Calculate Residuals for a Cox Regression
DESCRIPTION:
Calculates martingale, deviance, score or Schoenfeld residuals for a Cox proportional hazards model.
USAGE: 
residuals.coxph(object, type=c("martingale", "deviance",
                "score", "schoenfeld", "dfbeta", "dfbetas",
                "scaledsch"), collapse=F, weighted=F)

This is a method for the function residuals for objects inheriting from class "coxph". However, as several types of residuals are available for coxph objects, there is an additional optional argument type.

REQUIRED ARGUMENTS:
object:
an object inheriting from class coxph, representing a fitted Cox regression model. Typically this is the output from the coxph function.
OPTIONAL ARGUMENTS:
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.
OPTIONAL ARGUMENTS:
if TRUE and the model was fit with case weights, then the weighted residuals are returned.
VALUE:
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.

NOTE:
For deviance residuals, the status variable may need to be reconstructed. For score and Schoenfeld residuals, the X matrix will need to be reconstructed.
REFERENCES:
Therneau, T., Grambsch, P., and Fleming. T. (1990). Martingale based residuals for survival models. Biometrika 77, 147-160.
SEE ALSO:
coxph .
EXAMPLES: 
fit <- coxph(Surv(start, stop, event) ~ (age + surgery) * transplant,
             data=heart)
mresid <- resid(fit, collapse=heart$id)