Description Usage Arguments Details Examples
These are methods for the maxLik related objects. See also the documentation for the corresponding generic functions
1 2 3 4 5 6 |
object |
a ‘maxLik’ object (or a
‘maxim’ object for |
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 |
calculates Akaike's Information Criterion (and other information criteria).
extracts the estimated parameters (model's coefficients).
extracts standard errors (using the Hessian matrix).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## 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)
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