fitdistr                package:MASS                R Documentation

_M_a_x_i_m_u_m-_l_i_k_e_l_i_h_o_o_d _F_i_t_t_i_n_g _o_f _U_n_i_v_a_r_i_a_t_e _D_i_s_t_r_i_b_u_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     fitdistr(x, densfun, start, ...)

_A_r_g_u_m_e_n_t_s:

       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. 

_D_e_t_a_i_l_s:

     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'.

_V_a_l_u_e:

     an object of class '"fitdistr"', a list with two components,

estimate: the parameter estimates, and 

      sd: the estimated standard errors. 

_R_e_f_e_r_e_n_c_e_s:

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

_E_x_a_m_p_l_e_s:

     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

