GLMM                  package:lme4                  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
     Penalized Quasi-Likelihood.

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

     GLMM(formula, family, data, random, control, niter, method, verbose, ...)

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

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

  family: a GLM family, see `glm'. 

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

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

 control: an optional argument to be passed to `lme'. 

   niter: maximum number of PQL iterations.  Default is 20. 

  method: character: Estimation method to be used.  Possible values are
          `"PQL"', the default, or `"Laplace"'. `"PQL"' provides
          penalized quasi-likelihood estimates. `"Laplace"' provides
          PQL estimation followed by optimization of the second-order
          Laplacian approximation to the marginal log-likelihood. 

 verbose: logical: print out record of iterations?  Default is `FALSE'. 

     ...: Optional further arguments such as `subset' and `na.action'. 

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

     A object of class `"glmm"': see `reStruct-class'.

_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:

     library(lme4)
     data(guImmun)
     fm1 = GLMM(immun ~ kid2p + mom25p + ord + ethn +
                       momEd + husEd + momWork + rural + pcInd81,
               data = guImmun, family = binomial,
               random = ~1|comm/mom)
     summary(fm1)
     fm2 = GLMM(immun ~ kid2p + mom25p + ord + ethn +
                       momEd + husEd + momWork + rural + pcInd81,
               data = guImmun, family = binomial,
               random = ~1|comm, method = 'Laplace')
     summary(fm2)

