hhh4_predict {surveillance}

Predictions from a hhh4 Model

Description

Get fitted (component) means from a hhh4 model.

Usage

## 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).

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(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)