ordgee                package:geepack                R Documentation

_G_E_E _f_o_r _C_l_u_s_t_e_r_e_d _O_r_d_i_n_a_l _R_e_s_p_o_n_s_e_s

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

     Produces an object of class `geese' which is a Generalized
     Estimating Equation fit of the clustered ordinal data.

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

     ordgee(formula = formula(data), ooffset = NULL, id, waves = NULL,
            data = parent.frame, subset = NULL, na.action = na.omit,
            contrasts = NULL, weights = NULL, z = NULL,
            mean.link = "logit", corstr = "independence",
            control = geese.control(...), b = NA, alpha = NA,
            scale.fix = TRUE, scale.val = 1, int.const = TRUE,
            rev = FALSE,...)

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

 formula: a formula expression as for 'glm', of the form 'response ~
          predictors'. See the documentation of lm and formula for
          details. As for glm, this specifies the linear predictor for
          modelling the mean. A term of the form 'offset(expression)'
          is allowed. 

 ooffset: vector of offset for the odds ratio model.

      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: an integer vector which identifies components in clusters.
          The length of 'waves' should be the same as the number of
          observation. components with the same 'waves' value will have
          the same link functions. 

    data: an optional data frame in which to interpret the variables
          occurring in the 'formula', along with the 'id' and 'n'
          variables. 

  subset: expression saying which subset of the rows of the data should
          be used in the fit.  This can be a logical vector (which is
          replicated to have length equal to the number of
          observations), or a numeric vector indicating which
          observation numbers are to be included, or a character vector
          of the row names to be included. All observations are
          included by default. 

na.action: a function to filter missing data.  For 'gee' only 'na.omit'
          should be used here. 

contrasts: a list giving contrasts for some or all of the factors
          appearing in the model formula.  The elements of the list
          should have the same name as the variable and should be
          either a contrast matrix (specifically, any full-rank matrix
          with as many rows as there are levels in the factor), or else
          a function to compute such a matrix given the number of
          levels. 

 weights: an optional vector of weights to be used in the fitting
          process. The length of 'weights' should be the same as the
          number of observations. 

       z: a design matrix for the odds ratio model. The number of rows
          of z is 

                       c^2 sum n_i(n_i - 1)/2,

          where n_i is the cluster size, and c is the number of
          categories minus 1. 

mean.link: a character string specifying the link function for the
          means. The following are allowed: '"logit"', '"probit"', and
          '"cloglog"'. 

  corstr: a character string specifying the log odds. The following are
          allowed: '"independence"', '"exchangeable"',
          '"unstructured"', and '"userdefined"'. 

 control: a list of iteration and algorithmic constants. See
          'geese.control' for their names and default values. These can
          also be set as arguments to 'geese' itself. 

       b: an initial estimate for the mean parameters.

   alpha: an initial estimate for the odds ratio parameters.

scale.fix: a logical variable indicating if scale is fixed; it is set
          at TRUE currently (it can not be FALSE yet!).

scale.val: this argument is ignored currently.

int.const: a logical variable; if true, the intercepts are constant,
          and if false, the intercepts are different for different
          components in the response.

     rev: a logical variable. For example, for a three level ordered
          response Y = 2, the accumulated indicator is coded as (1, 0,
          0) if true and (0, 1, 1) if false.

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

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

     An object of class '"geese"' representing the fit.

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

     Jun Yan jyan@stat.uiowa.edu

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

     Heagerty, P.J. and Zeger, S.L. (1996) Marginal regression models
     for clustered ordinal measurements. _JASA_, *91* 1024-1036.

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

     'glm', 'lm', 'geese'.

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

     data(respdis)
     resp.l <- reshape(respdis, varying =list(c("y1", "y2", "y3", "y4")),
                       v.names = "resp", direction = "long")
     resp.l <- resp.l[order(resp.l$id, resp.l$time),]
     fit <- ordgee(ordered(resp) ~ trt, id=id, data=resp.l, int.const=FALSE)
     summary(fit)

     data(ohio)
     ohio$resp <- ordered(as.factor(ohio$resp))
     fit <- ordgee(resp ~ age + smoke + age:smoke, id = id, data=ohio)
     summary(fit)

