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.fit' function  of the 'geepack' package for doing the
     actual computations. geeglm has a syntax similar to  glm and
     returns an object similar to a glm object.  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, waves=NULL, zcor=NULL, 
                       corstr = "independence",
                       scale.fix = FALSE,
                       scale.value =1, std.err="san.se",
                       ...) 

_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: No action is taken. Indeed geeglm only works on complete
          data.

   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. 

   waves: Wariable specifying the ordering of repeated mesurements on
          the same unit.  Also used in connection with missing values.
          See examples below.

    zcor: Used for entering a user defined working correlation
          structure.

  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'.

 std.err: Type of standard error to be calculated. Defualt 'san.se' is
          the usual robust estimate.  Other options are 'jack': if
          approximate jackknife variance estimate should be computed.
          'j1s': if 1-step jackknife variance estimate should be
          computed. 'fij': logical indicating if fully iterated
          jackknife variance estimate should be computed. 

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

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

     An object of type 'geeglm'

_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. geeglm only works for complete data. Thus if there
     are NA's in data you can specify data=na.omit(mydata).

_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)

