summary.maxim: Summary method for maximization

Description

Summarizes the general maximization results in a way that does not assume the function is log-likelihood.

Usage

# S3 method for maxim
summary( object, hessian=FALSE, unsucc.step=FALSE, ... )
# S3 method for summary.maxim
print(x,
                              max.rows=getOption("max.rows", 20),
                              max.cols=getOption("max.cols", 7),
                              ... )

Value

Object of class summary.maxim, intended to be printed with corresponding print method.

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
x: object of class summary.maxim, summary of maximization result.
max.rows: maximum number of rows to be printed. This applies to the resulting coefficients (as those are printed as a matrix where the other column is the gradient), and to the Hessian if requested.
max.cols: maximum number of columns to be printed. Only Hessian output, if requested, uses this argument.
...: currently not used.

Author

Ott Toomet

Examples

Run this code

## minimize a 2D quadratic function:
f <- function(b) {
  x <- b[1]; y <- b[2];
  val <- -(x - 2)^2 - (y - 3)^2  # concave parabola
  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.  
res <- maxNR( f, start = c(0,0) ) 
summary(res)
summary(res, hessian=TRUE)