COBRA (version 0.99.4)

COBRA: COBRA

Description

The function COBRA delivers prediction outcomes for a testing sample on the basis of a training sample and a bunch of basic regression machines. By default, those machines are wrappers to the R packages lars, ridge, tree and randomForest, covering a somewhat wide spectrum in contemporary prediction methods for regression. However the most interesting way to use COBRA is to use any regression method suggested by the context (see argument machines). COBRA may natively parallelize the computations (use option parallel).

Usage

COBRA(train.design,
      train.responses,
      split,
      test,
      machines,
      machines.names,
      logGrid = FALSE,
      grid = 200,
      alpha.machines,
      parallel = FALSE,
      nb.cpus = 2,
      plots = FALSE,
      savePlots = FALSE,
      logs = FALSE,
      progress = TRUE,
      path = "")

Arguments

train.design

Mandatory. The design matrix for the training sample.

train.responses

Mandatory. The responses vector for the training sample.

split

Optional. How should COBRA cut the training sample?

test

Mandatory. The design matrix of the testing sample.

machines

Optional. Regression basic machines provided by the user. This should be a matrix, whose number of rows is the length of the training sample (ntrain) plus the length of the testing sample (ntest), and with as many columns as machines. Element

machines.names

Optional. If machines is provided, a list including the names of the machines.

logGrid

Optional. If TRUE, parameter epsilon is generated according to a logarithmic scale. This should be TRUE if the user has a clue about the small magnitude of predictions.

grid

Optional. How many points should be used in the discretization scheme for calibrating the parameter epsilon.

alpha.machines

Optional. Coerce COBRA to use exactly alpha.machines. Obviously this should be a integer between 1 and the total number of machines.

parallel

Optional. If TRUE, computations will be dispatched over available cpus.

nb.cpus

Optional. If parallel, how many cpus should be used. Obviously this should not exceed the number of available cpus!

plots

Optional. If TRUE, explanatory plots about calibrating epsilon and alpha (see publication) are generated according to the path variable.

savePlots

Optional. If TRUE, plots are saved as .pdf files according to path, otherwise they pop up in the R IDE.

logs

Optional. If TRUE, quadratic risks over the training sample for all machines and COBRA are written in the file "risks.txt" according to the path variable.

progress

Optional. If TRUE, a progress bar and final quadratic errors are printed.

path

Optional. If savePlots and either plots or logs are TRUE, where should the corresponding files be created?

Value

• Returns a list including only
• predict
  The vector of predicted values.

Details

For most users, options grid and split should be set to their default values.

References

http://www.lsta.upmc.fr/doct/guedj/index.html

G. Biau, A. Fischer, B. Guedj and J. D. Malley (2013), COBRA: A Nonlinear Aggregation Strategy. http://arxiv.org/abs/1303.2236 and http://hal.archives-ouvertes.fr/hal-00798579

See Also

COBRA-package

Examples

n <- 500
d <- 30
ntrain <- 400
X <- replicate(d,2*runif(n = n)-1)
Y <- X[,1]^2 + X[,3]^3 + exp(X[,10]) + rnorm(n = n, sd = .1)
train.design <- as.matrix(X[1:ntrain,])
train.responses <- Y[1:ntrain]
test <- as.matrix(X[-(1:ntrain),])
test.responses <- Y[-(1:ntrain)]

## using the default machines
if(require(lars) && require(tree) && require(ridge) &&
require(randomForest))
{
res <- COBRA(train.design = train.design,
             train.responses = train.responses,
             test = test)

print(cbind(res$predict,test.responses))
plot(test.responses,res$predict,xlab="Responses",ylab="Predictions",pch=3,col=2)
abline(0,1,lty=2)
}

## using own machines
machines.names <- c("Soothsayer","Dummy")
machines <- matrix(nr = n, nc = 2, data = 0)
machines[,1] <- Y+rnorm(n = n, sd=.1)          ## soothsayer
machines[,2] <- mean(train.responses)          ## dummy prediction, averaging train.responses

res2 <- COBRA(train.design = train.design,
              train.responses = train.responses,
              test = test,
              machines = machines,
              machines.names = machines.names)

print(cbind(res2$predict,test.responses))
plot(test.responses,res2$predict,xlab="Responses",ylab="Predictions",pch=3,col=2)
abline(0,1,lty=2)