

binnest(repeated)                            R Documentation

_B_i_n_a_r_y _R_a_n_d_o_m _E_f_f_e_c_t_s _M_o_d_e_l _w_i_t_h _T_w_o _L_e_v_e_l_s _o_f _N_e_s_t_i_n_g

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

     `binnest' is designed to handle binary and binomial
     data with two levels of nesting. The first level is the
     individual and the second will consist of clusters
     within individuals.

     The variance components at the two levels can only
     depend on the covariates if `response' has class,
     `repeated'.

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

     binnest(response, totals=NULL, nest=NULL, ccov=NULL, tvcov=NULL,
             mu=~1, re1=~1, re2=~1, preg=NULL, pre1=NULL, pre2=NULL,
             binom.mix=c(10,10), binom.prob=c(0.5,0.5), fcalls=900,
             eps=0.01, print.level=0)

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

response: A list of three column matrices with counts, cor-
          responding totals (not necessary if the response
          is binary), and (second-level) nesting indicator
          for each individual, one matrix or dataframe of
          such counts, or an object of class, response (cre-
          ated by `restovec') or repeated (created by
          `rmna').

  totals: If `response' is a matrix or dataframe, a corre-
          sponding matrix or dataframe of totals (not neces-
          sary if the response is binary). Ignored other-
          wise.

    nest: If `response' is a matrix or dataframe, a corre-
          sponding matrix or dataframe of nesting indica-
          tors. Ignored otherwise.

    ccov: If `response' is a matrix, dataframe, list, or
          object of class, `response', a matrix of time-con-
          stant covariates or an object of class, `tccov'
          (created by `tcctomat'). All of these covariates
          are used in the fixed effects part of the model.
          Ignored if response has class, `repeated'.

   tvcov: If `response' is a matrix, dataframe, list, or
          object of class, `response', an object of class,
          `tvcov' (created by `tvctomat'). All of these
          covariates are used in the fixed effects part of
          the model. Ignored if response has class,
          `repeated'.

      mu: If `response' has class, `repeated', a formula
          beginning with ~, specifying a linear regression
          function for the fixed effects, in the Wilkinson
          and Rogers notation, containing selected covari-
          ates in the response object. (A logit link is
          assumed.)

     re1: If `response' has class, `repeated', a formula
          beginning with ~, specifying a linear regression
          function for the variance of the first level of
          nesting, in the Wilkinson and Rogers notation,
          containing selected covariates in the response
          object. If NULL, a random effect is not fitted at
          this level. (A log link is assumed.)

     re2: If `response' has class, `repeated', a formula
          beginning with ~, specifying a linear regression
          function for the variance of the second level of
          nesting, in the Wilkinson and Rogers notation,
          containing selected covariates in the response
          object. If NULL, a random effect is not fitted at
          this level. (A log link is assumed.)

    preg: Initial parameter estimates for the fixed effect
          regression model: either the model specified by
          `mu' or else the intercept plus one for each
          covariate in `ccov' and `tvcov'.

    pre1: Initial parameter estimates for the first level of
          nesting variance model: either the model specified
          by `re1' or just the intercept. If NULL, a random
          effect is not fitted at this level.

    pre2: Initial parameter estimates for the second level
          of nesting variance model: either the model speci-
          fied by `re1' or just the intercept. If NULL, a
          random effect is not fitted at this level.

binom.mix: A vector of two values giving the totals for the
          binomial distributions used as the mixing distri-
          butions at the two levels of nesting.

binom.prob: A vector of two values giving the probabilities
          in the binomial distributions used as the mixing
          distributions at the two levels of nesting. If
          they are 0.5, the mixing distributions approximate
          normal mixing distributions; otherwise, they are
          skewed.

  fcalls: Number of function calls allowed.

     eps: Convergence criterion.

print.level: If 1, the iterations are printed out.

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

     A list of classes `binnest' is returned.

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

     T.R. Ten Have and J.K. Lindsey

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

     `gar', `read.list', `restovec', `rmna', `tcctomat',
     `tvctomat'.

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

     y <- rbind(matrix(rbinom(20,1,0.6), ncol=4),
             matrix(rbinom(20,1,0.4), ncol=4))
     resp <- restovec(y, nest=1:4, times=F)
     ccov <- tcctomat(c(rep(0,5),rep(1,5)), name="treatment")
     reps <- rmna(resp, ccov=ccov)
     # two random effects
     binnest(reps, mu=~treatment, preg=c(1,0), pre1=1, pre2=1)
     # first level random effect only
     binnest(reps, mu=~treatment, preg=c(1,-1), pre1=1)
     # second level random effect only
     binnest(reps, mu=~treatment, preg=c(1,-1), pre2=1)

