survreg.object package:survival R Documentation _P_a_r_a_m_e_t_r_i_c _S_u_r_v_i_v_a_l _M_o_d_e_l _O_b_j_e_c_t _D_e_s_c_r_i_p_t_i_o_n: This class of objects is returned by the 'survreg' function to represent a fitted parametric survival model. Objects of this class have methods for the functions 'print', 'summary', 'predict', and 'residuals'. _C_O_M_P_O_N_E_N_T_S: The following components must be included in a legitimate 'survreg' object. _c_o_e_f_f_i_c_i_e_n_t_s the coefficients of the 'linear.predictors', which multiply the columns of the model matrix. It does not include the estimate of error (sigma). The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to non-estimable coefficients. _i_c_o_e_f coefficients of the baseline model, which will contain the intercept and log(scale), or mulitple scale factors for a stratified model. _v_a_r the variance-covariance matrix for the parameters, including the log(scale) parameter(s). _l_o_g_l_i_k a vector of length 2, containing the log-likelihood for the baseline and full models. _i_t_e_r the number of iterations required _l_i_n_e_a_r._p_r_e_d_i_c_t_o_r_s the linear predictor for each subject. _d_f the degrees of freedom for the final model. For a penalized model this will be a vector with one element per term. _s_c_a_l_e the scale factor(s), with length equal to the number of strata. _i_d_f degrees of freedom for the initial model. _m_e_a_n_s a vector of the column means of the coefficient matrix. _d_i_s_t the distribution used in the fit. The object will also have the following components found in other model results (some are optional): 'linear predictors', 'weights', 'x', 'y', 'model', 'call', 'terms' and 'formula'. See 'lm'. _S_e_e _A_l_s_o: 'survreg', 'lm'