

glmm(repeated)                               R Documentation

_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

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

     `glmm' fits a generalized linear mixed model with a
     random intercept using a normal mixing distribution
     computed by Gauss-Hermite integration. For the normal,
     gamma, and inverse Gaussian distributions, the
     deviances supplied are -2 log likelihood, not the usual
     `glm' deviance; the degrees of freedom take into
     account estimation of the dispersion parameter.

     If weights and/or offset are to be used or the formula
     transforms the some variables, all of the data must be
     supplied in a dataframe.  Because the `glm' function is
     such a hack, if this is not done, weird error messages
     will result.

     na.omit is not allowed.

_U_s_a_g_e_:

     glmm(formula, family=family, data=list(), weights=NULL, offset=NULL,
             nest, delta=1, maxiter=20, points=10, print.level=0,
             control=glm.control(epsilon=0.0001,maxit=10,trace=FALSE))

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

 formula: A symbolic description of the model to be fitted.
          If it contains transformations of the data, a
          dataframe must be supplied.

  family: A description of the error distribution and link
          function to be used in the model; see `family' for
          details.

    data: An optional dataframe containing the variables in
          the model.

 weights: An optional weight vector. If this is used, data
          must be supplied in a data.frame.

  offset: The known component in the linear predictor. If
          this is used, data must be supplied in a
          data.frame. An offset cannot be specified in the
          model formula.

    nest: The variable classifying observations by the unit
          (cluster) upon which they were observed.

   delta: If the response variable has been transformed,
          this is the Jacobian of that transformation, so
          that AICs are comparable.

 maxiter: The maximum number of iterations of the outer loop
          for numerical integration.

  points: The number of points for Gauss-Hermite integration
          of the random effect.

print.level: If set equal to 2, the log probabilities are
          printed out when the underflow error is given.

 control: A list of parameters for controlling the fitting
          process.

_V_a_l_u_e_:

     `glmm' returns a list of class `glmm'

_A_u_t_h_o_r_(_s_)_:

     J.K. Lindsey

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

     `family', `glm', `glm.control', `gnlmm', `gnlr',
     `gnlr3', `fmr'

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

     nest <- gl(5,4)
     y <- rpois(20,5+2*codes(nest))
     # overdispersion model
     glmm(y~1, family=poisson, nest=gl(20,1), points=3)
     # clustered model
     glmm(y~1, family=poisson, nest=nest, points=3)

