pR2                   package:pscl                   R Documentation

_c_o_m_p_u_t_e _v_a_r_i_o_u_s _p_s_e_d_u_o-_R_2 _m_e_a_s_u_r_e_s

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

     compute various pseduo-R2 measures for various GLMs

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

     pR2(object, ...)

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

  object: a fitted model object, for now of class 'glm', 'polr', or
          'mulitnom'

     ...: additional arguments to be passed to or from functions

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

     Numerous pseudo r-squared measures have been proposed for
     generalized linear models, involving a comparison of the
     log-likelihood for the fitted model against the log-likelihood of
     a null/restricted model with no predictors, normalized to run from
     zero to one as the fitted model provides a better fit to the data
     (providing a rough analogue to the computation of r-squared in a
     linear regression).

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

     A vector of length 6 containing 

     llh: The log-likelihood from the fitted model

 llhNull: The log-likelihood from the intercept-only restricted model

      G2: Minus two times the difference in the log-likelihoods

McFadden: McFadden's pseudo r-squared

    r2ML: Maximum likelihood pseudo r-squared

    r2CU: Cragg and Uhler's pseudo r-squared

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

     Simon Jackman jackman@stanford.edu

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

     Long, J. Scott. 1997. _Regression Models for Categorical and
     Limited Dependent Variables_.  Sage.  pp104-106.

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

     'extractAIC', 'logLik'

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

     data(admit)
     require(MASS)
     ## ordered probit model
     op1 <- polr(score ~ gre.quant + gre.verbal + ap + pt + female,
                 Hess=TRUE,
                 data=admit,
                 method="probit")
     pR2(op1)   

