glmmPQL                 package:MASS                 R Documentation

_F_i_t _G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _M_i_x_e_d _M_o_d_e_l_s _v_i_a _P_Q_L

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

     Fit a GLMM model with multivariate normal random effects, using
     PQL.

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

     glmmPQL(fixed, random, family, data, correlation, weights,
             control, niter = 10, verbose = TRUE, ...)

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

   fixed: a two-sided linear formula object describing the
          fixed-effects part of the model.

  random: A formula or list of formulae describing the random effects.

  family: a GLM family.

    data: an optional data frame used as the first place to find
          variables in the formulae.

correlation: an optional correlation structure.

 weights: optional case weights as in `glm'.

 control: an optional argument to be passed to `lme'. (This is modified
          in the S version by passed unchanged in the R version.)

   niter: maximum number of iterations.

 verbose: logical: print out record of iterations?

     ...: Further arguments for `lme'.

_D_e_t_a_i_l_s:

     `glmmPQL' is a wrapper to iterative calls to `lme'.  Initial
     values are found by a call to `glm', and then `lme' is applied to
     the working vector, including the BLUPs of the random effects in
     forming the linear predictor.

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

     A object of class `"lme"': see `lmeObject'.

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

     B. D. Ripley

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

     Schall, R. (1991) Estimation in generalized linear models with
     random effects. Biometrika 78, 719-727.

     Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in
     generalized linear mixed models. Journal of the American
     Statistical Association 88, 9-25.

     Wolfinger, R. and O'Connell, M. (1993) Generalized linear mixed
     models: a pseudo-likelihood approach. Journal of Statistical
     Computation and Simulation 48, 233-243.

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

     `lme'

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

     require(nlme)

     data(bacteria)
     summary(glmmPQL(y ~ trt + I(week> 2), random = ~ 1 | ID, 
                       family = binomial, data = bacteria))

