Nothing
rlongonly.test <- function( m, n=2, k=n, segments=NULL, x.t=1, x.l=0, x.u=x.t, max.iter=1000 )
{
###
### This function generates m long only random portfolios with n investments where
### weights are between given lower and upper bound and the sum of
### the weights equals a given total sum. Results are return as a matrix
###
### Arguments
### m = a positive integer value for the number of portfolios to be generated
### m = a positive integer value for the number of investments in a portfolio
### k = a positive integer value for the number of non zero weights
### segments = a vector or list of investment indices that defines portfolio segments
### x.t = a numeric value for the sum of the investment weights
### x.l = a numeric value for the lower bound for each weight
### x.u = a numeric value for the upper bound for each weight
### max.iter = a positive integer value for the maximum iterations in the rejection method
###
if ( missing( m ) )
stop( "Argument 'm' is missing" )
if ( m <= 0 )
stop( "Argument 'm' is not positive" )
###
### private function
###
by.case <- function( case, number, size, theseSegments, total, lower, upper, iterations )
{
return( random.longonly.test( n=number, k=size, segments=theseSegments, x.t=total,
x.l=lower, x.u=upper, max.iter=iterations ) )
}
results <- lapply( 1:m, by.case, n, k, segments, x.t, x.l, x.u, max.iter )
###
### separate the investment weights and iterations into a matrix and vector
###
xmatrix <- matrix( 0, nrow=m, ncol=n )
iters <- rep( 0, m )
for ( case in 1:m ) {
thisResult <- results[[case]]
iters[case] <- thisResult$iter
xmatrix[case,] <- thisResult$x
}
###
### create a new result list
###
result <- list( xmatrix=xmatrix, iters=iters )
return( result )
}
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