Summary method for maximization
Summarizes the maximization results
## 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),
... )object |
optimization result, object of class
|
hessian |
logical, whether to display Hessian matrix. |
unsucc.step |
logical, whether to describe last unsuccesful step
if |
x |
object of class |
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. |
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 |
message |
a brief message, explaining the outcome (see
|
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.
|
hessian |
estimated hessian at maximum (if requested) |
Ott Toomet
## 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 )Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.