| 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", "log-normal", "lognormal", "logistic",
"negative binomial", "normal", "t", "uniform" 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 (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.
|
Direct optimization of the log-likelihood is performed, with numerical derivatives. The estimated standard errors are taken from the observed information, calculated by a numerical approximation.
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, uniform, weibull.
an object of class "fitdistr", a list with two components,
estimate |
the parameter estimates, and |
sd |
the estimated standard errors. |
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