Calculate residuals for a coxph fit.

Usage

resid(object,
       type=c("martingale", "deviance", "score", "schoenfeld",
	      "dfbeta", "dfbetas", "scaledsch"),
       collapse)

Arguments

object a coxph object, output from a coxph fit.
type character string indicating the type of residual desired; the default is martingale. 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.

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 transformatons 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.

Side Effects

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

References

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

See Also

coxph

Examples

data(jasa1)
fit <- coxph(Surv(start, stop, event) ~ (age + surgery)* transplant,data=jasa1)
mresid <- resid(fit, collapse=jasa1$id)


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