vuong                  package:pscl                  R Documentation

_V_u_o_n_g'_s _n_o_n-_n_e_s_t_e_d _h_y_p_o_t_h_e_s_i_s _t_e_s_t

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

     Compares two models fit to the same data that do not nest.

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

     vuong(m1, m2, digits = getOption("digits"))

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

      m1: model 1, an object inheriting from class 'glm', 'negbin' or
          'zeroinfl'

      m2: model 2, as for model 1

  digits: significant digits in printed result

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

     The Vuong non-nested test is based on a comparison of the
     predicted probabilities of two models that do not nest.  Examples
     include comparisons of zero-inflated count models with their
     non-zero-inflated analogs (e.g., zero-inflated Poisson versus
     ordinary Poisson, or zero-inflated negative-binomial versus
     ordinary negative-binomial).  A large, positive test statistic
     provides evidence of the superiority of model 1 over model 2,
     while a large, negative test statistic is evidence of the
     superiority of model 2 over model 1.  Under the null that the
     models are indistinguishable, the test statistic is asymptotically
     distributed standard normal.

     The function will fail if the models do not contain identical
     values in their respective components named 'y' (the value of the
     response being modeled).

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

     nothing returned, prints the test-statistic and p value and exits
     silently.

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

     Simon Jackman <jackman@stanford.edu>

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

     Vuong, Q.H. 1989. "Likelihood ratio tests for model selection and
     non-nested hypotheses." _Econometrica_. 57:307-333.

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

     data(bioChemists)
     glm1 <- glm(art ~ .,
                 data=bioChemists,
                 family=poisson,trace=TRUE)  ## poisson GLM

     zip <- zeroinfl(count=art ~ .,
                     x = ~ fem + mar + kid5 + phd + ment,
                     z = ~ fem + mar + kid5 + phd + ment,
                     dist="poisson",        
                     data=bioChemists,trace=TRUE) ## zero-infl poisson

     vuong(glm1,zip)

     ## compare negbin with zero-inflated negbin
     require(MASS)
     nb1 <- glm.nb(art ~ .,
                   data=bioChemists,
                   trace=TRUE)
     zinb <- zeroinfl(count=art ~ .,
                      x = ~ fem + mar + kid5 + phd + ment,
                      z = ~ fem + mar + kid5 + phd + ment,
                      dist="negbin",
                      data=bioChemists,trace=TRUE)
     vuong(nb1,zinb)

