CompetingRiskFrailtySurvfitControlpackage:CompetingRiskFrailtyR Documentation

_C_o_n_t_r_o_l _V_a_l_u_e_s _f_o_r _F_i_t_t_i_n_g _o_f _C_o_m_p_e_t_i_n_g _R_i_s_k_s _w_i_t_h _F_r_a_i_l_t_i_e_s _M_o_d_e_l

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

     The values supplied in the call of
     'CompetingRiskFrailtySurvfitCreate' function will replace the
     defaults, and a list with all possible arguments is returned. The
     returned list is used as the 'control' argument to the
     'CompetingRiskFrailtySurvfitCreate' function.

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

     CompetingRiskFrailtySurvfitControl(niter.EM=50, niter.epoch = 2, tol.epoch = 1e-08, tol.variance = 1e-08,
                                        tol.frailty = 1e-06, print.penalty.mixture=TRUE,print.EM=TRUE,
                                        print.estimates=FALSE,print.log.lik=TRUE,...)

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

niter.EM: maximum number of the (outer) EM-iterations.

niter.epoch: maximum number of the (inner) iterations in optimization
          for  varying coefficients parameters theta and penalty
          parameters of their random parts, within an EM-iteration.

tol.epoch: tolerance for the convergence criterion for the fixed and
          random parameters of the varying coefficients.

tol.variance: tolerance for the convergence criterion for the penalty
          values of varying coefficients.

tol.frailty: tolerance for the the convergence criterion for the
          frailty terms.

print.penalty.mixture: logical value for printing the value of the
          penalty parameter from the specified grid of values.

print.EM: logical value for printing the current number of an
          EM-itertion.

print.estimates: logical value for printing the estimates of the fixed
          parameters theta and penalties of varying coefficients after
          the last EM-iteration.

print.log.lik: logical value for printing the marginal log likelihood
          of the model in each EM-iteration.

     ...: other parameters which can only be: 'num.knots' for the
          number of spline knots for  survival time. If specified it is
          a vector of integer values of the length equal to the number
          of competing risks. If not specified, the optimal values will
          be defined internally. 

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

     The defaults or user specified values are applied as the 'control'
     argument in the call of the 'CompetingRiskFrailtySurvfitCreate'
     function. It can be an (empty) list object or a call to the 
     'CompetingRiskFrailtySurvfitControl' function itself, whether or
     not with supplied arguments to be changed from their default
     values.

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

     a list with components for each of the possible arguments.

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

     Pavel Khomski <pkhomski@wiwi.uni-bielefeld.de>

_R_e_f_e_r_e_n_c_e_s:

     Kauerman G. and Khomski P. (2006). _Full time or part time
     reemployment: a competing risk model with frailties and smooth
     effects using a penalty based approach_, to appear.

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

     'CompetingRiskFrailtySurvfitCreate'

