residuals.survreg {survival} | R Documentation

## Compute Residuals for 'survreg' Objects

### Description

This is a method for the function `residuals` for objects inheriting from class `survreg`.

### Usage

    residuals.survreg(object, type=c("response", "deviance","dfbeta","dfbetas",
    "working","ldcase","ldresp","ldshape", "matrix"), rsigma=TRUE,
    collapse=FALSE, weighted=FALSE, ...)

### Arguments

`object` | an object inheriting from class `survreg`.
`type` | type of residuals, with choices of `"response"`, `"deviance"`, `"dfbeta"`, `"dfbetas"`, `"working"`, `"ldcase"`, `"lsresp"`, `"ldshape"`, and `"matrix"`. See the LaTeX documentation (`survival/doc/survival.ps.gz`) for more detail.
`rsigma` | include the scale parameters in the variance matrix, when doing computations. (I can think of no good reason not to).
`collapse` | optional vector of subject groups. If given, this must be of the same length as the residuals, and causes the result to be per group residuals.
`weighted` | give weighted residuals? Normally residuals are unweighted.
`...` | other unused arguments

### Value

A vector or matrix of residuals is returned. Response residuals are on the scale of the original data, working residuals are on the scale of the linear predictor, and deviance residuals are on log-likelihood scale. The dfbeta residuals are a matrix, where the ith row gives the approximate change in the coefficients due to the addition of subject i. The dfbetas matrix contains the dfbeta residuals, with each column scaled by the standard deviation of that coefficient.
The matrix type produces a matrix based on derivatives of the log-likelihood function. Let L be the log-likelihood, p be the linear predictor X %*% coef, and s be log(σ). Then the 6 columns of the matrix are L, dL/dp,ddL/(dp dp), dL/ds, ddL/(ds ds) and ddL/(dp ds). Diagnostics based on these quantities are discussed in an article by Escobar and Meeker. The main ones are the likelihood displacement residuals for perturbation of a case weight (`ldcase`), the response value (`ldresp`), and the `shape`.

### References

Escobar, L. A. and Meeker, W. Q. (1992). Assessing influence in regression analysis with censored data. Biometrics 48, 507-528.

### See Also

`predict.survreg`

### Examples

    data(aml)
    fit <- survreg(Surv(time,status) ~x, aml)
    rr  <- residuals(fit, type='matrix')

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