R: Estimate transfer function models by Least Squares.

armax.ls.fit {hydromad}

R Documentation

Estimate transfer function models by Least Squares.

Description

Calibrate unit hydrograph transfer function models (armax or expuh) using Least Squares with prefiltering.

Usage

armax.ls.fit(DATA,
             order = hydromad.getOption("order"),
             delay = hydromad.getOption("delay"),
             prefilter = hydromad.getOption("prefilter"),
             warmup = hydromad.getOption("warmup"),
             normalise = FALSE,
             fixed.ar = NULL,
             weights = NULL,
             initX = TRUE,
             na.action = na.pass,
             trace = hydromad.getOption("trace"))

expuh.ls.fit(DATA,
             order = hydromad.getOption("order"),
             delay = hydromad.getOption("delay"),
             quiet = FALSE,
             ...)

Arguments

`DATA`	a `ts`-like object with named columns: `U` observed input time series. `Q` observed output time series.
`order`	the transfer function order. See `armax`.
`delay`	delay (lag time / dead time) in number of time steps. If missing, this will be estimated from the cross correlation function.
`prefilter`
`warmup`
`normalise`
`fixed.ar`
`weights`
`initX`
`na.action`
`trace`
`...`	passed on to `armax.ls.fit`.
`quiet`	to suppress the message when re-fitting if non-physical poles (i.e. negative or imaginary poles) are detected.

Details

In normal usage, one would not call these functions directly, but rather specify the routing fitting method for a hydromad model using that function's rfit argument. E.g. to specify fitting an expuh routing model by least squares one could write

hydromad(..., routing = "expuh", rfit = "ls")

which uses the default order, hydromad.getOption("order"), or

hydromad(..., routing = "expuh", rfit = list("ls", order = c(2,1))).

Value

a tf object, which is a list with components

`coefficients`	the fitted parameter values.
`fitted.values`	the fitted values.
`residuals`	the residuals.
`delay`	the (possibly fitted) delay time.

Author(s)

Felix Andrews felix@nfrac.org

References

Jakeman

Examples

U <- ts(c(0, 0, 0, 1, rep(0, 30), 1, rep(0, 20)))
Y <- expuh.sim(lag(U, -1), tau_s = 10, tau_q = 2, v_s = 0.5, v_3 = 0.1)
set.seed(0)
Yh <- Y * rnorm(Y, mean = 1, sd = 0.2)
fit1 <- armax.ls.fit(ts.union(U = U, Q = Yh),
                     order = c(2, 2), warmup = 0)
fit1
xyplot(ts.union(observed = Yh, fitted = fitted(fit1)),
       superpose = TRUE)

[Package hydromad version 0.9-18 Index]