ghyp-data                package:ghyp                R Documentation

_G_e_t _m_e_t_h_o_d_s _f_o_r _o_b_j_e_c_t_s _i_n_h_e_r_i_t_i_n_g _f_r_o_m _c_l_a_s_s _g_h_y_p_b_a_s_e

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

     These functions simply return data stored within generalized
     hyperbolic distribution objects, i.e. slots of the classes 'ghyp'
     and 'mle.ghyp'.  'ghyp.fit.info' extracts information about the
     fitting procedure from objects of  class  'mle.ghyp'.

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

     ghyp.fit.info(object)

     ghyp.data(object)

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

  object: An object inheriting from class  'ghyp'.

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

     'ghyp.fit.info' returns list with components: 

            'logLikelihood'  The maximized log-likelihood value.
                      'aic'  The Akaike information criterion.
            'fitted.params'  A boolean vector stating which parameters were fitted.
                'converged'  A boolean whether 'optim' converged or not.
                   'n.iter'  The number of iterations.
               'error.code'  Error code from 'optim'.
            'error.message'  Error message from 'optim'.
       'parameter.variance'  Parameter variance (only for univariate fits).

     'ghyp.data' returns 'NULL' if no data is stored within the object,
     a 'vector' if it is an univariate generalized hyperbolic
     distribution and 'matrix' if it is an multivariate generalized
     hyperbolic distribution.

_N_o_t_e:

     'ghyp.fit.info' requires an object of  class 'mle.ghyp'. In the
     univariate case the parameter variance is returned as well. The
     parameter variance is defined as the inverse of the negative
     hesse-matrix computed by 'optim'. Note that this makes sense only
     in the case that the estimates are asymptotically normal
     distributed. 

     The class 'ghyp' contains a 'data' slot. Data can be stored either
     when an object is initialized or via the fitting routines and the
     argument 'save.data'.

_A_u_t_h_o_r(_s):

     David Lthi

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

     'coef', 'mean', 'vcov', 'logLik', 'AIC' for other accessor
     functions,    'fit.ghypmv', 'fit.ghypuv', 'ghyp' for constructor
     functions,  'optim' for possible error messages.

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

       ## multivariate generalized hyperbolic distribution
       ghyp.mv <- ghyp(lambda = 1, alpha.bar = 0.1, mu = rep(0, 2), sigma = diag(rep(1, 2)), 
                       gamma = rep(0, 2), data = matrix(rt(1000, df = 4), ncol = 2))

       ## Get data
       ghyp.data(ghyp.mv)

       ## 'ghyp.fit.info' does only work when the object is of class 'mle.ghyp',
       ## i.e. is created by 'fit.ghypuv' etc.
       mv.fit <- fit.tmv(data = ghyp.data(ghyp.mv), control = list(abs.tol = 1e-3))
       ghyp.fit.info(mv.fit)

