COBRA (version 0.99.4)

COBRA-package: Nonlinear Aggregation of Predictors

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 minimal 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).

Details

Package:

COBRA

Type:

Package

Version:

0.99.4

Date:

2013-07-30

License:

GPL (>= 2)

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

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)