hhh4_predict function

Predictions from a hhh4 Model

Get fitted (component) means from a hhh4 model.

## S3 method for class '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).

Returns

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(s)

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)