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, ...)

_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'. 

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

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

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

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

     Additional arguments, some of them standard in model-fitting
     functions, can be passed to 'GLMM'.

     _s_u_b_s_e_t an optional expression indicating the subset of the rows of
          'data' that should be used in the fit. This can be a logical
          vector, 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.

     _m_e_t_h_o_d 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.

     _n_a._a_c_t_i_o_n a function that indicates what should happen when the
          data contain 'NA's.  The default action ('na.fail') causes
          'lme' to print an error message and terminate if there are
          any incomplete observations.

     _c_o_n_t_r_o_l an optional argument to be passed to 'lme'.

     _m_o_d_e_l, _x logicals.  If 'TRUE' the corresponding components of the
          fit (the model frame, the model matrices) are returned.

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

     An object of class '"lme"': see 'ssclme-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:

     data(guImmun)
     fm1 <-
         GLMM(immun ~ kid2p + mom25p + ord + ethn +
                      momEd + husEd + momWork + rural + pcInd81,
              family = binomial, data = guImmun, random = ~1|comm)
     summary(fm1)

