\Header{fitdistr}{Maximum-likelihood Fitting of Univariate Distributions}
\alias{coef.fitdistr}{fitdistr}
\alias{print.fitdistr}{fitdistr}
\keyword{distribution}{fitdistr}
\keyword{htest}{fitdistr}
\begin{Description}\relax
Maximum-likelihood fitting of univariate distributions, allowing
parameters to be held fixed if desired.
\end{Description}
\begin{Usage}
\begin{verbatim}
fitdistr(x, densfun, start, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] A numeric vector.

\item[\code{densfun}] Either a character string or a function returning a density evaluated
at its first argument.

Distributions \code{"beta"}, \code{"cauchy"}, \code{"chi-squared"},
\code{"exponential"}, \code{"f"}, \code{"gamma"}, \code{"log-normal"},
\code{"lognormal"}, \code{"logistic"}, \code{"negative binomial"},
\code{"normal"}, \code{"t"}, \code{"uniform"} and \code{"weibull"} are
recognised, case being ignored.

\item[\code{start}] A named list giving the parameters to be optimized with initial
values.  This can be omitted for some of the named distributions
(see Details).

\item[\code{...}] Additional parameters, either for \code{densfun} or for \code{optim}.
In particular, it can be used to specify bounds via \code{lower} or
\code{upper} or both.  If arguments of \code{densfun} (or the density
function corresponding to a character-string specification) are included
they will be held fixed.

\end{ldescription}
\end{Arguments}
\begin{Details}\relax
For \code{densfun = "normal"} the closed-form MLEs (and standard
errors) are used, and \code{start} should not be supplied.

For all other distributions, direct optimization of the log-likelihood
is performed, with numerical derivatives.  The estimated standard
errors are taken from the observed information matrix, calculated by a
numerical approximation.

For the following named distributions, reasonable starting values will
be computed if \code{start} is omitted or only partially specified:
\code{cauchy}, \code{gamma}, \code{logistic}, \code{negative binomial}
(parametrized by \code{mu} and \code{size}), \code{t}, \code{uniform},
\code{weibull}.
\end{Details}
\begin{Value}
An object of class \code{"fitdistr"}, a list with two components,

\begin{ldescription}
\item[\code{estimate}] the parameter estimates, and
\item[\code{sd}] the estimated standard errors.
\end{ldescription}
\end{Value}
\begin{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.
\end{References}
\begin{Examples}
\begin{ExampleCode}
set.seed(123)
x <- rgamma(100, shape = 5, rate = 0.1)
fitdistr(x, "gamma")
## now do this directly with more control.
fitdistr(x, dgamma, list(shape = 1, rate = 0.1), lower = 0.01)

set.seed(123)
x2 <- rt(250, df = 9)
fitdistr(x2, "t", df = 9)
## allow df to vary: not a very good idea!
fitdistr(x2, "t")
## now do this directly with more control.
mydt <- function(x, m, s, df) dt((x-m)/s, df)/s
fitdistr(x2, mydt, list(m = 0, s = 1), df = 9, lower = c(-Inf, 0))

set.seed(123)
x3 <- rweibull(100, shape = 4, scale = 100)
fitdistr(x3, "weibull")

set.seed(123)
x4 <- rnegbin(500, mu = 5, theta = 4)
fitdistr(x4, "Negative Binomial") # R only
\end{ExampleCode}
\end{Examples}

