to_add_to_pkg/TB11.wiggle v3.r

In CCSBT/sbtr: sbtr

#' @title Wiggle factor
#' @description
#' Get future TACs from .s3 file and calculate wiggle factor. W = sum_years((d2TAC/dt2)^2 = (TAC[y+1]-2TAC[y]+TAC[y-1])^2). Basically the smoothness penalty you put on a GAM to avoid over-fitting.
#' @export
#' 
wiggle <- function(s3file, start.yr, end.yr, quants=c(0.5))
{
   tmp <- s3file
   col.start <- grep(paste("C.",start.yr,sep=""),colnames(tmp))
   col.end <- grep(paste("C.",end.yr,sep=""),colnames(tmp))
   cmat <- tmp[,col.start:col.end]
	# get rid of scale issue and mean standardise each catch trajectory
	cmat.ms <- t(apply(cmat,1,function(x){x <- x/mean(x)}))
	xx <- (cmat.ms[,-c(1,2)]-2*cmat.ms[,-c(1,dim(cmat.ms)[2])]+cmat.ms[,-c(dim(cmat.ms)[2]-1,dim(cmat.ms)[2])])^2
	W <- apply(xx,1,sum)
	# W is the statistic (value unimportant so no need for scale)
   temp <- quantile(W,quants)
	invisible(temp)
}