CompetingRiskFrailtySurvfitCreatepackage:CompetingRiskFrailtyR Documentation

_C_o_m_p_e_t_i_n_g _R_i_s_k _M_o_d_e_l _w_i_t_h _F_r_a_i_l_t_i_e_s _a_n_d _S_m_o_o_t_h _V_a_r_y_i_n_g _C_o_e_f_f_i_c_i_e_n_t_s

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

     This function offers a strategy of fitting the competing risks
     models with frailties for estimating a hazard function.  The
     effects of covariates are modelled as varying coefficients. For
     flexible smoothing of the effects truncated polynomial splines are
     employed. The frailties (or unobserved heterogenities) of the
     (clustered) observations are also modelled in a flexible way by
     employing a mixture of gamma distributions. The estimation routine
     is based on the EM-algorithm.

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

     CompetingRiskFrailtySurvfitCreate(formula=formula, data=data, na.action=na.fail,
                                              control=control, risk.names=risk.names)

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

 formula: a symbolic description of the model to be fit. The details of
          the model specification are given below.

    data: an optional data frame containing variables in the model. If
          not supplied the handling of missing values will be
          accordingly  to the value of 'na.action'.

na.action: an optional parameter for handling with missing data.

 control: an optional list argument with control parameters needed for
          optimization routine. If not specified, default values from
          'CompetingRiskFrailtySurvfitControl' will be taken; otherwise
          the values,  which are defined by user, will overwrite the
          defaults.

risk.names: a character vector containing the names of competing risks.
          Its elements should be in the order,  in which the columns
          describing the resulting states (or risks) have been supplied
          as a 'status' parameter in the call of the
          'CompetingRiskFrailtySurv' function. 

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

     A formula argument can be specified in differentt ways. The
     response or left part of the formula can be either an object of
     class  'CompetingRiskFrailtySurv' or it can be a call to the
     'CompetingRiskFrailtySurv' function with its arguments specified
     in it.  The right part may be left unspecified (without '~') or it
     can be of '~1' type. In this case no covariates are supplied to
     the model, hence only baseline hazard will be adopted.  In the
     case with covariates these must be supplied explicitly through
     their names in a standard way. In all cases the 'formula' argument
      must be supplied.

     If factor variables are to be considered, their reference
     categories should be specified bevor applied for optimization. 

     A 'control' argument can be absent, in this case the defaults from
     the 'CompetingRiskFrailtySurvfitControl' will be taken; or it can
     be either a list with some or all components of it specified by
     their names with according values, or it can be a call to the
     'CompetingRiskFrailtySurvfitControl' function with parameters
     specified in it.

     The most of computation and optimization is implemented in the
     "internal" function 'CompetingRiskFrailtyOptim', which is  in the
     body of 'CompetingRiskFrailtySurvfitCreate'. 

     The working spline bases are ones consisted from truncated
     polynomials. Other alternative could be B-splines, but has not
     been implemented yet and might be considered in the following
     update of the package.

     The computation procedure can take considerable time, depending on
     data supplied. All evaluations are written in R-code, so no
     external program code like C or Fortran has been used. We advice
     initialy to set the number of iterations not to be too large, and
     eventually increase it if needed.

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

     An object of class 'CompetingRiskFrailtySurvit'. It has methods
     for 'print' and 'plot'.

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

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

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

     Kauermann 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:

     'CompetingRiskFrailtySurv', 'CompetingRiskFrailtySurvfitControl',
     'CompetingRiskFrailtySurvfitObject'

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

     #data(unemployed.comp.risk)
     #ID<-unemployed.comp.risk$id
     #surv.time<-unemployed.comp.risk$surv.time
     #status<-unemployed.comp.risk[,3:4]
     #my.Surv<-CompetingRiskFrailtySurv(ID=ID,surv.time=surv.time,status=status)
     #my.Survfit<-CompetingRiskFrailtySurvfitCreate(my.Surv~nationality+gender+age+training+university,
     #                                              data=unemployed.comp.risk,risk.names=c("full.time","part.time"))
     #evaluation takes ca. 25 minuts

