Maximum-likelihood fitting of univariate distributions, allowing parameters to be held fixed if desired.
A numeric vector of length at least one containing only finite values.
Either a character string or a function returning a density evaluated at its first argument.
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
For the Normal, log-Normal, geometric, exponential and Poisson
distributions the closed-form MLEs (and exact standard errors) are
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
upper are supplied (when
L-BFGS-B is used) or
method is supplied explicitly.
"t" named distribution the density is taken to be the
location-scale family with location
m and scale
For the following named distributions, reasonable starting values will
be computed if
start is omitted or only partially specified:
"negative binomial" (parametrized by
"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.
logLik methods for class
An object of class
"fitdistr", a list with four components,
the parameter estimates,
the estimated standard errors,
the estimated variance-covariance matrix, and
Numerical optimization cannot work miracles: please note the comments
optim on scaling data. If the fitted parameters are
far away from one, consider re-fitting specifying the control
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
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## avoid spurious accuracy op <- options(digits = 3) 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 = 0.001) 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") options(op)
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