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)