glmm-class               package:lme4               R Documentation

_C_l_a_s_s "_g_l_m_m"

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

     A generalized linear mixed-effects model.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form `new("glmm", ...)' but
     more commonly they are created by calls to `GLMM'.

_S_l_o_t_s:

     `_f_a_m_i_l_y': Object of class `"family"', specifying the error
          distribution and the link function used in the model.

     `_o_r_i_g_y': Object of class `"numeric"', the original response.

     `_n': Object of class `"numeric"', if the family is `binomial', the
          number of observations for each data point.

     `_p_r_i_o_r._w_e_i_g_h_t_s': Object of class `"numeric"', weights used when
          creating the model.frame.

     `_i_n_i_t._w_e_i_g_h_t_s': Object of class `"numeric"', weights from a fixed
          effects generalized linear model for the data.

     `_i_n_i_t._y': Object of class `"numeric"', the linear predictor from a
          fixed effects generalized linear model for the data.

     `_m_e_t_h_o_d': Object of class `"character"', the method used to fit
          the generalized linear mixed model.

     `_r_e_S_t_r_u_c_t': Object of class `"reStruct"', from class `"lme"', the
          random-effects structure for the model.

     `_f_r_a_m_e': Object of class `"data.frame"', from class `"lme"', the
          model.frame used to fit the model.

     `_n_a._a_c_t_i_o_n': Object of class `"ANY"', from class `"lme"', the
          `na.action' argument used when creating the `model.frame'.

     `_f_i_t_t_e_d': Object of class `"numeric"', from class `"lme"', the
          fitted values in the linear predictor scale.

     `_c_a_l_l': Object of class `"call"', from class `"lme"', the function
          call used to create the object.

_E_x_t_e_n_d_s:

     Class `"lme"', directly.

_M_e_t_h_o_d_s:

     _L_M_E_o_p_t_i_m_i_z_e<- `signature(x = "glmm", value = "list")': optimize
          the PQL approximation to the log-likelihood.

     _V_a_r_C_o_r_r `signature(x = "glmm")': Extract variance and correlation
          components.

     _f_i_x_e_f<- `signature(object = "glmm", value = "numeric")': assign
          the fixed effects (used for `method = "Laplace"').

     _g_e_t_R_e_s_p_o_n_s_e `signature(object = "glmm")': extract the response.

     _l_o_g_L_i_k `signature(object = "glmm")': extract the (approximate)
          log-likelihood.

     _s_h_o_w `signature(object = "glmm")': show the object.

     _s_u_m_m_a_r_y `signature(object = "glmm")': summarize the object.

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

     Saikat DebRoy saikat@stat.wisc.edu and Douglas Bates
     bates@stat.wisc.edu

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

     library(lme4)
     data(guImmun)
     # This returns an object of class glmm
     fm = GLMM(immun ~ kid2p + mom25p + ord + ethn +
                       momEd + husEd + momWork + rural + pcInd81,
               data = guImmun, family = binomial,
               random = ~1|comm/mom)
     fm

