

frailty(survival5)                           R Documentation

_(_A_p_p_r_o_x_i_m_a_t_e_) _F_r_a_i_l_t_y _m_o_d_e_l_s

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

     When included in a coxph or survreg, fits by penalised
     likelihood a random effects (frailty) model. `frailty'
     is generic, with methods for t, Gaussian and Gamma dis-
     tributions.

_U_s_a_g_e_:

     frailty(x, distribution="gamma", ...)
     frailty.gamma(x, sparse = (nclass > 5), theta, df, eps = 1e-05, method = c("em","aic", "df", "fixed"), ...)
     frailty.gaussian(x, sparse = (nclass > 5), theta, df, method = c("reml","aic", "df", "fixed"), ...)
     frailty.t(x, sparse = (nclass > 5), theta, df, eps = 1e-05, tdf = 5,method = c("aic", "df", "fixed"), ...)

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

       x: group indicator

distribution: frailty distribution

     ...: Arguments for specific distribution, including

  sparse: Use sparse Newton-Raphson algorithm

      df: Approximate degrees of freedom

   theta: Penalty

     eps: Accuracy of `df'

  method: maximisation algorithm

     tdf: df of t-distribution

    caic: Use corrected AIC rather than AIC

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

     The penalised likelihood method is equivalent to maxi-
     mum (partial) likelihood for the gamma frailty but not
     for the others.

     The sparse algorithm uses the diagonal of the informa-
     tion matrix for the random effects, which saves a lot
     of space.

     The frailty distributions are really the log-t and log-
     normal: t and Gaussian are random effects on the scale
     of the linear predictor.

_V_a_l_u_e_:

     An object of class `coxph.penalty' containing a factor
     with attributes specifying the control functions.

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

     `coxph',`survreg',`ridge',`pspline'

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

     data(kidney)
     kfit <- coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney)
     kfit0 <- coxph(Surv(time, status)~ age + sex + disease, kidney)
     kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease +
                     frailty(id, dist='gauss'), kidney)
     coxph(Surv(time, status) ~ age + sex + frailty(id, dist='gauss', method='aic',caic=T), kidney)
     # uncorrected aic
     coxph(Surv(time, status) ~ age + sex + frailty(id, method='aic', caic=F), kidney)

     data(rats)
     rfit2a <- survreg(Surv(time, status) ~ rx +
                       frailty.gaussian(litter, df=13, sparse=F), rats )
     rfit2b <- survreg(Surv(time, status) ~ rx +
                       frailty.gaussian(litter, df=13, sparse=T), rats )
     rfit2a
     rfit2b

