R: Methods for the various standard functions

AIC.maxLik {maxLik} | R Documentation

## Methods for the various standard functions

### Description

These are methods for the maxLik related objects. See also the documentation for the corresponding generic functions

### Usage

    ## S3 method for class 'maxLik'
    AIC(object, ..., k=2)
    ## S3 method for class 'maxim'
    coef(object, ...)
    ## S3 method for class 'maxLik'
    stdEr(x, eigentol=1e-12, ...)

### Arguments

`object` | a ‘maxLik’ object (or a ‘maxim’ object for `coef`)
`k` | numeric, the penalty per parameter to be used; the default ‘k = 2’ is the classical AIC.
`x` | a ‘maxLik’ object
`eigentol` | The standard errors are only calculated if the ration of the smallest and largest eigenvalue of the Hessian matrix is less than “eigentol”. Otherwise the Hessian is treated as singular.
`...` | other arguments for methods

### Details

AIC

  calculates Akaike's Information Criterion (and other information criteria).

coef

  extracts the estimated parameters (model's coefficients).

stdEr

  extracts standard errors (using the Hessian matrix).

### Examples

    ## estimate mean and variance of normal random vector
    set.seed( 123 )
    x <- rnorm(50, 1, 2 )
    ## log likelihood function.
    ## Note: 'param' is a vector
    llf <- function( param ) {
       mu <- param[ 1 ]
       sigma <- param[ 2 ]
       return(sum(dnorm(x, mean=mu, sd=sigma, log=TRUE)))
    }
    ## Estimate it.  Take standard normal as start values
    ml <- maxLik(llf, start = c(mu=0, sigma=1) )

    coef(ml)
    stdEr(ml)
    AIC(ml)

[Package maxLik version 1.3-4 Index]