Davies (version 1.1-5)

davies.start: start value for Davies minimization routines

Description

Gives a start value for the optimization routines. Uses heuristics that seem to work.

Usage

davies.start(x, threeps=c(0.1,0.5,0.9), small = 0.01)

Arguments

x

dataset to be used

threeps

a three-element vector representing the quantiles to be balanced. The default values balance the first and ninth deciles and the median. These seem to work for me pretty well; YMMV

small

a small value to be used for $\lambda_1$ and $\lambda_1$ because exactly zero sucks

Details

Returns a start value of the pararameters for use in one of the Davies fitting routines maximum.likelihood() or least.squares().

Uses three heuristic methods (one assuming $\lambda_1= -\lambda_2$, one with $\lambda_1=0$, and one with $\lambda_2=0$). Returns the best one of the three, as measured by objective().

See Also

least.squares , maximum.likelihood, objective

Examples

data <- rnorm(100)^2
davies.start(data)
least.squares(data)

params <- c(10 , 0.1 , -0.1)
x <- rdavies(100 , params)
davies.start(x)

f <- function(threeps){objective(davies.start(x,threeps),x)}

(jj<-optim(c(0.1,0.5,0.9),f))
davies.start(x,jj$par)
least.squares(x)

#not bad at all.