startupmsg>  NEWS("startupmsg")

Loading required package: sfsmisc
:distr>  Object Oriented Implementation of Distributions (version
:distr>  2.7.0)
:distr> 
:distr>  Attention: Arithmetics on distribution objects are
:distr>  understood as operations on corresponding random variables
:distr>  (r.v.s); see distrARITH().
:distr> 
:distr>  Some functions from package 'stats' are intentionally masked
:distr>  ---see distrMASK().
:distr> 
:distr>  Note that global options are controlled by distroptions()
:distr>  ---c.f. ?"distroptions".
:distr> 
:distr>  For more information see ?"distr", NEWS("distr"), as well as
:distr>    http://distr.r-forge.r-project.org/
:distr>  Package "distrDoc" provides a vignette to this package as
:distr>  well as to several extension packages; try
:distr>  vignette("distr").


Attaching package: 'distr'

The following objects are masked from 'package:stats':

    df, qqplot, sd

Loading required package: MASS
Loading required package: parallel
[1] 19
[1] 19
[1] 52
[1] 53

RPEnsemble documentation built on Feb. 24, 2021, 5:06 p.m.