mle {stats4} | R Documentation

## 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-class`.

### 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(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE))
    (fit <- mle(ll))
    mle(ll, fixed=list(xhalf=6))

    summary(fit)
    logLik(fit)
    vcov(fit)
    plot(profile(fit), absVal=FALSE)
    confint(fit)

    ## use bounded optimization
    ## the lower bounds are really > 0, but we use >=0 to stress-test profiling
    (fit1 <- mle(ll, method="L-BFGS-B", lower=c(0, 0)))
    plot(profile(fit1), absVal=FALSE)

    ## a better parametrization:
    ll2 <- function(lymax=log(15), lxhalf=log(6))
        -sum(stats::dpois(y, lambda=exp(lymax)/(1+x/exp(lxhalf)), log=TRUE))
    (fit2 <- mle(ll2))
    plot(profile(fit2), absVal=FALSE)
    exp(confint(fit2))

[Package stats4 version 2.3.1 Index]