R: Summary method for maximization

summary.maxim {maxLik} | R Documentation

## Summary method for maximization

### Description

Summarizes the maximization results

### Usage

    ## S3 method for class '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)

### 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 )

[Package maxLik version 1.3-4 Index]