geeglm                package:geepack                R Documentation

_F_i_t _G_e_n_e_r_a_l_i_z_e_d _E_s_t_i_m_a_t_i_n_g _E_q_u_a_t_i_o_n_s (_G_E_E)

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

     The geeglm function fits generalized estimating equations using
     the 'geese' function of the 'geepack' package for doing the actual
     computations. The virtue of geeglm in relation to geese is, that
     the geeglm "works like" glm and it returns an object which is
     similar to a glm object. Some features of geese are however not
     available in geeglm. An important feature of geeglm, is that an
     anova method exists for these models.

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

     geeglm(formula, family = gaussian, data=parent.frame(), weights, subset, 
                       na.action, start = NULL, etastart, mustart, offset,
                       control = geese.control(...), 
                       method = "glm.fit", x = FALSE, y = TRUE,
                       contrasts = NULL, 
                       id,
                       corstr = "independence",
                       scale.fix = FALSE,
                       scale.value =1,
                       ...) 

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

 formula: See corresponding documentation to 'glm'

  family: See corresponding documentation to 'glm'

    data: See corresponding documentation to 'glm'

 weights: See corresponding documentation to 'glm'

  subset: See corresponding documentation to 'glm'

na.action: See corresponding documentation to 'glm'

   start: See corresponding documentation to 'glm'

etastart: See corresponding documentation to 'glm'

 mustart: See corresponding documentation to 'glm'

  offset: See corresponding documentation to 'glm'

 control: See corresponding documentation to 'glm'

  method: See corresponding documentation to 'glm'

       x: See corresponding documentation to 'glm'

       y: See corresponding documentation to 'glm'

contrasts: See corresponding documentation to 'glm'

      id: a vector which identifies the clusters.  The length of `id'
          should be the same as the number of observations.  Data are
          assumed to be sorted so that observations on a cluster are
          contiguous rows for all entities in the formula. 

  corstr: a character string specifying the correlation structure. The
          following are permitted: '"independence"', '"exchangeable"',
          '"ar1"', '"unstructured"' and '"userdefined"' 

scale.fix: a logical variable; if true, the scale parameter is fixed at
          the value of 'scale.value'. 

scale.value: numeric variable giving the value to which the scale
          parameter should be fixed; used only if 'scale.fix == TRUE'.

     ...: further arguments passed to or from other methods.

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

     ~Describe the value returned If it is a LIST, use 

  comp1 : Description of 'comp1'

  comp2 : Description of 'comp2'

     ...

_W_a_r_n_i_n_g:

     geeglm has not been thoroughly tested. Please report bugs.

_N_o_t_e:

     See the documentation for the 'geese' function  for additional
     information.

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

     Sren Hjsgaard, sorenh@agrsci.dk

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

     Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis
     using generalized linear models. Biometrika, *73* 13-22.

     Prentice, R.L. and Zhao, L.P. (1991). Estimating equations for
     parameters in means and covariances of multivariate discrete and
     continuous responses. Biometrics, *47* 825-839.

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

     'geese', 'glm','anova.geeglm'

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

     data(dietox)
     dietox$Cu     <- as.factor(dietox$Cu)
     mf <- formula(Weight~Cu*(Time+I(Time^2)+I(Time^3)))
     gee1 <- geeglm(mf, data=dietox, id=Pig, family=poisson("identity"),corstr="ar1")
     gee1
     summary(gee1)

     mf2 <- formula(Weight~Cu*Time+I(Time^2)+I(Time^3))
     gee2 <- geeglm(mf2, data=dietox, id=Pig, family=poisson("identity"),corstr="ar1")
     anova(gee2)

