maxLik (version 0.5-6)

summary.maxim: Summary method for maximisation/minimisation

Description

Summarises the maximisation results

Usage

## S3 method for class 'maxim':
summary( object, hessian=FALSE, unsucc.step=FALSE, ... )

Arguments

object

optimisation result, object of class maxim. See maxNR.

hessian

logical, whether to display Hessian matrix.

unsucc.step

logical, whether to describe last unsuccesful step if code == 3

...

currently not used.

Value

• Object of class summary.maxim, intended to print with corresponding print method. There are following components:
• type
  type of maximisation.
• iterations
  number of iterations.
• code
  exit code (see maxNR.)
• message
  a brief message, explaining code.
• unsucc.step
  description of last unsuccessful step, only if requested and code == 3
• maximum
  function value at maximum
• estimate
  matrix with following columns:

  • results:

  {coefficient estimates at maximum} gradient:{estimated gradient at maximum}

item

hessian

See Also

maxNR

Examples

## minimize a 2D quadratic function:
f <- function(b) {
  x <- b[1]; y <- b[2];
    val <- (x - 2)^2 + (y - 3)^2
    attr(val, "gradient") <- c(2*x - 4, 2*y - 6)
    attr(val, "hessian") <- matrix(c(2, 0, 0, 2), 2, 2)
    val
}
## Note that NR finds the minimum of a quadratic function with a single
## iteration.  Use c(0,0) as initial value.  
result1 <- maxNR( f, start = c(0,0) ) 
summary( result1 )
## Now use c(1000000, -777777) as initial value and ask for hessian
result2 <- maxNR( f, start = c( 1000000, -777777)) 
summary( result2 )