optimCoxBoostPenalty        package:CoxBoost        R Documentation

_C_o_a_r_s_e _l_i_n_e _s_e_a_r_c_h _f_o_r _a_d_e_q_u_a_t_e _p_e_n_a_l_t_y _p_a_r_a_m_e_t_e_r

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

     This routine helps in finding a penalty value that leads to an
     ``optimal'' number of boosting steps for CoxBoost, determined by
     cross-validation, that is not too small/in a specified range.

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

     optimCoxBoostPenalty(time,status,x,minstepno=50,maxstepno=200,
                          start.penalty=500,iter.max=10,upper.margin=0.05,
                          trace=FALSE,...)

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

    time: vector of length 'n' specifying the observed times.

  status: censoring indicator, i.e., vector of length 'n' with entries
          '0' for censored observations and '1' for uncensored
          observations.

       x: 'n * p' matrix of covariates.

minstepno, maxstepno: range of boosting steps in which the ``optimal''
          number of boosting steps is wanted to be.

start.penalty: start value for the search for the appropriate penalty.

iter.max: maximum number of search iterations.

upper.margin: specifies the fraction of 'maxstepno' which is used as an
          upper margin in which a cross-validation minimum is not taken
          to be one. This is necessary because of random fluctuations
          of cross-validated partial log-likelihood.

   trace: logical value indicating whether information on progress
          should be printed.

     ...: miscellaneous parameters for 'cv.CoxBoost'.

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

     The penalty parameter for 'CoxBoost' has to be chosen only very
     coarsely.  In Tutz and Binder (2006) it is suggested for
     likelihood based boosting just to make sure, that the optimal
     number of boosting steps, according to some criterion such as
     cross-validation, is larger or equal to 50.  With a smaller number
     of steps, boosting may become too ``greedy'' and show sub-optimal
     performance.  This procedure uses a very coarse line search and so
     one should specify a rather large range of boosting steps.

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

     List with element 'penalty' containing the ``optimal'' penalty and
     'cv.res' containing the corresponding result of 'cv.CoxBoost'.

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

     Written by Harald Binder binderh@fdm.uni-freiburg.de.

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

     Tutz, G. and Binder, H. (2006) Generalized additive modelling with
     implicit variable selection by likelihood based boosting.
     _Biometrics_, 62:961-971.

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

     'CoxBoost', 'cv.CoxBoost'

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

     ## Not run: 
     #   Generate some survival data with 10 informative covariates 
     n <- 200; p <- 100
     beta <- c(rep(1,10),rep(0,p-10))
     x <- matrix(rnorm(n*p),n,p)
     real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
     cens.time <- rexp(n,rate=1/10)
     status <- ifelse(real.time <= cens.time,1,0)
     obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)

     #  determine penalty parameter

     optim.res <- optimCoxBoostPenalty(time=obs.time,status=status,x=x,
                                       trace=TRUE,start.penalty=500)

     #   Fit with obtained penalty parameter and optimal number of boosting
     #   steps obtained by cross-validation

     cbfit <- CoxBoost(time=obs.time,status=status,x=x,
                       stepno=optim.res$cv.res$optimal.step,
                       penalty=optim.res$penalty) 
     summary(cbfit)

     ## End(Not run)

