residuals.coxph           package:survival           R Documentation

_C_a_l_c_u_l_a_t_e _R_e_s_i_d_u_a_l_s _f_o_r _a '_c_o_x_p_h' _F_i_t

_D_e_s_c_r_i_p_t_i_o_n:

     Calculates martingale, deviance, score or Schoenfeld residuals for
     a Cox proportional hazards model.

_U_s_a_g_e:

     ## S3 method for class 'coxph':
     residuals(object,
            type=c("martingale", "deviance", "score", "schoenfeld",
                   "dfbeta", "dfbetas", "scaledsch","partial"),
            collapse=FALSE, weighted=FALSE, ...)
     ## S3 method for class 'coxph.null':
     residuals(object,
            type=c("martingale", "deviance","score","schoenfeld"),
            collapse=FALSE, weighted=FALSE, ...)

_A_r_g_u_m_e_n_t_s:

  object: an object inheriting from class 'coxph', representing a
          fitted Cox regression model. Typically this is the output
          from the 'coxph' function. 

    type: character string indicating the type of residual desired.
          Possible values are '"martingale"', '"deviance"', '"score"',
          '"schoenfeld"', "dfbeta"', '"dfbetas"', and '"scaledsch"'.
          Only enough of the string to determine a unique match is
          required. 

collapse: vector indicating which rows to collapse (sum) over. In
          time-dependent models more than one row data can pertain to a
          single individual. If there were 4 individuals represented by
          3, 1, 2 and 4 rows of data respectively, then
          'collapse=c(1,1,1, 2, 3,3, 4,4,4,4)' could be used to obtain
          per subject rather than per observation residuals. 

weighted: if 'TRUE' and the model was fit with case weights, then the
          weighted residuals are returned. 

     ...: other unused arguments

_V_a_l_u_e:

     For martingale and deviance residuals, the returned object is a
     vector with one element for each subject (without 'collapse'). For
     score residuals it is a matrix with one row per subject and one
     column per variable. The row order will match the input data for
     the original fit. For Schoenfeld residuals, the returned object is
     a matrix with one row for each event and one column per variable. 
     The rows are ordered by time within strata, and an attribute
     'strata' is attached that contains the number of observations in
     each strata. The scaled Schoenfeld residuals are used in the
     'cox.zph' function.

     The score residuals are each individual's contribution to the
     score vector. Two transformations of this are often more useful:
     'dfbeta' is the approximate change in the coefficient vector if
     that observation were dropped, and 'dfbetas' is the approximate
     change in the coefficients, scaled by the standard error for the
     coefficients.

_N_O_T_E:

     For deviance residuals, the status variable may need to be
     reconstructed. For score and Schoenfeld residuals, the X matrix
     will need to be reconstructed.

_R_e_f_e_r_e_n_c_e_s:

     T. Therneau, P. Grambsch, and T. Fleming. "Martingale based
     residuals for survival models", _Biometrika_, March 1990.

_S_e_e _A_l_s_o:

     'coxph'

_E_x_a_m_p_l_e_s:

      fit <- coxph(Surv(start, stop, event) ~ (age + surgery)* transplant,
                    data=heart)
      mresid <- resid(fit, collapse=heart$id)

