diagnostic: Calculate a number of objectives to compare time series

In tiger: TIme series of Grouped ERrors

Description

diagnostic_dawson take two vectors (assumed to be time series) and calculates the following objective functions to compare them: correalation, Nash Sutcliffe efficiency, ratio of the integral, lagtime (maximum of the cross correlation), the number of timesteps with opposite sign of the derivative, the highest ratio between recession coefficients and the root mean square error, as well as the ones listed in Dawson 2007.

diagnostic_window calcualtes these measures for a part of the time series only. It is used internally by

diagnostic_series takes this a step further by calculating the above measures for a gliding window along the time series and calculating additional measures. Similar to diagnostic, the function takes two vectors (assumed to be time series) and calculates a number of objectives compare them. In contrast to the more simple diagnostic, the same objectives are applied to a gliding window and a few additional objectives are calcualated: the ratio of the derivatives, the ratio of the recession coefficients for each time step and the current quantile of the residuals.

Usage

diagnostic_window(position, window.size, measured, 
		modelled, use_qualV = FALSE,
		diff.ecdf=NA)
diagnostic_series(measured, modelled, window.size, 
		step.size = 1, integral_correction = FALSE, 
		use_qualV = FALSE)
diagnostic_dawson(modelled, measured, p=NA, m=NA, additional=TRUE, 
		use_qualV=FALSE, diff.ecdf=NA )

Arguments

modelled	Modelled time series or array with dimension c(number_series, dim(measured))
measured	Measured time series
position	Index from where to start the calculation
window.size	Number of time steps to include
step.size	Size of the steps defining the number of scores to be calculating along the time series. For example, with a value of 5 every fifth value is included
integral_correction	Boolean. If true, the ratio of the integrals is divided by the total ratio of the entire integral. This way, relative integral errors can be detected.
p	The number of free parameters in each model - required to calculate AIC and BIC
m	The number of data points that were used in the model calibration - required to calculate AIC and BIC
additional	Boolean, indicating whether to calculate additional measures to the ones defined in Dawson 2007
use_qualV	Boolean, indicating whether to calculate the additional measures defined in Jachner 2007
diff.ecdf	ecdf-function of the bias (measured-modelled)

Details

For more details on the objectives, see the see-also-section

Value

A data frame with the described objectives

Author(s)

Dominik Reusser

References

Dawson, C. W.; Abrahart, R. J. & See, L. M. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts Environmental Modelling & Software, 2007 , 22 , 1034-1052

Jachner, S.; van den Boogaart, K. G. & Petzoldt, T. Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV) Journal of Statistical Software, 2007 , 22 , 1-30

See Also

qualVcor, nashS,lagtime,count.diff.direction.error,k_rel

Examples

   data(example.peaks,package="tiger")

   plot(reference.peak, type="l")
   lines(example.peaks[1,], lty=2)

   diagnostic_dawson(measured = reference.peak, modelled = example.peaks[1,])

   #first half only
   diagnostic_window(measured=reference.peak, modelled=example.peaks[1,],
			 position = 1, window.size = 45 )
   
   #gliding window for 20 time steps
   diagnostic_series(measured=reference.peak, modelled=example.peaks[1,],
			 window.size = 20 )

tiger documentation built on May 2, 2019, 2:22 a.m.