This creates a time-series of predicted tides, based on a
tidal model object that was created by as.tidem() or tidem().
Usage
# S3 method for class 'tidem'
predict(object, newdata, ...)Arguments
- object
a tidem object.
- newdata
vector of POSIXt times at which to make the prediction. For models created with
tidem(), thenewdataargument is optional, and if it is not provided, then the predictions are at the observation times given totidem(). However,newdatais required ifas.tidem()had been used to createobject.- ...
optional arguments passed on to children.
Details
All the tidal constituents that are stored in object are used,
not just those that are statistically significant or that have amplitude
exceeding any particular value. In this respect, predict.tidem()
follows a pattern established by e.g. predict.lm(). Note that
the constituents in object are straightforward if it
was constructed with as.tidem(), but considerably more complicated
for tidem(), and so the documentation for the latter ought to
be studied closely, especially with regard to the Rayleigh criterion.
Sample of Usage
# prediction at specified times
data(sealevel)
m <- tidem(sealevel)
# Check fit over 2 days (interpolating to finer timescale)
look <- 1:48
time <- sealevel[["time"]]
elevation <- sealevel[["elevation"]]
oce.plot.ts(time[look], elevation[look])
# 360s = 10 minute timescale
t <- seq(from=time[1], to=time[max(look)], by=360)
lines(t, predict(m, newdata=t), col="red")
legend("topright", col=c("black","red"),
legend=c("data","model"),lwd=1)See also
Other things related to tides:
[[,tidem-method,
[[<-,tidem-method,
as.tidem(),
plot,tidem-method,
summary,tidem-method,
tidalCurrent,
tidedata,
tidem,
tidem-class,
tidemAstron(),
tidemVuf(),
webtide()
Examples
# Show non-tidal sealevel signal in Halifax Harbour during
# the year 2002. The spike resulted from Hurricane Juan.
library(oce)
data(sealevel)
time <- sealevel[["time"]]
elevation <- sealevel[["elevation"]]
prediction <- tidem(sealevel) |> predict()
#> Note: the tidal record is too short to fit for constituents: SA, PI1, S1, PSI1, GAM2, H1, H2, T2, R2
oce.plot.ts(time, elevation - prediction)
