GLMM_MCMCifit             package:mixAK             R Documentation

_I_n_i_t_i_a_l (_R_E)_M_L _f_i_t_s _f_o_r _t_h_e _G_L_M_M__M_C_M_C _f_u_n_c_t_i_o_n

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

     This is a help function for 'GLMM_MCMC' function. Besides initial
     (RE)ML fits, the function created variables derived from the
     design matrices.

     THIS FUNCTION IS NOT TO BE CALLED BY ORDINARY USERS.

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

     GLMM_MCMCifit(do.init, na.complete,
         y, dist, id, time, x, z, random.intercept,
         xempty, zempty, Rc, Rd, p, p_fi, q, q_ri, lbeta, dimb)

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

 do.init: logical value indicating whether initial (RE)ML fits should
          be done

na.complete: logical value. If 'TRUE' then the function removes rows
          containing NA's from 'y', 'id', 'x', 'z' whenever there is at
          least one missing value for arbitrary response. If 'FALSE'
          then the missing values are removed response by response,
          i.e., different response variables may have different numbers
          of observations.

       y: see output element 'y' of 'GLMM_MCMCdata' function

    dist: see argument'dist' of 'GLMM_MCMC' function

      id: see output element 'id' of 'GLMM_MCMCdata' function

    time: see argument 'time' of 'GLMM_longitClust'

       x: see output element 'x' of 'GLMM_MCMCdata' function

       z: see output element 'z' of 'GLMM_MCMCdata' function

random.intercept: see output element 'random.intercept' of
          'GLMM_MCMCdata' function

  xempty: see output element 'xempty' of 'GLMM_MCMCdata' function

  zempty: see output element 'zempty' of 'GLMM_MCMCdata' function

      Rc: see output element 'Rc' of 'GLMM_MCMCdata' function

      Rd: see output element 'Rd' of 'GLMM_MCMCdata' function

       p: see output element 'p' of 'GLMM_MCMCdata' function

    p_fi: see output element 'p_fi' of 'GLMM_MCMCdata' function

       q: see output element 'q' of 'GLMM_MCMCdata' function

    q_ri: see output element 'q_ri' of 'GLMM_MCMCdata' function

   lbeta: see output element 'lbeta' of 'GLMM_MCMCdata' function

    dimb: see output element 'dimb' of 'GLMM_MCMCdata' function

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

     A list with the following components (some of them not included if
     'do.init' is 'FALSE'): 

       Y: a list of length R with observations really used in fitting
          process (after removal of missing values)

      ID: a list of length R with id's corresponding to Y

    time: a vector 'time' upon removal of missing values

       x: a list resulting from the original argument x after removal
          of observations with some missing information additionaly,
          intercept column is added if fixed intercept included in the
          model

       z: a list resulting from the original argument z after removal
          of observations with some missing information additionaly,
          intercept column is added if random intercept included in the
          model

       I: number of clusters in the original data (before removing
          NA's)

       n: a list of length R, each component is a vector or length I
          (may contain zeros if some cluster disappears for particular
          response due to NA's)

      Cn: vectorized n

   sumCn: sum(Cn) = total number of observations

    Cy_c: vector with continuous response to be passed to C++, equal to
          0 if there is no continuous response

    Cy_d: vector with discrete response to be passed to C++, equal to 0
          if there is no discrete response

      CX: vector containing X matrices (without ones for possible
          intercept) to be passed to C++, equal to 0 if there are no X
          matrices

      CZ: vector containing Z matrices (without ones for possible
          intercept) to be passed to C++, equal to 0 if there are no Z
          matrices

 iintcpt: data.frame(Est, SE) with estimated intercepts and their SE, R
          rows, row equal to (0, 0) if there is no fixed intercept for
          particular response

  ifixef: a list of length R, each component is equal to 0 if there are
          no fixed effects for particular response, and is equal to
          data.frame(Est, SE) if there are fixed effects

  isigma: vector of length R, equal to 0 for discrete response, equal
          to estimated residual standard deviation for continuous
          response

 iEranef: a list of length R, each component is equal to 0 if there are
          no random effects for particular response, and is equal to
          data.frame(Est, SE) with estimated means of the random
          effects and their std. errors if there are random effects

iSDranef: a list of length R, each component is equal to 0 if there are
          no random effects for particular response, and is equal to a
          vector with estimated standard deviations of the random
          effects if there are random effects

      ib: a list of length R, each component is equal to 0 if there are
          no random effects for particular response, and a matrix with
          EB estimates of random effects shifted by their estimated
          mean if there are random effects

is.intcpt: logical vector of length R

is.fixef: logical vector of length R

is.ranef: logical vector of length R

is.sigma: logical vector of length R

   ibMat: matrix with initial values of random effects (EB estimates
          from (RE)ML fits)

iEranefVec: vector with estimated means of random effects

iSEranefVec: vector with standard errors of estimated means of random
          effects

iSDranefVec: vector with estimated standard deviations of random
          effects

   ibeta: vector with initial values of beta's (including fixed
          intercepts)

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

     Arno&#353t Kom&#225rek arnost.komarek[AT]mff.cuni.cz

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

     'GLMM_MCMC'.

