Nothing
genetic<-function(mat, kmin, kmax=kmin, popsize=max(100,2*ncol(mat)), nger=100,
mutate=FALSE, mutprob=0.01, maxclone=5, exclude=NULL, include=NULL,
improvement=TRUE, setseed= FALSE, criterion="default", pcindices="first_k",
initialpop=NULL, force=FALSE, H=NULL, r=0,tolval=1000*.Machine$double.eps,
tolsym=1000*.Machine$double.eps)
{
#########################################################################################
# auxiliary variables (includes declarations to avoid "no visible binding" NOTE) #
#########################################################################################
p <- ncol(mat) # Number of original variables
nexclude <- length(exclude) # Number of excluded variables
ninclude <- length(include) # Number of included variables
if (pcindices[1]!="first_k") esp <- TRUE # The user has specified the set of Principal Components to be used with the GCD criterion
else esp <- FALSE # The user has not specified the set of Principal Components to be used with the GCD criterion
if (!is.null(initialpop)) pilog <- TRUE # The user has specified an initial population
else pilog <- FALSE # The user has not specified an initial population
exc <- exclude # Name for vector of excluded variables after 'validation' routine
inc <- include # Name for vector of included variables after 'validation' routine
###############################
# general validation of input #
###############################
initialization(mat, criterion, r)
if (validation(mat, kmin, kmax, exclude, include, criterion, pcindices, tolval, tolsym, "genetic") == "singularmat") singularmat = TRUE
else singularmat = FALSE
maxnovar = 400
if ((p > maxnovar) & (force==FALSE)) stop("\n For very large data sets, memory problems may crash the R session. \n To proceed anyways, repeat the function call with \n the argument 'force' set to 'TRUE' (after saving anything important \n from the current session)\n")
######################################################################
# Parameter validation if the criterion is one of "TAU_2", "XI_2", #
# "ZETA_2" or "CCR1_2" or "WALD" #
######################################################################
if (criterion == "TAU_2" || criterion == "XI_2" || criterion ==
"ZETA_2" || criterion == "CCR1_2" || criterion == "WALD") validnovcrit(mat,criterion,H,r,p,tolval,tolsym)
##############################################################
# more specific validation of input for the genetic function #
##############################################################
validgenetic(kmin, kmax, popsize, mutprob, exclude, nexclude, include, ninclude, initialpop)
##############################################
# initializations for the Fortran subroutine #
##############################################
# Normalize matrices in order to improve numerical accuracy
if (r==0) mat <- mat/max(mat)
else {
scl <- sqrt(diag(mat))
sclotprd <- outer(scl,scl)
mat <- mat/sclotprd
H <- H/sclotprd
}
if (setseed == TRUE) set.seed(2,kind="default")
valores<-rep(0.0,length(kmin:kmax)*popsize)
vars<-rep(0,popsize*length(kmin:kmax)*kmax)
bestval<-rep(0.0,length(kmin:kmax))
bestvar<-rep(0,kmax*length(kmin:kmax))
kabort<-kmax+1
if (criterio == 3) {
decespectral<-eigen(mat,symmetric=TRUE)
valp<-decespectral$values
vecp<-decespectral$vectors
}
else {
valp<-rep(0,p)
vecp<-matrix(nrow=p,ncol=p,rep(0,p*p))
}
##################################
# call to the Fortran subroutine #
##################################
if (criterion == "WALD") {
### Convert a min Wald problem into an (artificial) equivalent max "XI2" problem
Waldval <- sum(diag(solve(mat,H)))
H <- H / Waldval
criterion <- "XI_2"
criterio <- 5
}
else Waldval <- NULL
Fortout <- .Fortran("genetic",as.integer(criterio),as.integer(p),
as.double(as.vector(mat)),
as.integer(kmin),as.integer(kmax),as.double(valores),
as.integer(vars),as.double(bestval),as.integer(bestvar),
as.integer(nexclude),as.integer(exc),as.integer(ninclude),
as.integer(inc),as.integer(popsize),as.integer(nger),
as.integer(maxclone),as.logical(mutate),
as.double(mutprob),as.logical(improvement),
as.integer(length(pcindices)),as.integer(pcindices),as.logical(esp),
as.integer(kabort),as.logical(pilog),
as.integer(as.vector(initialpop)),as.double(valp),
as.double(as.vector(vecp)),as.double(as.vector(H)),as.integer(r),
as.logical(singularmat),as.double(tolval),logical(1),
PACKAGE="subselect")
#######################################
# Preparing and returning the output #
#######################################
output <- prepRetOtp(c(Fortout[6:9],call=match.call()),kmin,kmax,popsize,Waldval,"genetic",Numprb=Fortout[[32]])
kabort <- Fortout[[23]]
# if (kabort <= kmin || is.null(output)) return(invisible(NA))
# maxk <- min(kabort-1,kmin+nrow(output$bestsets)-1)
if (is.null(output)) return(invisible(NA))
maxk <- min(kabort,kmin+nrow(output$bestsets)-1)
list(subsets=output$subsets[,1:maxk,1:(maxk-kmin+1),drop=FALSE],
values=output$values[,1:(maxk-kmin+1),drop=FALSE],
bestvalues=output$bestvalues[1:(maxk-kmin+1)],
bestsets=output$bestsets[1:(maxk-kmin+1),1:maxk,drop=FALSE],
call=output$call)
}
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