survBayes             package:survBayes             R Documentation

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

     Fits a proportional hazards model to time to event data by a
     Bayesian approach. Right and interval censored data and a
     lognormal frailty term can be fitted.

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

     survBayes(formula = formula(data), data = parent.frame(), burn.in = 1000, number.sample = 1000, max.grid.size = 50, control, control.frailty, seed.set, ...)

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

 formula: a formula object, with the response on the left of a '~'
          operator, and the terms on the right. The response must be a
          survival object of type '"right"' or '"interval"' as returned
          by the 'Surv' function. 

    data: a data.frame in which to interpret the variables named in the
          'formula'. 

 burn.in: burn.in 

number.sample: number of sample 

max.grid.size: number of grid points 

 control: Object of class 'control' specifying iteration limit and
          other control options. Default is control(...). 

control.frailty: Object of class 'control.frailty' specifying iteration
          limit and other control options. Default is
          control.frailty(...). 

seed.set: setting of the seed of the random number generator 

     ...: further parameters 

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

     Fits a proportional hazards model to time to event data by a
     Bayesian approach. The time axis is split into 'max.grid.size'
     intervals and the log baseline hazard is assumed to be a auto
     regressive process of order one. Right and interval censored data
     and a lognormal frailty term can be fitted. In case of interval
     censored data the assumed observation times are augmented by a
     piecewise exponential distribution conditioned on the respective
     interval.

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

     The returned values are 

t.where : used grid points

    lbh : samples of the log baseline hazard at the grid points

    beta: samples of the vector of covariates

sigma.lbh: samples of sigma.lbh.0 and sigma.lbh.1

alpha.cluster: samples of the frailty values

sigma.cluster: samples of frailty variance

m.h.performance: for beta, lbh and, if appropriate, alpha

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

     V. Henschel, Ch. Heiss, U. Mansmann

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

     'coxph', 'Surv'

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

     data(AA.data)
     AA.res<-survBayes(Surv(t.left,t.right,z*3,type="interval")~mo+lok+frailty(gr,dist="gauss"),data=AA.data,burn.in=10,number.sample=10)

