R: summary the Maximum-Likelihood estimation

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` | The standard errors are only calculated if the ratio of the smallest and largest eigenvalue of the Hessian matrix is less than “eigentol”. Otherwise the Hessian is treated as singular.
`...` | currently not used.

### Value

An object of class 'summary.maxLik' with following components:

type

  type of maximization.

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.

### Author(s)

Ott Toomet, Arne Henningsen

### See Also

`maxLik`

### Examples

    ## ML estimation of exponential distribution:
    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, control=list(printLevel=2))
    summary(a)
    ## Estimate with analytic gradient and hessian
    a <- maxLik(loglik, gradlik, hesslik, start=1, control=list(printLevel=2))
    summary(a)

[Package maxLik version 1.3-4 Index]