tsum                  package:hett                  R Documentation

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_h_e_t_e_r_o_s_c_e_d_a_s_t_i_c _t _m_o_d_e_l

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

     Summarizes the location _or_ scale components of a heteroscedastic
     _t_ model

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

     tsum(object, dispersion = NULL, correlation = FALSE,
                  symbolic.cor = FALSE, ...)

     ## S3 method for class 'tsum':
     print(x, digits = max(3, getOption("digits") - 3), symbolic.cor =
           x$symbolic.cor, signif.stars = getOption("show.signif.stars"),
           scale = TRUE, ...)

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

  object: either the location _or_ scale object created by fitting a
          heteroscedastic t object with 'tlm'

       x: an object of class "'tsum'"

dispersion: 1 if summarizing the location model; 2 if summarizing the
          scale model (see Details)

correlation: logical; if 'TRUE', the correlation matrix of the
          estimated parameters is returned and printed.

  digits: the number of significant digits to be printed.

symbolic.cor: logical. If 'TRUE', print the correlations in a symbolic
          form (see `symnum') rather than as numbers.

signif.stars: logical. if 'TRUE', "significance stars" are printed for
          each coefficient.

   scale: logical. If 'TRUE' then the dispersion is known in advance
          (2), and is printed accordingly.

     ...: further arguments passed to or from other methods.

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

     The argument supplied to 'dispersion' must be either 1 (location
     model) or 2 (scale model). The reason for this is because the
     fitting of the model has already scaled the covariance matrix for
     the location coefficients. Hence the scaled and unscaled versions
     of covariance matrix for the location model are identical.     

     This function will not be generally called by the user as it will
     only summarize the location or scale model but not both. Instead
     the user should refer to 'summary.tlm' to print a summary of both
     models.

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

     'tsum' returns an object of class "'tsum'", a list with components

    call: the component from 'object'

df.residual: the component from 'object'

coefficients: the matrix of coefficients, standard errors, z-values and
          p-values

dispersion: the supplied dispersion argument

      df: a 2-vector of the rank of the model and the number of
          residual degrees of freedom

cov.unscaled: the unscaled ('dispersion = 1') estimated covariance
          matrix of the estimated coefficients

cov.scaled: ditto, scaled by 'dispersion'

correlation: (only if 'correlation' is true.)  The estimated
          correlations of the estimated coefficients

symbolic.cor: (only if 'correlation' is true.)  The value of the
          argument 'symbolic.cor'

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

     'summary.tlm', 'tlm'

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

     data(mm, package = "hett")
     attach(mm)
     tfit <- tlm(m.marietta ~ CRSP, ~ CRSP, data = mm, start = list(dof = 3),
     estDof = TRUE) 
     tsum(tfit$loc.fit, dispersion = 1) 

