maxLik (version 1.3-4)

maxLik: Maximum likelihood estimation


This is the main interface for the maxLik package, and the function that performs Maximum Likelihood estimation. It is a wrapper for different optimizers returning an object of class "maxLik". Corresponding methods handle the likelihood-specific properties of the estimates, including standard errors.


maxLik(logLik, grad = NULL, hess = NULL, start, method,
constraints=NULL, ...)



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 of the corresponding individual observation.


gradient of log-likelihood. Must have the parameter vector as the first argument. Must return either a single gradient vector with length equal to the number of parameters, or a matrix where each row is the gradient vector of the corresponding individual observation. If NULL, numeric gradient will be used.


hessian of log-likelihood. Must have the parameter vector as the first argument. Must return a square matrix. If NULL, numeric Hessian will be used.


numeric vector, initial value of parameters. If it has names, these will also be used for naming the results.


maximisation method, currently either "NR" (for Newton-Raphson), "BFGS" (for Broyden-Fletcher-Goldfarb-Shanno), "BFGSR" (for the BFGS algorithm implemented in R), "BHHH" (for Berndt-Hall-Hall-Hausman), "SANN" (for Simulated ANNealing), "CG" (for Conjugate Gradients), or "NM" (for Nelder-Mead). Lower-case letters (such as "nr" for Newton-Raphson) are allowed. If missing, a suitable method is selected automatically.


either NULL for unconstrained maximization or a list, specifying the constraints. See maxBFGS.

further arguments, such as control are passed to the selected maximisation routine, i.e. maxNR, maxBFGS, maxBFGSR, maxBHHH, maxSANN, maxCG, or maxNM (depending on argument method). Arguments not used by the optimizers are forwarded to logLik, grad and hess.


object of class 'maxLik' which inherits from class 'maxim'. The structure is identical to that of the class “maxim” (see maxNR) but the methods differ.


The constrained maximum likelihood estimation should be considered experimental. In particular, the variance-covariance matrix is not corrected for constrained parameter space.


maxLik supports constrained optimization in the sense that constraints are passed further to the underlying optimization routines, and suitable default method is selected. However, no attempt is made to correct the resulting variance-covariance matrix. Hence the inference may be wrong. A corresponding warning is issued by the summary method.

See Also

maxNR, nlm and optim for different non-linear optimisation routines, see maxBFGS for the constrained maximization examples.


## Estimate the parameter of exponential distribution
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, control=list(printLevel=2))
summary( a )
## Estimate with analytic gradient and hessian
a <- maxLik(loglik, gradlik, hesslik, start=1)
summary( a )
## Next, we give an example with vector argument:  Estimate the mean and
## variance of a random normal sample by maximum likelihood
loglik <- function(param) {
  mu <- param[1]
  sigma <- param[2]
  ll <- -0.5*N*log(2*pi) - N*log(sigma) - sum(0.5*(x - mu)^2/sigma^2)
x <- rnorm(100, 1, 2) # use mean=1, stdd=2
N <- length(x)
res <- maxLik(loglik, start=c(0,1)) # use 'wrong' start values
summary( res )
## The previous example showing parameter names and fixed values
resFix <- maxLik(loglik, start=c(mu=0, sigma=1), fixed="sigma")
summary(resFix)  # 'sigma' is exactly 1.000 now.
# }