anova.coxph             package:survival             R Documentation

_A_n_a_l_y_s_i_s _o_f _D_e_v_i_a_n_c_e _f_o_r _a _C_o_x _m_o_d_e_l.

_D_e_s_c_r_i_p_t_i_o_n:

     Compute an analysis of deviance table for one or more Cox model
     fits.

_U_s_a_g_e:

     ## S3 method for class 'coxph':
     anova(object, ...,  test = NULL)

_A_r_g_u_m_e_n_t_s:

  object: An object of class 'coxph'

     ...: Further 'coxph' objects

    test: a character string, (partially) matching one of '"Chisq"',
          '"F"' or '"Cp"'. See 'stat.anova'.

_D_e_t_a_i_l_s:

     Specifying a single object gives a sequential analysis of deviance
     table for that fit.  That is, the reductions in the residual
     deviance as each term of the formula is added in turn are given in
     as the rows of a table, plus the residual deviances themselves.

     If more than one object is specified, the table has a row for the
     residual degrees of freedom and deviance for each model. For all
     but the first model, the change in degrees of freedom and deviance
     is also given. (This only make statistical sense if the models are
     nested.)  It is conventional to list the models from smallest to
     largest, but this is up to the user.

     The table will optionally contain test statistics (and P values)
     comparing the reduction in deviance for the row to the residuals.
     Mallows' Cp statistic is the residual deviance plus twice the 
     residual degrees of freedom, which is closely related to AIC.
     Don't use 'test=F', it doesn't make sense for a Cox model.

_V_a_l_u_e:

     An object of class '"anova"' inheriting from class '"data.frame"'.

_W_a_r_n_i_n_g:

     The comparison between two or more models by 'anova' or will only
     be valid if they are fitted to the same dataset. This may be a
     problem if there are missing values.

_S_e_e _A_l_s_o:

     'coxph', 'anova'.

_E_x_a_m_p_l_e_s:

     fit <- coxph(Surv(futime, fustat) ~ resid.ds *rx + ecog.ps, data = ovarian) 
     anova(fit)
     anova(fit,test="Chisq")
     fit2 <- coxph(Surv(futime, fustat) ~ resid.ds +rx + ecog.ps, data
     =ovarian)
     anova(fit2,fit)
      

