fextreme                 package:evd                 R Documentation

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

     Maximum-likelihood fitting for the distribution of the
     maximum/minimum of a given number of independent variables from a
     specified distribution.

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

     fextreme(x, start, densfun, distnfun, ..., distn, mlen = 1, largest =
         TRUE, std.err = TRUE, corr = FALSE, method = "Nelder-Mead")

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

       x: A numeric vector.

   start: A named list giving the initial values for the parameters
          over which the likelihood is to be maximized.

densfun, distnfun: Density and distribution function of the specified
          distribution.

     ...: Additional parameters, either for the specified distribution
          or for the optimization function 'optim'. If parameters of
          the distribution are included they will be held fixed at the
          values given (see *Examples*). If parameters of the
          distribution are not included either here or as a named
          component in 'start' they will be held fixed at the default
          values specified in the corresponding density and
          distribution functions (assuming they exist; an error will be
          generated otherwise).

   distn: A character string, optionally specified as an alternative to
          'densfun' and 'distnfun' such that the density and
          distribution functions are formed upon the addition of the
          prefixes 'd' and 'p' respectively.

    mlen: The number of independent variables.

 largest: Logical; if 'TRUE' (default) use maxima, otherwise minima.

 std.err: Logical; if 'TRUE' (the default), the standard errors are
          returned.

    corr: Logical; if 'TRUE', the correlation matrix is returned.

  method: The optimization method (see 'optim' for details).

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

     Maximization of the log-likelihood is performed. The estimated
     standard errors are taken from the observed information,
     calculated by a numerical approximation. 

     If the density and distribution functions are user defined, the
     order of the arguments must mimic those in R base (i.e. data
     first, parameters second). Density functions must have 'log'
     arguments.

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

     Returns an object of class 'c("extreme","evd")'.

     The generic accessor functions 'fitted' (or 'fitted.values'),
     'std.errors', 'deviance', 'logLik' and 'AIC' extract various
     features of the returned object. The function 'anova' compares
     nested models.

     An object of class 'c("extreme","evd")' is a list containing at
     most the following components 

estimate: A vector containing the maximum likelihood estimates.

 std.err: A vector containing the standard errors.

deviance: The deviance at the maximum likelihood estimates.

    corr: The correlation matrix.

convergence, counts, message: Components taken from the list returned
          by 'optim'.

    call: The call of the current function.

    data: The data passed to the argument 'x'.

       n: The length of 'x'.

_S_e_e _A_l_s_o:

     'anova.evd', 'forder', 'optim'

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

     uvdata <- rextreme(100, qnorm, mean = 0.56, mlen = 365)
     fextreme(uvdata, list(mean = 0, sd = 1), distn = "norm", mlen = 365)
     fextreme(uvdata, list(rate = 1), distn = "exp", mlen = 365)
     fextreme(uvdata, list(scale = 1), shape = 1, distn = "gamma", mlen = 365)
     fextreme(uvdata, list(shape = 1, scale = 1), distn = "gamma", mlen = 365)

