Methods for the various standard functions
These are methods for the maxLik related objects. See also the documentation for the corresponding generic functions
## S3 method for class 'maxLik' AIC(object, ..., k=2) ## S3 method for class 'maxim' coef(object, ...) ## S3 method for class 'maxLik' coef(object, ...) ## S3 method for class 'maxLik' stdEr(x, eigentol=1e-12, ...)
object |
a ‘maxLik’ object ( |
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 ratio 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 |
calculates Akaike's Information Criterion (and other information criteria).
extracts the estimated parameters (model's coefficients).
extracts standard errors (using the Hessian matrix).
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.