summary.maxim: Summary method for maximization

In maxLik: Maximum Likelihood Estimation and Related Tools

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

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.

Value

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

Author(s)

Ott Toomet

See Also

maxNR, returnCode, returnMessage

Examples

## 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)

maxLik documentation built on May 29, 2024, 2:32 a.m.