ridge                package:survival                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, specifies a ridge
     regression term.  The likelihood is penalised by 'theta'/2 time
     the sum of squared coefficients. 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=TRUE)

_A_r_g_u_m_e_n_t_s:

     ...: predictors to be ridged 

   theta: penalty is 'theta'/2 time sum of squared coefficients 

      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:

     fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1),
                   ovarian)
     fit1

     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