summary.maxim {maxLik} | R Documentation |
Summarises the maximisation results
## S3 method for class 'maxim' summary( object, hessian=FALSE, unsucc.step=FALSE, ... )
object |
optimisation result, object of class
|
hessian |
logical, whether to display Hessian matrix. |
unsucc.step |
logical, whether to describe last unsuccesful step
if |
... |
currently not used. |
Object of class summary.maxim
, intended to print with
corresponding print method. There are following components:
type |
type of maximisation. |
iterations |
number of iterations. |
code |
exit code (see |
message |
a brief message, explaining code. |
unsucc.step |
description of last unsuccessful step, only if
requested and |
maximum |
function value at maximum |
estimate |
matrix with following columns:
|
constraints |
information about the constrained optimization.
Passed directly further from |
hessian |
estimated hessian at maximum, only if requested |
Ott Toomet siim@obs.ee
## 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 )