hurdletest               package:pscl               R Documentation

_T_e_s_t_i_n_g _f_o_r _t_h_e _P_r_e_s_e_n_c_e _o_f _a _Z_e_r_o _H_u_r_d_l_e

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

     Wald test of the null hypothesis that no zero hurdle is required
     in hurdle regression models for count data.

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

     hurdletest(object, ...)

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

  object: A fitted model object of class '"hurdle"' as returned by
          'hurdle', see details for more information.

     ...: arguments passed to 'linear.hypothesis'.

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

     If the same count distribution and the same set of regressors is
     used in the hurdle model for both, the count component and the
     zero hurdle component, then a test of pairwise equality between
     all coefficients from the two components assesses the null
     hypothesis that no hurdle is needed in the model.

     The function 'hurdletest' is a simple convenience interface to the
     function 'linear.hypothesis' from the 'car' packages that can be
     employed to carry out a Wald test for this  hypothesis.

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

     An object of class '"anova"' as returned by 'linear.hypothesis'.

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

     Achim Zeileis <Achim.Zeileis@R-project.org>

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

     Cameron, A. Colin and Pravin K. Trivedi. 1998. _Regression
     Analysis of Count  Data_. New York: Cambridge University Press.

     Cameron, A. Colin and Pravin K. Trivedi 2005. _Microeconometrics:
     Methods and Applications_. Cambridge: Cambridge University Press.

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

     'hurdle', 'linear.hypothesis'

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

     data("bioChemists", package = "pscl")
     fm <- hurdle(art ~ ., data = bioChemists, dist = "negbin", zero = "negbin")
     if(require("car")) hurdletest(fm)

