## Maximum likelihood estimation

### Description

Estimate parameters by the method of maximum likelihood.

### Usage

mle(minuslogl, start = formals(minuslogl), method = "BFGS", fixed = list(), ...)

### Arguments

`minuslogl` |
Function to calculate negative log-likelihood |

`start` |
Named list. Initial values for optimizer |

`method` |
Optimization method to use. See `optim` |

`fixed` |
Named list. Parameter values to keep fixed during
optimization |

`...` |
Further arguments to pass to `optim` |

### Details

The `optim`

optimizer is used to find the minimum of the negative
log-likelihood. An approximate covariance matrix for the parameters is
obtained by inverting the Hessian matrix at the optimum.

### Value

An object of class `"mle"`

### Note

Be careful to note that the argument is -log L (not -2 log L). It
is for the user to ensure that the likelihood is correct, and that
asymptotic likelihood inference is valid.

### See Also

`mle-class`

### Examples

x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
ll <- function(ymax=15,xhalf=6)
-sum(dpois(y,lambda=ymax/(1+x/xhalf),log=TRUE))
mle(ll)
mle(ll,fixed=list(xhalf=6))