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"`.

### 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.

`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 2.2.1 Index]