fitdistr

0th

Percentile

Maximum-likelihood Fitting of Univariate Distributions

Maximum-likelihood fitting of univariate distributions, allowing parameters to be held fixed if desired.

Keywords
distribution, htest
Usage
fitdistr(x, densfun, start, ...)
Arguments
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", <

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 co
Details

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 "t" named distribution the density is taken to be the location-scale family with location m and scale s. 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". Note that these starting values may not be good enough if the fit is poor: in particular they are not resistant to outliers unless the fitted distribution is long-tailed.

There are print, coef and logLik methods for class "fitdistr".

Value

  • An object of class "fitdistr", a list with three components,
  • estimatethe parameter estimates,
  • sdthe estimated standard errors, and
  • loglikthe log-likelihood.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Aliases
  • fitdistr
  • print.fitdistr
  • coef.fitdistr
  • logLik.fitdistr
Examples
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 fixed-df fit 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
Documentation reproduced from package MASS, version 7.3-1, License: GPL-2 | GPL-3

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