| fitdistr {MASS} | R Documentation |
Maximum-likelihood fitting of univariate distributions, allowing parameters to be held fixed if desired.
fitdistr(x, densfun, start, ...)
x |
A numeric vector. |
densfun |
Either a character string or a function returning a density evaluated
at its first argument.
Distributions "beta", "cauchy", "chi-squared",
"exponential", "f", "gamma", "geometric",
"log-normal", "lognormal", "logistic",
"negative binomial", "normal", "Poisson",
"t" and "weibull" are recognised, case being ignored.
|
start |
A named list giving the parameters to be optimized with initial values. This can be omitted for some of the named distributions and must be for others (see Details). |
... |
Additional parameters, either for densfun or for optim.
In particular, it can be used to specify bounds via lower or
upper or both. If arguments of densfun (or the density
function corresponding to a character-string specification) are included
they will be held fixed.
|
For the Normal, log-Normal, exponential and Poisson distributions the
closed-form MLEs (and exact standard errors) are used, and
start should not be supplied.
For all other distributions, direct optimization of the log-likelihood
is performed using optim. The estimated standard
errors are taken from the observed information matrix, calculated by a
numerical approximation. For one-dimensional problems the Nelder-Mead
method is used and for multi-dimensional problems the BFGS method,
unless arguments named lower or upper are supplied when
L-BFGS-B is used or method is supplied explicitly.
For the following named distributions, reasonable starting values will
be computed if start is omitted or only partially specified:
cauchy, gamma, logistic, negative binomial
(parametrized by mu and size), t and weibull.
There are print, coef and
logLik methods for class "fitdistr".
An object of class "fitdistr", a list with three components,
estimate |
the parameter estimates, |
sd |
the estimated standard errors, and |
loglik |
the log-likelihood. |
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
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