SA: Simulated Annealing for variable selection in classification
In ChemometricsWithR: Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences

Description Usage Arguments Details Value Author(s) References See Also Examples

A set of functions implementing simple variable selection in classification applications using simulated annealing

SAfun(x, response, eval.fun, Tinit, niter = 100,
      cooling = 0.05, fraction = 0.3, ...)
SAfun2(x, response, eval.fun, Tinit, niter = 100,
       cooling = 0.05, fraction = 0.3, ...)

SAstep(curr.set, maxvar, fraction = .3, size.dev = 1)

`x`	Data matrix: independent variables used by `eval.fun`
`response`	Class vector, used by `eval.fun`
`eval.fun`	evaluation function. Should take a data matrix, a class vector (or factor), and a subset argument
`Tinit`	Initial temperature
`niter`	Maximal number of iterations
`cooling`	Cooling speed
`fraction`	Size of the desired subset, as a fraction of the total number of variables
`...`	Further arguments to the evaluation function
`curr.set`	Current trial solution
`maxvar`	The total number of variables to choose from
`size.dev`	Parameter governing the variability in size of subsequent subsets

Simulated Annealing (SA) starts with a random subset, and proceeds by random moves in the solution space. In this implementation, a new solution may deviate in length at most size.dev variables: at most two variables may be swapped in or out at each step. If a step is an improvement, it is unconditionally accepted. If not, acceptance is a stochastic process depending on the current temperature - with high temperatures, "bad" moves are more likely to be accepted than with low temperatures. The process stops after a predefined number of iterations.

Functions SAfun and SAfun2 both return a list containing the following fields:

`best`	The best subset
`best.q`	The quality of the best subset

In addition, the outcome of SAfun2 also contains

`qualities`	A vector containing quality values of solutions seen throughout the optimization
`accepts`	A vector containing logicals indicating which solutions were accepted and which were rejected

Ron Wehrens

R. Wehrens. "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences". Springer, Heidelberg, 2011.

Evaluation, GA

if (require("pls")) {
data(gasoline, package = "pls")
## usually more than 50 iterations are needed
SAobj <- SAfun(gasoline$NIR, gasoline$octane,
               eval.fun = pls.cvfun, Tinit = 3,
               fraction = .02, niter = 50, ncomp = 2)
SAobj
} else {
  cat("Package pls not available.\nInstall it by typing 'install.packages(\"pls\")'")
}