estimateRho: Estimate the autocorrelation of a mid- to high-resolution...
In McDowellLab/loadflex: Models and Tools for Watershed Flux Estimates

Description Usage Arguments Details Value See Also

Uses arima() to estimate the first-order autocorrelation constant, rho, for the residuals calculated from a load model and new data.

estimateRho(load.model, flux.or.conc = c("flux", "conc"),
  abs.or.rel.resids = c("absolute", "relative"), use.log = FALSE,
  newdata = NULL, plot.acf = TRUE, timestep.tol = .Machine$double.eps^0.5,
  irregular.timesteps.ok = NA)

`load.model`	a loadModel descendant
`flux.or.conc`	The format in which residuals should be calculated
`abs.or.rel.resids`	Should residuals be computed as the difference or the ratio of the observed and predicted values?
`use.log`	logical. use log residuals?
`newdata`	prediction values. If this is set to NULL, `getFittingData(load.model)` will be used.
`plot.acf`	logical. Should the autocorrelation function be plotted?
`timestep.tol`	the acceptable tolerance for considering timesteps regular.
`irregular.timesteps.ok`	logical. If FALSE, this function requires that the timesteps between observations are identical to one another, and a plot is generated and an error is thrown if this requirement is not met. If TRUE, the check is not performed. If NA (the default), the check is performed but the function proceeds with a warning and no plot if the timesteps are found to be irregular. Estimates of autocorrelation are weak to wrong when timesteps are irregular, but timesteps are often at least a bit irregular in the real world.

For the purpose of estimating prediction error for a composite method application, this function should be called with a dataset of comparable (or identical) resolution to that of the interpolation data.

For the purpose of estimating the covariance of prediction errors for aggregation, this function should not be called unless newdata are at approximately the same (or higher) temporal resolution as that of the data to be used for load estimation. This will almost always involve sensor data.

If you have no data with sufficient resolution to reasonably call this function, take your pick from the established assumptions implemented in functions such as rhoEqualDates or rho1DayBand, or write your own assumptions.

Return the rho function and the fitted model.

Other diagnostics: getCorrectionFraction, residDurbinWatson

McDowellLab/loadflex documentation built on May 8, 2019, 9:48 a.m.