maxLik {micEcon} | R Documentation |
This is just a wrapper for maximisation routines which return object of class "maxLik". Corresponding methods can correctly handle the likelihood-specific properties of the estimate including the fact that inverse of negative hessian is the variance-covariance matrix.
maxLik(logLik, grad = NULL, hess = NULL, start, method = "Newton-Raphson", ...)
logLik |
log-likelihood function. Must have the parameter vector as the first argument. Must return either a single log-likelihood value or a numeric vector where each component is log-likelihood corresponding to individual observations. |
grad |
gradient of log-likelihood. Must have the parameter
vector as the first argument. Must return either single gradient
vector with length equal to the number of parameters, or a matrix
where each row corresponds to gradient vector of individual
observations. If NULL , numeric gradient will be used. |
hess |
hessian of log-likelihood. Must have the parameter
vector as the first argument. Must return a square matrix. If
NULL , numeric gradient will be used. |
start |
numeric vector, initial value of parameters. |
method |
maximisation method, currently either "Newton-Rapshon" or "BHHH". |
... |
further arguments for the maximisation routine. |
object of class 'maxLik' which inherits from class 'maximisation'.
Components are identical to those of class 'maximisation',
see maxNR
.
Ott Toomet otoomet@ut.ee
maxNR
, nlm
and optim
for different non-linear optimisation routines.
## ML estimation of exponential duration model: 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, print.level=2) summary(a) ## Estimate with analytic gradient and hessian a <- maxLik(loglik, gradlik, hesslik, start=1) summary(a)