Davies (version 1.2-0)

least.squares: Finds the optimal Davies distribution for a dataset

Description

Finds the best-fit Davies distribution using either the least-squares criterion (least.squares()) or maximum likelihood (maximum.likelihood())

Usage

least.squares(data, do.print = FALSE, start.v = NULL)
maximum.likelihood(data, do.print = FALSE, start.v = NULL)

Arguments

data

dataset to be fitted

do.print

Boolean with TRUE meaning print a GFM

start.v

A suitable starting vector of parameters c(C,lambda1,lambda2), with default NULL meaning to use start()

Value

Returns the parameters \(C,\lambda_1,\lambda_2\) of the best-fit Davies distribution to the dataset data

Details

Uses optim() to find the best-fit Davies distribution to a set of data.

Function least.squares() does not match that of Hankin and Lee 2006.

See Also

davies.start, optim, objective, likelihood

Examples

# NOT RUN {
  p <- c(10 , 0.1 , 0.1)
  d <- rdavies(10,p)

  maximum.likelihood(d)  # quite slow
  least.squares(d)       # much faster but not recommended
# }