research {GammaTest}R Documentation

The Random Embedding Search

Description

Calculates the Gamma statistic for a random selection of possible subset of inputs and plots the results in the form of a Gamma histogram.

Usage

research(data, percentage=10, plot=TRUE, ...)

Arguments

data Either an input-output dataset, where the outputs are in the last column.
percentage The random sample to take as a percentage of the total number of embeddings. Defaults to 10.
plot Logical indicating whether the Gamma histogram is plotted. Defaults to TRUE.
... Additional Gamma.Test parameters. Use if you wish to change the default values of p and eps when computing the Gamma Tests.

Details

Given an input dimension d, there are

2^d - 1

possible input subsets. A full embedding search calculates the Gamma statistic for each subset. The input subsets are then ordered in increasing Gamma value. The random embedding search is used to identify relevent variables for a smooth model when the number of candidate inputs is large i.e. around 20.

Value

Gammas An array of Gamma statistics ordered in increasing value.
mask.array An array of ordered masks by Gamma value.

Author(s)

Samuel E. Kemp. To report any bugs or suggestions please email: sekemp@glam.ac.uk

References

For papers and Gamma test related material visit http://users.cs.cf.ac.uk:81/Antonia.J.Jones/GammaArchive/IndexPage.htm

See Also

gammatest gammahist FCM

Examples

# Example on an AR(1) process
ar1     <- arima.sim(500, model=list(ar=0.9), sd=sqrt(1))
dvar1 <- dvec(ar1, 10)
my.fe   <- research(dvar1)

[Package GammaTest version 2.1 Index]