R/deriva.R

Defines functions deriva

Documented in deriva

#' Numerical derivatives
#'
#' The function computes the first derivates and the information score matrix.
#' Central finite-differences and forward finite-differences are used for the first
#' and second derivatives respectively.
#'
#' @param nproc number of processors for parallel computing
#' @param b value of parameters to be optimized over
#' @param funcpa function to be minimized (or maximized), with argument the vector
#' of parameters over which minimization isto take place.
#' It should return a scalar result.
#' @param .packages character vector of packages that funcpa depends on
#' @param \dots other arguments of the funcpa function
#'
#' @return \item{v}{vector containing the upper part of the information score
#' matrix and the first derivatives} \item{rl}{the value of the funcpa function
#' at point b}
#' @author Viviane Philipps, Boris Hejblum, Cecile Proust-Lima, Daniel Commenges
#' @references Donald W. Marquardt An algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics, Vol. 11, No. 2. (Jun, 1963), pp. 431-441.
#'
#' @examples
#' b <- 0.1
#' f <- function(b){return((2*b[1]**2+3*b[1]))}
#' d <- deriva(b=b,funcpa=f)
#' 
#' @export
#' 
deriva <- function(nproc=1,b,funcpa,.packages=NULL,...){

    m <- length(b)
    bh2 <- bh <- rep(0,m)
    v <- rep(0,(m*(m+3)/2))
    fcith <- fcith2 <- rep(0,m)
    ## function 
    rl <- funcpa(b,...)
    
    if(nproc>1)
        {
            ### remplacer les 2 boucles par une seule
            grid <- cbind(c(rep(1:m,1:m),1:m),c(unlist(sapply(1:m,function(k) seq(1,k))),rep(0,m)))
            mm <- nrow(grid)
            h <- sapply(b,function(x){max(1E-7,(1E-4*abs(x)))})


            ## derivees premieres:
            ll <- foreach(k=(m*(m+1)/2)+1:m,
                          .combine=cbind,
                          .packages=.packages) %dopar%
            {
                i <- grid[k,1]
                
                bp <- b
                bp[i] <- b[i]+h[i]
                av <- funcpa(bp,...)
                
                bm <- b
                bm[i] <- b[i]-h[i]
                ar <- funcpa(bm,...)
                
                d <- (av-ar)/(2*h[i])
                
                c(av,d)
            }

            fcith <- ll[1,]
            v1 <- ll[2,] 

            ## derivees secondes:
            v2 <- foreach(k=1:(m*(m+1)/2),
                          .combine=c,
                          .packages=.packages) %dopar%
            {
                i <- grid[k,1]
                j <- grid[k,2]
                bij <- b
                bij[i] <- bij[i]+h[i]
                bij[j] <- bij[j]+h[j]
                
                res <- -(funcpa(bij,...)-fcith[i]-fcith[j]+rl)/(h[i]*h[j])
                res
            }
            
            v <- c(v2,v1)
            
        }
    else
        {
            ## gradient null
            for(i in 1:m){
                bh <- bh2 <- b
                th <- max(1E-7,(1E-4*abs(b[i])))
                bh[i] <- bh[i] + th
                bh2[i] <- bh2[i] - th
                
                fcith[i] <- funcpa(bh,...)
                fcith2[i] <- funcpa(bh2,...)
            }
                        
            k <- 0
            m1 <- m*(m+1)/2
            l <- m1
            for(i in 1:m){
		l <- l+1
		bh <- b
		thn <- - max(1E-7,(1E-4*abs(b[i])))
		v[l] <- -(fcith[i]-fcith2[i])/(2*thn)
		for(j in 1:i){
			bh <- b
			k <- k+1
			thi <- max(1E-7,(1E-4*abs(b[i])))
			thj <- max(1E-7,(1E-4*abs(b[j])))
			th <- thi * thj
			bh[i] <- bh[i]+thi
			bh[j] <- bh[j]+thj
			temp <-funcpa(bh,...)
			v[k] <- -(temp-(fcith[j])-(fcith[i])+rl)/th
                    }
            }
        }
    	
    result <- list(v=v,rl=rl)
    return(result)
}

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marqLevAlg documentation built on April 2, 2021, 1:05 a.m.