ghtFit                package:fBasics                R Documentation

_G_H_T _D_i_s_t_r_i_b_u_t_i_o_n _F_i_t

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

     Estimates the distributional parameters for a  generalized
     hyperbolic Student-t distribution.

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

      
     ghtFit(x, beta = 0.1, delta = 1, mu = 0, nu = 10, 
         scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, 
         title = NULL, description = NULL, ...) 

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

       x: a numeric vector.  

beta, delta, mu: The parameters are 'beta', 'delta', and 'mu':
           skewness parameter 'beta' is in the range (0, alpha); scale
          parameter 'delta' must be zero or positive;  location
          parameter 'mu', by default 0; and lambda parameter 'lambda',
          by default 1. 

      nu: defines the number of degrees of freedom.  Note, 'alpha'
          takes the limit of 'abs(beta)',  and 'lambda=-nu/2'. 

   scale: a logical flag, by default 'TRUE'. Should the time series be
          scaled by its standard deviation to achieve a more stable
          optimization? 

  doplot: a logical flag. Should a plot be displayed? 

    span: x-coordinates for the plot, by default 100 values 
          automatically selected and ranging between the 0.001,  and
          0.999 quantiles. Alternatively, you can specify the range by
          an expression like 'span=seq(min, max, times = n)', where,
          'min' and 'max' are the  left and right endpoints of the
          range, and 'n' gives  the number of the intermediate points. 

   trace: a logical flag. Should the parameter estimation process be
          traced? 

   title: a character string which allows for a project title. 

description: a character string which allows for a brief description. 

     ...: parameters to be parsed. 

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

     The function 'nlm' is used to minimize the "negative"  maximum
     log-likelihood function. 'nlm' carries out a minimization  using a
     Newton-type algorithm.

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

     returns a list with the following components:

estimate: the point at which the maximum value of the log liklihood 
          function is obtained. 

 minimum: the value of the estimated maximum, i.e. the value of the log
          liklihood function. 

    code: an integer indicating why the optimization process
          terminated.
           1: relative gradient is close to zero, current iterate is
          probably  solution; 
           2: successive iterates within tolerance, current iterate is
          probably  solution; 
           3: last global step failed to locate a point lower than
          'estimate'.  Either 'estimate' is an approximate local
          minimum of the  function or 'steptol' is too small; 
           4: iteration limit exceeded; 
           5: maximum step size 'stepmax' exceeded five consecutive
          times.  Either the function is unbounded below, becomes
          asymptotic to a  finite value from above in some direction or
          'stepmax'  is too small. 

gradient: the gradient at the estimated maximum. 

   steps: number of function calls. 

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

         
     ## ghtFit -
        # Simulate Random Variates:
        set.seed(1953)
        
     ## ghtFit -  
        # Fit Parameters:

