hhh4 ModelGet fitted (component) means from a hhh4 model.
# S3 method for hhh4
predict(object, newSubset=object$control$subset,
type="response", ...)matrix of fitted means for each time point (of newSubset) and region.
fitted hhh4 model (class "hhh4").
subset of time points for which to return the
predictions. Defaults to the subset used for fitting the model, and
must be a subset of 1:nrow(object$stsObj).
the type of prediction required. The default
("response" or, equivalently, "mean") is on the
scale of the response variable (mean = endemic plus epidemic components).
The alternatives are: "endemic", "epidemic",
"epi.own" (i.e. the autoregressive part), and
"epi.neighbours" (i.e. the spatio-temporal part).
unused (argument of the generic).
Michaela Paul and Sebastian Meyer
## simulate simple seasonal noise with reduced baseline for t >= 60
t <- 0:100
y <- rpois(length(t), exp(3 + sin(2*pi*t/52) - 2*(t >= 60)))
obj <- sts(y)
plot(obj)
## fit true model
fit <- hhh4(obj, list(end = list(f = addSeason2formula(~lock)),
data = list(lock = as.integer(t >= 60)),
family = "Poisson"))
coef(fit, amplitudeShift = TRUE, se = TRUE)
## compute predictions for a subset of the time points
stopifnot(identical(predict(fit), fitted(fit)))
plot(obj)
lines(40:80, predict(fit, newSubset = 40:80), lwd = 2)
## advanced: compute predictions for "newdata" (here, a modified covariate)
mod <- fit
mod$terms <- NULL # to be sure
mod$control$data$lock[t >= 60] <- 0.5
pred <- meanHHH(mod$coefficients, terms(mod))$mean
plot(fit, xaxis = NA)
lines(mod$control$subset, pred, lty = 2)
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