maxLik (version 1.3-4)

summary.maxim: Summary method for maximization

Description

Summarizes the maximization results

Usage

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

Arguments

object

optimization 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 maximization.

iterations

number of iterations.

code

exit code (see returnCode.)

message

a brief message, explaining the outcome (see returnMessage).

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

constraints

information about the constrained optimization. NULL if unconstrained maximization.

hessian

estimated hessian at maximum (if requested)

See Also

maxNR, returnCode, returnMessage

Examples

# NOT RUN {
## 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 )
# }