

ridge(survival5)                             R Documentation

_R_i_d_g_e _r_e_g_r_e_s_s_i_o_n

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

     When used in a coxph or survreg model formula, speci-
     fies a ridge regression term.  The likelihood is
     penalised by `theta'/2 time the sum of squared coeffi-
     cients. If `scale=T' the penalty is calculated for
     coefficients based on rescaling the predictors to have
     unit variance. If `df' is specified then `theta' is
     chosen based on an approximate degrees of freedom.

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

     ridge(..., theta, df=nvar/2, eps=0.1, scale=T)

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

     ...: predictors to be ridged

   theta: penalty is `theta'/2 time sum of squared coeffi-
          cients

      df: Approximate degrees of freedom

     eps: Accuracy required for `df'

   scale: Scale variables before applying penalty?

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

     An object of class `coxph.penalty' containing the data
     and control functions.

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

     Gray (1992) "Flexible methods of analysing survival
     data using splines, with applications to breast cancer
     prognosis" JASA 87:942-951

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

     `coxph',`survreg',`pspline',`frailty'

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

     data(ovarian)
     fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1),
                   ovarian)
     fit1
     data(cancer)
     lfit0 <- survreg(Surv(time, status) ~1, cancer)
     lfit1 <- survreg(Surv(time, status) ~ age + ridge(ph.ecog, theta=5), cancer)
     lfit2 <- survreg(Surv(time, status) ~ sex + ridge(age, ph.ecog, theta=1), cancer)
     lfit3 <- survreg(Surv(time, status) ~ sex + age + ph.ecog, cancer)

     lfit0
     lfit1
     lfit2
     lfit3

