| 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 |
fixed |
Named list. Parameter values to keep fixed during optimization. |
... |
Further arguments to pass to |
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(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))