predict.survreg           package:survival           R Documentation

_P_r_e_d_i_c_t_e_d _V_a_l_u_e_s _f_o_r _a '_s_u_r_v_r_e_g' _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Predicted values for a 'survreg' object

_U_s_a_g_e:

     ## S3 method for class 'survreg':
     predict(object, newdata,  
     type=c("response", "link", "lp", "linear", "terms", "quantile",  
             "uquantile"),  
      se.fit=FALSE, terms=NULL, p=c(0.1, 0.9),...)

_A_r_g_u_m_e_n_t_s:

  object: result of a model fit using the 'survreg' function.  

 newdata: data for prediction.  If absent predictions are for the 
          subjects used in the original fit.  

    type: the type of predicted value.   This can be on the original
          scale of the data (response),  the linear predictor
          ('"linear"', with '"lp"' as an allowed abbreviation),  a
          predicted quantile on the original scale of the data
          ('"quantile"'),  a quantile on the linear predictor scale
          ('"uquantile"'), or the matrix of terms for the linear
          predictor ('"terms"'). At this time '"link"' and linear
          predictor ('"lp"') are identical.  

  se.fit: if 'TRUE', include the standard errors of the prediction in
          the result.  

   terms: subset of terms.  The default for residual type '"terms"' is
          a matrix with  one column for every term (excluding the
          intercept) in the model.  

       p: vector of percentiles.  This is used only for quantile
          predictions.  

     ...: for future methods

_V_a_l_u_e:

     a vector or matrix of predicted values.

_R_e_f_e_r_e_n_c_e_s:

     Escobar and Meeker (1992). Assessing influence in regression
     analysis with  censored data. _Biometrics,_ 48, 507-528.

_S_e_e _A_l_s_o:

     'survreg',  'residuals.survreg'

_E_x_a_m_p_l_e_s:

     # Draw figure 1 from Escobar and Meeker
     fit <- survreg(Surv(time,status) ~ age + age^2, data=stanford2, 
             dist='lognormal') 
     plot(stanford2$age, stanford2$time, xlab='Age', ylab='Days', 
             xlim=c(0,65), ylim=c(.01, 10^6), log='y') 
     pred <- predict(fit, newdata=list(age=1:65), type='quantile', 
                      p=c(.1, .5, .9)) 
     matlines(1:65, pred, lty=c(2,1,2), col=1) 

     # Predicted Weibull survival curve for a lung cancer subject with
     #  ECOG score of 2
     lfit <- survreg(Surv(time, status) ~ ph.ecog, data=lung)
     pct <- 1:98/100   # The 100th percentile of predicted survival is at +infinity
     ptime <- predict(lfit, newdata=data.frame(ph.ecog=2), type='quantile',
                      p=pct, se=TRUE)
     matplot(cbind(ptime$fit, ptime$fit + 2*ptime$se.fit,
                              ptime$fit - 2*ptime$se.fit)/30.5, 1-pct,
             xlab="Months", ylab="Survival", type='l', lty=c(1,2,2), col=1)

