Create a temporalAtRisk object from a numeric vector.
# S3 method for numeric
temporalAtRisk(obj, tlim, xyt = NULL, warn = TRUE, ...)a numeric vector of length (tlim[2]-tlim[1] + 1) giving the temporal intensity up to a constant of proportionality at each integer time within the interval defined by tlim
an integer vector of length 2 giving the time limits of the observation window
an object of class stppp. If NULL (default) then the function returned is not scaled. Otherwise, the function is scaled so that f(t) = expected number of counts at time t.
Issue a warning if the given temporal intensity treated is treated as 'known'?
additional arguments
a function f(t) giving the temporal intensity at time t for integer t in the interval as.integer([tlim[1],tlim[2]]) of class temporalAtRisk
Brix A, Diggle PJ (2001). Spatiotemporal Prediction for log-Gaussian Cox processes. Journal of the Royal Statistical Society, Series B, 63(4), 823-841.
Diggle P, Rowlingson B, Su T (2005). Point Process Methodology for On-line Spatio-temporal Disease Surveillance. Environmetrics, 16(5), 423-434.
Note that in the prediction routine, lgcpPredict, and the simulation routine, lgcpSim, time discretisation is achieved
using as.integer on both observation times and time limits t_1 and t_2 (which may be stored as non-integer values). The
functions that create temporalAtRisk objects therefore return piecewise constant step-functions that can be evaluated for any real
t in [t_1,t_2], but with the restriction that mu(t_i) = mu(t_j) whenever as.integer(t_i)==as.integer(t_j).
A temporalAtRisk object may be (1) 'assumed known', corresponding to the default argument xyt=NULL; or (2) scaled to a particular dataset
(argument xyt=[stppp object of interest]). In the latter case, in the routines available (temporalAtRisk.numeric
and temporalAtRisk.function), the dataset of interest should be referenced, in which case the scaling of mu(t) will be done
automatically. Otherwise, for example for simulation purposes, no scaling of mu(t) occurs, and it is assumed that the mu(t) corresponds to the
expected number of cases during the unit time interval containing t.
temporalAtRisk, spatialAtRisk, temporalAtRisk.function, constantInTime, constantInTime.numeric, constantInTime.stppp, print.temporalAtRisk, plot.temporalAtRisk