# residuals.coxph

##### Calculate Residuals for a `coxph' Fit

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

- Keywords
- survival

##### Usage

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

##### Arguments

- 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

##### 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

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

##### See Also

##### Examples

```
# NOT RUN {
fit <- coxph(Surv(start, stop, event) ~ (age + surgery)* transplant,
data=heart)
mresid <- resid(fit, collapse=heart$id)
# }
```

*Documentation reproduced from package survival, version 3.1-8, License: LGPL (>= 2)*