vgam-class               package:VGAM               R Documentation

_C_l_a_s_s "_v_g_a_m"

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

     Vector generalized additive models.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'vgam(...)'.

_S_l_o_t_s:

     '_n_l._c_h_i_s_q': Object of class '"numeric"'.  Nonlinear chi-squared
          values. 

     '_n_l._d_f': Object of class '"numeric"'.  Nonlinear chi-squared
          degrees of freedom values. 

     '_s_p_a_r': Object of class '"numeric"'  containing the (scaled)
          smoothing parameters. 

     '_s._x_a_r_g_u_m_e_n_t': Object of class '"character"' holding the variable
          name of any 's()' terms. 

     '_v_a_r': Object of class '"matrix"' holding approximate pointwise
          standard error information. 

     '_B_s_p_l_i_n_e': Object of class '"list"' holding the scaled (internal
          and boundary) knots, and the fitted B-spline coefficients.
          These are used for prediction. 

     '_e_x_t_r_a': Object of class '"list"'; the 'extra' argument on entry
          to 'vglm'. This contains any extra information that might be
          needed by the family function. 

     '_f_a_m_i_l_y': Object of class '"vglmff"'. The family function.  

     '_i_t_e_r': Object of class '"numeric"'. The number of IRLS iterations
          used. 

     '_p_r_e_d_i_c_t_o_r_s': Object of class '"matrix"'  with M columns which
          holds the M linear predictors. 

     '_a_s_s_i_g_n': Object of class '"list"', from class ' "vlm"'. This
          named list gives information matching the columns and the
          (LM) model matrix terms.

     '_c_a_l_l': Object of class '"call"', from class ' "vlm"'. The matched
          call.

     '_c_o_e_f_f_i_c_i_e_n_t_s': Object of class '"numeric"', from class ' "vlm"'.
          A named vector of coefficients.

     '_c_o_n_s_t_r_a_i_n_t_s': Object of class '"list"', from class ' "vlm"'. A
          named list of constraint matrices used in the fitting.

     '_c_o_n_t_r_a_s_t_s': Object of class '"list"', from class ' "vlm"'. The
          contrasts used (if any).

     '_c_o_n_t_r_o_l': Object of class '"list"', from class ' "vlm"'. A list
          of parameters for controlling the fitting process. See
          'vglm.control' for details.

     '_c_r_i_t_e_r_i_o_n': Object of class '"list"', from class ' "vlm"'. List
          of convergence criterion evaluated at the final IRLS
          iteration.

     '_d_f._r_e_s_i_d_u_a_l': Object of class '"numeric"', from class ' "vlm"'.
          The residual degrees of freedom.

     '_d_f._t_o_t_a_l': Object of class '"numeric"', from class ' "vlm"'. The
          total degrees of freedom.

     '_d_i_s_p_e_r_s_i_o_n': Object of class '"numeric"', from class ' "vlm"'.
          The scaling parameter.

     '_e_f_f_e_c_t_s': Object of class '"numeric"', from class ' "vlm"'. The
          effects.

     '_f_i_t_t_e_d._v_a_l_u_e_s': Object of class '"matrix"', from class ' "vlm"'.
          The fitted values. This may be missing or consist entirely of
          'NA's, e.g., the Cauchy model. 

     '_m_i_s_c': Object of class '"list"', from class ' "vlm"'. A named
          list to hold miscellaneous parameters.

     '_m_o_d_e_l': Object of class '"data.frame"', from class ' "vlm"'. The
          model frame.

     '_n_a._a_c_t_i_o_n': Object of class '"list"', from class ' "vlm"'. A list
          holding information about missing values.

     '_o_f_f_s_e_t': Object of class '"matrix"', from class ' "vlm"'. If
          non-zero, a M-column matrix of offsets.

     '_p_o_s_t': Object of class '"list"', from class ' "vlm"' where
          post-analysis results may be put.

     '_p_r_e_p_l_o_t': Object of class '"list"', from class ' "vlm"' used by
          'plotvgam'; the plotting parameters may be put here.

