logitord              package:repeated              R Documentation

_O_r_d_i_n_a_l _R_a_n_d_o_m _E_f_f_e_c_t_s _M_o_d_e_l_s _w_i_t_h _D_r_o_p_o_u_t_s

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

     'logitord' fits an longitudinal proportional odds model in
     discrete time to the ordinal outcomes and a logistic model to the
     probability of dropping out using a common random effect for the
     two.

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

     logitord(y, id, out.ccov=NULL, drop.ccov=NULL, tvcov=NULL,
             out.tvcov=!is.null(tvcov), drop.tvcov=!is.null(tvcov),
             pout, pdrop, prand.out, prand.drop,
             random.out.int=TRUE, random.out.slope=!is.null(tvcov),
             random.drop.int=TRUE, random.drop.slope=!is.null(tvcov),
             binom.mix=5, fcalls=900, eps=0.0001, print.level=0)

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

       y: A vector of binary or ordinal responses with levels 1 to k
          and 0 indicating drop-out.

      id: Identification number for each individual.

out.ccov: A vector, matrix, or model formula of time-constant
          covariates for the outcome regression, with variables having
          the same length as 'y'.

drop.ccov: A vector, matrix, or model formula of time-constant
          covariates for the drop-out regression, with variables having
          the same length as 'y'.

   tvcov: One time-varying covariate vector.

out.tvcov: Include the time-varying covariate in the outcome
          regression.

drop.tvcov: Include the time-varying covariate in the drop-out
          regression.

    pout: Initial estimates of the outcome regression coefficients,
          with length equal to the number of levels of the response
          plus the number of covariates minus one.

   pdrop: Initial estimates of the drop-out regression coefficients,
          with length equal to one plus the number of covariates.

prand.out: Optional initial estimates of the outcome random parameters.

prand.drop: Optional initial estimates of the drop-out random
          parameters.

random.out.int: If TRUE, the outcome intercept is random.

random.out.slope: If TRUE, the slope of the time-varying covariate is
          random for the outcome regression (only possible if a
          time-varying covariate is supplied and if out.tvcov and
          random.out.int are TRUE).

random.drop.int: If TRUE, the drop-out intercept is random.

random.drop.slope: If TRUE, the slope of the time-varying covariate is
          random for the drop-out regression (only possible if a
          time-varying covariate is supplied and if drop.tvcov and
          random.drop.int are TRUE).

binom.mix: The total in the binomial distribution used to approximate
          the normal mixing distribution.

  fcalls: Number of function calls allowed.

     eps: Convergence criterion.

print.level: If 1, the iterations are printed out.

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

     A list of class 'logitord' is returned.

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

     T.R. Ten Have and J.K. Lindsey

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

     Ten Have, T.R., Kunselman, A.R., Pulkstenis, E.P. and Landis, J.R.
     (1998) Biometrics 54, 367-383, for the binary case.

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

     'nordr', 'ordglm'.

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

     y <- trunc(runif(20,max=4))
     id <- gl(4,5)
     age <- rpois(20,20)
     times <- rep(1:5,4)
     logitord(y, id=id, out.ccov=~age, drop.ccov=age, pout=c(1,0,0),
             pdrop=c(1,0))
     logitord(y, id, tvcov=times, pout=c(1,0,0), pdrop=c(1,0))

