summary.maxim: Summary method for maximization

In maxLik: Maximum Likelihood Estimation and Related Tools

Description

Summarizes the maximization results

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 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)

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
  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 )

Example output

Loading required package: miscTools

Please cite the 'maxLik' package as:
Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.

If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
https://r-forge.r-project.org/projects/maxlik/
--------------------------------------------
Newton-Raphson maximisation 
Number of iterations: 25 
Return code: 5 
Infinite value 
Function value: Inf 
Estimates:
           estimate       gradient
[1,] -7.034857e+155 -1.406971e+156
[2,] -1.055228e+156 -2.110457e+156
--------------------------------------------
--------------------------------------------
Newton-Raphson maximisation 
Number of iterations: 24 
Return code: 5 
Infinite value 
Function value: Inf 
Estimates:
           estimate       gradient
[1,]  2.110451e+155  4.220903e+155
[2,] -1.641470e+155 -3.282940e+155
--------------------------------------------

maxLik documentation built on Nov. 25, 2020, 3 a.m.