anova.negbin              package:MASS              R Documentation

_L_i_k_e_l_i_h_o_o_d _R_a_t_i_o _T_e_s_t_s _f_o_r _N_e_g_a_t_i_v_e _B_i_n_o_m_i_a_l _G_L_M_s

_D_e_s_c_r_i_p_t_i_o_n:

     Method function to perform sequential likelihood ratio tests for
     Negative Binomial generalized linear models.

_U_s_a_g_e:

     ## S3 method for class 'negbin':
     anova(object, ..., test = "Chisq")

_A_r_g_u_m_e_n_t_s:

  object: Fitted model object of class '"negbin"', inheriting from
          classes '"glm"' and '"lm"', specifying a Negative Binomial
          fitted GLM.  Typically the output of 'glm.nb()'. 

     ...: Zero or more additional fitted model objects of class
          '"negbin"'.  They should form a nested sequence of models,
          but need not be specified in any particular order. 

    test: Argument to match the 'test' argument of 'anova.glm'. Ignored
          (with a warning if changed) if a sequence of two or more
          Negative Binomial fitted model objects is specified, but
          possibly used if only one object is specified. 

_D_e_t_a_i_l_s:

     This function is a method for the generic function 'anova()' for
     class '"negbin"'. It can be invoked by calling 'anova(x)' for an
     object 'x' of the appropriate class, or directly by calling
     'anova.negbin(x)' regardless of the class of the object.

_N_o_t_e:

     If only one fitted model object is specified, a sequential
     analysis of deviance table is given for the fitted model.  The
     'theta' parameter is kept fixed.  If more than one fitted model
     object is specified they must all be of class '"negbin"' and
     likelihood ratio tests are done of each model within the next.  In
     this case 'theta' is assumed to have been re-estimated for each
     model.

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'glm.nb', 'negative.binomial', 'summary.negbin'

_E_x_a_m_p_l_e_s:

     m1 <- glm.nb(Days ~ Eth*Age*Lrn*Sex, quine, link = log)
     m2 <- update(m1, . ~ . - Eth:Age:Lrn:Sex)
     anova(m2, m1)
     anova(m2)