     '_p_r_i_o_r._w_e_i_g_h_t_s': Object of class '"numeric"', from class ' "vlm"' 
          holding the initially supplied weights.

     '_q_r': Object of class '"list"', from class ' "vlm"'. QR
          decomposition at the final iteration. 

     '_R': Object of class '"matrix"', from class ' "vlm"'. The *R*
          matrix in the QR decomposition used in the fitting. 

     '_r_a_n_k': Object of class '"integer"', from class ' "vlm"'.
          Numerical rank of the fitted model.

     '_r_e_s_i_d_u_a_l_s': Object of class '"matrix"', from class ' "vlm"'. The
          _working_ residuals at the final IRLS iteration.

     '_r_s_s': Object of class '"numeric"', from class ' "vlm"'. Residual
          sum of squares at the final IRLS iteration with the adjusted
          dependent vectors and weight matrices.

     '_s_m_a_r_t._p_r_e_d_i_c_t_i_o_n': Object of class '"list"', from class ' "vlm"'.
          A list of data-dependent parameters (if any) that are used by
          smart prediction.

     '_t_e_r_m_s': Object of class '"list"', from class ' "vlm"'. The
          'terms' object used.

     '_w_e_i_g_h_t_s': Object of class '"matrix"', from class ' "vlm"'. The
          weight matrices at the final IRLS iteration. This is in
          matrix-band form.

     '_x': Object of class '"matrix"', from class ' "vlm"'. The model
          matrix (LM, not VGLM).

     '_x_l_e_v_e_l_s': Object of class '"list"', from class ' "vlm"'. The
          levels of the factors, if any, used in fitting.

     '_y': Object of class '"matrix"', from class ' "vlm"'. The
          response, in matrix form.

_E_x_t_e_n_d_s:

     Class '"vglm"', directly. Class '"vlm"', by class "vglm".

_M_e_t_h_o_d_s:

     _c_d_f 'signature(object = "vglm")': cumulative distribution
          function. Useful for quantile regression and extreme value
          data models.

     _d_e_p_l_o_t 'signature(object = "vglm")': density plot. Useful for
          quantile regression models.

_d_e_v_i_a_n_c_e 'signature(object = "vglm")': deviance of the model (where
     applicable). 

_p_l_o_t 'signature(x = "vglm")': diagnostic plots. 

_p_r_e_d_i_c_t 'signature(object = "vglm")': extract the additive predictors
     or predict the additive predictors at a new data frame.

_p_r_i_n_t 'signature(x = "vglm")': short summary of the object. 

_q_t_p_l_o_t 'signature(object = "vglm")': quantile plot (only applicable to
     some models). 

_r_e_s_i_d 'signature(object = "vglm")': residuals. There are various types
     of these. 

_r_e_s_i_d_u_a_l_s 'signature(object = "vglm")': residuals. Shorthand for
     'resid'. 

_r_l_p_l_o_t 'signature(object = "vglm")': return level plot. Useful for
     extreme value data models.

_s_u_m_m_a_r_y 'signature(object = "vglm")': a more detailed summary of the
     object. 

_N_o_t_e:

     VGAMs have all the slots that 'vglm' objects have ('vglm-class'),
     plus the first few slots described in the section above.

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

     Thomas W. Yee

_R_e_f_e_r_e_n_c_e_s:

     Yee, T. W. and Wild, C. J. (1996) Vector generalized additive
     models. _Journal of the Royal Statistical Society, Series B,
     Methodological_, *58*, 481-493.

     <URL: http://www.stat.auckland.ac.nz/~yee>

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

     'vgam.control', 'vglm', 's', 'vglm-class', 'vglmff-class'.

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

     # Fit a nonparametric proportional odds model
     data(pneumo)
     pneumo = transform(pneumo, let=log(exposure.time))
     vgam(cbind(normal, mild, severe) ~ s(let),
          cumulative(parallel=TRUE), pneumo)

