newton {Bhat}R Documentation

Function minimization with box-constraints

Description

Newton-Raphson algorithm for minimizing a function f over the parameters specified in the input list x. Note, a Newton-Raphson search is very efficient in the 'quadratic region' near the optimum. In higher dimensions it tends to be rather unstable and may behave chaotically. Therefore, a (local or global) minimum should be available to begin with. Use the optim or dfp functions to search for optima.

Usage

newton(x, f, eps=0.1, itmax=10, relax=0, nfcn=0)

Arguments

x a list with components 'label' (of mode character), 'est' (the parameter vector with the initial guess), 'low' (vector with lower bounds), and 'upp' (vector with upper bounds)
f the function that is to be minimized over the parameter vector defined by the list x
eps converges when all (logit-transformed) derivatives are smaller eps
itmax maximum number of Newton-Raphson iterations
relax numeric. If 0, take full Newton step, otherwise 'relax' step incrementally until a better value is found
nfcn number of function calls

Value

list with the following components:

fmin the function value f at the minimum
label the labels
est a vector of the parameter estimates at the minimum. newton does not overwrite x
low lower 95% (Wald) confidence bound
upp upper 95% (Wald) confidence bound

The confidence bounds assume that the function f is a negative log-likelihood

Note

newton computes the (logit-transformed) Hessian of f (using logit.hessian). This function is part of the Bhat exploration tool

Author(s)

E. Georg Luebeck (FHCRC)

See Also

dfp, ftrf, btrf, logit.hessian, plkhci

Examples

        # generate some Poisson counts on the fly
          dose <- c(rep(0,100),rep(1,100),rep(5,100),rep(10,100))
          data <- cbind(dose,rpois(400,20*(1+dose*.5*(1-dose*0.05))))

        # neg. log-likelihood of Poisson model with 'linear-quadratic' mean: 
          lkh <- function (x) { 
          ds <- data[, 1]
          y  <- data[, 2]
          g <- x[1] * (1 + ds * x[2] * (1 - x[3] * ds)) 
          return(sum(g - y * log(g)))
          }

        # for example define
          x <- list(label=c("a","b","c"),est=c(10.,10.,.01),low=c(0,0,0),upp=c(100,20,.1))

        # calls:
          r <- dfp(x,f=lkh)
          x$est <- r$est
          results <- newton(x,lkh)

[Package Bhat version 0.9-08 Index]