dfp {Bhat} | R Documentation |
This Davidon-Fletcher-Powell optimization algorithm has been `hand-tuned'
for minimal setup configuration and for efficency. It uses an internal
logit-type transformation based on the pre-specified box-constraints.
Therefore, it usually does not require rescaling (see help for the R
optim function). dfp
automatically computes step sizes for each
parameter to operate with sufficient sensitivity in the functional
output. Performance is comparable to the BFGS algorithm in the R
function optim
. dfp
interfaces with newton
to
ascertain convergence, compute the eigenvalues of the Hessian, and
provide 95% confidence intervals when the function to be minimized is a
negative log-likelihood.
The dfp function minimizes a function f
over the parameters
specified in the input list x
. The algorithm is based on Fletcher's
"Switching Method" (Comp.J. 13,317 (1970))
dfp(x, f, tol=1e-05, nfcn=0)
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 |
tol |
a tolerance used to determine when convergence should be indicated |
nfcn |
number of function calls |
the code has been 'transcribed' from Fortran source code into R
list with the following components:
fmin |
the function value f at the minimum |
label |
the labels taken from list x |
est |
a vector of the estimates at the minimum. dfp
does not overwrite x |
status |
0 indicates convergence, 1 indicates non-convergence |
nfcn |
no. of function calls |
This function is part of the Bhat exploration tool
E. Georg Luebeck (FHCRC)
Fletcher's Switching Method (Comp.J. 13,317, 1970)
optim, newton
, ftrf
, btrf
, logit.hessian
# generate some Poisson counts on the fly dose <- c(rep(0,50),rep(1,50),rep(5,50),rep(10,50)) data <- cbind(dose,rpois(200,20*(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)) # call: results <- dfp(x,f=lkh)