residuals.Mort2Dsmooth {MortalitySmooth} | R Documentation |
Extracting different types of residuals from a Mort2Dsmooth
object.
## S3 method for class 'Mort2Dsmooth': residuals(object, type = c("deviance", "pearson", "anscombe", "working"), ...)
object |
An object of class "Mort2Dsmooth", usually, a result of
a call to Mort2Dsmooth . |
type |
The type of residuals which should be returned. The alternatives are: "deviance" (default), "anscombe", "pearson" and "working". |
... |
Further arguments passed to or from other methods. |
The references define the types of residuals.
The way of computing the residuals are described in Section 2.4 of McCullagh and Nelder's book. The working residuals are merely the differences between fitted and actual counts.
A matrix of the selected type of residuals over both the x
and
the y
axes in the Mort2Dsmooth
object.
Carlo G Camarda
Davison, A. C. and Snell, E. J. (1991). Residuals and diagnostics. In: Statistical Theory and Modelling. In Honour of Sir David Cox, FRS, eds. Hinkley, D. V., Reid, N. and Snell, E. J., Chapman & Hall.
McCullagh P. and Nelder, J. A. (1989). Generalized Linear Models. London: Chapman and Hall.
Mort2Dsmooth
for computing
Mort2Dsmooth.object
, residuals
.
# selected data ages <- 30:80 years <- 1970:2006 death <- selectHMDdata("Switzerland", "Deaths", "Males", ages = ages, years = years) exposure <- selectHMDdata("Switzerland", "Exposures", "Males", ages = ages, years = years) # fit fit <- Mort2Dsmooth(x=ages, y=years, Z=death, offset=log(exposure), method=3, lambdas=c(300,10)) # extracting residuals devR <- resid(fit, type="deviance") ansR <- resid(fit, type="anscombe") peaR <- resid(fit, type="pearson") worR <- resid(fit, type="working") # plotting deviance residuals over age and years res.list <- list(ages=ages, years=years) res.grid <- expand.grid(res.list) res.grid$dev <- c(devR) levelplot(dev~years*ages, res.grid, at=c(min(devR), -2, -1, 1, 2, max(devR)))