gumbel                 package:evir                 R Documentation

_F_i_t _G_u_m_b_e_l _D_i_s_t_r_i_b_u_t_i_o_n

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

     Fits gumbel distribution (GEV with 'xi = 0') to block maxima data.

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

     gumbel(data, block = NA, ...)

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

    data: data vector. Interpretation depends on value of block: if no
          block size is specified then data are interpreted as block
          maxima; if block size is set, then data are interpreted as
          raw data and block maxima are calculated.

   block: the block size. A numeric value is interpreted as the number
          of data values in each successive block. All the data is
          used, so the last block may not contain 'block' observations.
          If the 'data' has a 'times' attribute containing (in an
          object of class '"POSIXct"', or an object that can be
          converted to that class; see 'as.POSIXct') the times/dates of
          each observation, then 'block' may instead take the character
          values '"month"', '"quarter"', '"semester"' or '"year"'.

     ...: arguments passed to 'optim'

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

     This function is primarily intended for comparison with GEV for 
     assessing the need for a heavy-tailed Frechet (or short-tailed 
     Weibull) to model block maxima.

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

     An object of class '"gev"' describing the fit and including
     parameter estimates and standard errors. Fitting is carried out
     using maximum likelihood.

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

     'plot.gev', 'gev', 'optim', 'as.POSIXct'

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

     # Fit Gumbel to maxima of blocks of 100 observations
     data(bmw)
     out <- gumbel(bmw, 100) 
     # Fit Gumbel to the data in nidd.annual, the annual maximum water 
     # levels of the River Nidd, using the "BFGS" optimization method
     data(nidd.annual)
     out <- gumbel(nidd.annual, method = "BFGS", control = list(maxit = 500))

