This creates a time-series of predicted tides, based on a
tidal model object that was created by as.tidem()
or tidem()
.
# S3 method for tidem
predict(object, newdata, ...)
vector of POSIXt times at which to make the
prediction. For models created with tidem()
,
the newdata
argument is optional, and if it is not provided, then
the predictions are at the observation times given to
tidem()
. However, newdata
is required if as.tidem()
had been used to create object
.
optional arguments passed on to children.
A vector of predictions.
Other things related to tides:
[[,tidem-method
,
[[<-,tidem-method
,
as.tidem()
,
plot,tidem-method
,
summary,tidem-method
,
tidalCurrent
,
tidedata
,
tidem-class
,
tidemAstron()
,
tidemVuf()
,
tidem
,
webtide()
# NOT RUN {
# }
# NOT RUN {
library(oce)
# 1. tidal anomaly
data(sealevelTuktoyaktuk)
time <- sealevelTuktoyaktuk[["time"]]
elevation <- sealevelTuktoyaktuk[["elevation"]]
oce.plot.ts(time, elevation, type='l', ylab="Height [m]", ylim=c(-2, 6))
tide <- tidem(sealevelTuktoyaktuk)
lines(time, elevation - predict(tide), col="red")
abline(h=0, col="red")
# 2. 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)
# }
# NOT RUN {
# }
Run the code above in your browser using DataLab