Note: The
survreg function is deprecated; please use
instead.
Compute deviance, Pearson, working or matrix residuals for
a parametric survival model.
This is a method for the function
residuals for objects inheriting from
class
survreg.
However, as several types of residuals are
available for
survreg objects,
there is an additional optional argument
type.
USAGE:
residuals.survreg(object, type="deviance")
REQUIRED ARGUMENTS:
object
an object inheriting from class
survreg,
representing a parametric survival model.
Typically this is the output from the
survreg
function.
OPTIONAL ARGUMENTS:
type
type of residuals, with choices
"deviance",
"pearson",
"working" or
"matrix".
VALUE:
a vector of residuals is returned.
The sum of squared deviance residuals add up to the deviance.
The Pearson residuals are standardized residuals
on the scale of the response.
The working residuals reside on the object,
and are the residuals from the final IRLS fit.
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(sigma).
Then the 6 columns of the matrix are L, dL/dp, ddL/(dp dp),
dL/ds, ddL/(ds ds) and ddL/(dp ds), where d stands for the
derivative and dd the second derivative.
Diagnostics based on these quantities are discussed
in Escobar and Meeker (1992).
REFERENCES:
Escobar, L. A. and Meeker, W. Q. (1992).
Assessing influence in regression analysis with censored data.
Biometrics48, 507-528.