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 = 20, control, control.frailty, seed.set = 100, ...)

_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 survBayes.control(...). 

control.frailty: Object of class 'control.frailty' specifying
          parameters for the priors of frailties and other control
          options. Default is survBayes.control.lognormal.frailty(...)
          or survBayes.control.gamma.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 cubic
     spline penalized by an auto regressive process of order one. Right
     and interval censored data and a lognormal or gamma 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, if appropriate 

t.where : used grid points

    beta: samples of the vector of covariates

lbh.coef : samples of the log baseline hazard coefficients at the grid
          points

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

alpha.cluster: samples of the frailty values

sigma.cluster: samples of frailty variance

z.cluster: samples of the frailty values

mu.cluster: samples of the rate and shape of the gamma prior

m.h.performance: The performance of the Metropolis-Hasting steps is
          checked for beta, lbh and, if appropriate, alpha.cluster or
          mu.cluster

_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(aneurism.data)
     control<-survBayes.control(sigma.lbh.1=0.01,rate.sigma.lbh.1 = 1e-3, shape.sigma.lbh.1 = 1e-3)
     aneurism.res<-survBayes(Surv(left,right,cens*3,type="interval")~mo+loc+frailty(gr,dist="gamma"),data=aneurism.data,burn.in=0,number.sample=10,control=control)

