| residuals.coxph {survival} | R Documentation |
Calculates martingale, deviance, score or Schoenfeld residuals for a Cox proportional hazards model.
## 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, ...)
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 |
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.
For deviance residuals, the status variable may need to be reconstructed. For score and Schoenfeld residuals, the X matrix will need to be reconstructed.
T. Therneau, P. Grambsch, and T. Fleming. "Martingale based residuals for survival models", Biometrika, March 1990.
data(heart)
fit <- coxph(Surv(start, stop, event) ~ (age + surgery)* transplant,
data=heart)
mresid <- resid(fit, collapse=heart$id)