residuals.vlmc {VLMC} | R Documentation |
Compute residuals of a fitted vlmc
object.
This is yet a matter of research and may change in the future.
## S3 method for class 'vlmc' residuals(object, type = c("classwise", "deviance", "pearson", "working", "response", "partial"), y = object$y, ...)
object |
typically the result of |
type |
The type of residuals to compute, defaults to
|
y |
discrete time series with respect to which the residuals are to be computed. |
... |
possibly further arguments (none at the moment). |
If type = "classwise"
(the default), a numeric matrix of dimension
n x m of values I[i,j] -
p[i,j] where the indicator I[i,j] is 1 iff
y[i] == a[j]
and a
is the alphabet (or levels) of
y
, and p[i,j] are the elements of the estimated (1-step
ahead) predicted probabilities, p <- predict(object)
.
Hence, for each i, the only positive residual stands for the
observed class.
For all other type
s, the result is
a numeric vector of the length of the original time-series (with first
element NA
).
For type = "deviance"
,
r[i] = +- sqrt(-2 log(P[i]))
where P[i] is the predicted probability for the i-th
observation which is the same as p[i,y[i]] above (now
assuming y[i] in {1,2,...,m}).
The sum of the squared deviance residuals is the deviance of
the fitted model.
Martin Maechler
vlmc
,deviance.vlmc
, and
RCplot
for a novel residual plot.
example(vlmc) rp <- residuals(vlmc.pres) stopifnot(all(abs(apply(rp[-1,],1,sum)) < 1e-15)) matplot(seq(presidents), rp, ylab = "residuals", type="l") ## ``Tukey-Anscombe'' (the following is first stab at plot method): matplot(fitted(vlmc.pres), rp, ylab = "residuals", xaxt = "n", type="b", pch=vlmc.pres$alpha) axis(1, at = 0:(vlmc.pres$alpha.len-1), labels = strsplit(vlmc.pres$alpha,"")[[1]]) summary(rd <- residuals(vlmc.pres, type = "dev")) rd[1:7] ## sum of squared dev.residuals === deviance : all.equal(sum(rd[-1] ^ 2), deviance(vlmc.pres))