summary.maxLik {maxLik}R Documentation

summary the Maximum-Likelihood estimation

Description

Summary the Maximum-Likelihood estimation including standard errors and t-values.

Usage

## S3 method for class 'maxLik'
summary(object, eigentol=1e-12, ... )
## S3 method for class 'summary.maxLik'
coef(object, ...)

Arguments

object

object of class 'maxLik', or 'summary.maxLik', usually a result from Maximum-Likelihood estimation.

eigentol

nonzero print limit on the range of the absolute values of the hessian. Specifically, define:

absEig <- eigen(hessian(object), symmetric=TRUE)[['values']]

Then compute and print t values, p values, etc. only if min(absEig) > (eigentol * max(absEig)).

...

currently not used.

Value

summary.maxLik returns an object of class 'summary.maxLik' with following components:

type

type of maximisation.

iterations

number of iterations.

code

code of success.

message

a short message describing the code.

loglik

the loglik value in the maximum.

estimate

numeric matrix, the first column contains the parameter estimates, the second the standard errors, third t-values and fourth corresponding probabilities.

fixed

logical vector, which parameters are treated as constants.

NActivePar

number of free parameters.

constraints

information about the constrained optimization. Passed directly further from maxim-object. NULL if unconstrained maximization.

coef.summary.maxLik returns the matrix of estimated values, standard errors, and t- and p-values.

Author(s)

Ott Toomet otoomet@ut.ee, Arne Henningsen

See Also

maxLik

Examples

## ML estimation of exponential duration model:
t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta*t
gradlik <- function(theta) 1/theta - t
hesslik <- function(theta) -100/theta^2
## Estimate with numeric gradient and hessian
a <- maxLik(loglik, start=1, print.level=2)
summary(a)
## Estimate with analytic gradient and hessian
a <- maxLik(loglik, gradlik, hesslik, start=1)
summary(a)

[Package maxLik version 1.1-0 Index]