Get fitted (component) means from a hhh4 model.

## Usage

# S3 method for hhh4
predict(object, newSubset=object$control$subset,
type="response", ...)

## Arguments

object

fitted hhh4 model (class "hhh4").

newSubset

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$stsObj). type the type of prediction required. The default ("response" or, equivalently, "mean") is on the scale of the response variable (mean = endemic plus epidemic components). The alternatives are: "endemic", "epidemic", "epi.own" (i.e. the autoregressive part), and "epi.neighbours" (i.e. the spatio-temporal part). ... unused (argument of the generic). ## Value matrix of fitted means for each time point (of newSubset) and region. ## Note Predictions for “newdata”, i.e., with modified covariates or fixed weights, can be computed manually by adjusting the control list (in a copy of the original fit), dropping the old terms, and using the internal function meanHHH directly, see the Example. ## Author Michaela Paul and Sebastian Meyer ## Examples ## simulate simple seasonal noise with reduced baseline for t >= 60 t <- 0:100 y <- rpois(length(t), exp(3 + sin(2*pi*t/52) - 2*(t >= 60))) obj <- sts(y) plot(obj) ## fit true model fit <- hhh4(obj, list(end = list(f = addSeason2formula(~lock)), data = list(lock = as.integer(t >= 60)), family = "Poisson")) coef(fit, amplitudeShift = TRUE, se = TRUE) ## compute predictions for a subset of the time points stopifnot(identical(predict(fit), fitted(fit))) plot(obj) lines(40:80, predict(fit, newSubset = 40:80), lwd = 2) ## advanced: compute predictions for "newdata" (here, a modified covariate) mod <- fit mod$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)