tiger {tiger}R Documentation

Calculate temporal dynamics of model performance

Description

About fifty performance measures are calculated for a gliding window, comparing two time series. The resulting matrix is clustered, such that each time window can be assigned to an error type cluster. The mean performance measures for each cluster can be used to give meaning to each cluster. Additionally, synthetic peaks are used to better characterize the clusters.

Usage

tiger(modelled, measured, window.size, step.size = 1,
                 use.som = TRUE, som.dim = c(20, 20), som.init =
                 "sample", som.topol = "hexa", maxc = 15,
                 synthetic.errors = NA)
tiger.peaks(result, synthetic.errors)

Arguments

modelled Time series of modelled data
measured Time series of measured data
window.size Size of the moving window
maxc Maximum number of clusters to be tested
synthetic.errors Matrix returned from synth.peak.error
result object returned from tiger
use.som boolean, indicating whether to use SOM before applying fuzzy clustering
som.dim Dimension of the Self Organizing Map (SOM) c(x,y)
som.init Method to initialize the SOM
som.topol Topology of the SOM
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

Details

See the package vignette.

Value

maxc see input parameter
window.size see input parameter
modelled see input parameter
measured see input parameter
synthetic.errors see input parameter
measures.synthetic.peaks matrix of performance measures for synthetic errors
measures matrix of performance measures for the gliding time window
na.rows vector of boolean, indicating which time windows contain NA values
names names of the perfomance measures
measures.uniform measures, transformed to uniform distribution
measures.uniform.synthetic.peaks measures for synthetic errors, transformed with the corresponding transformation from previous item
error.names names of the synthetic error types
best.value.location list, indicating what the value for "no error" for each performance measure is
validityMeasure vector with validty index for solutions with 2:maxc clusters
cluster.assignment list of 2:maxc objects returned from cmeans

Author(s)

Dominik Reusser

References

Reusser, D. E., Blume, T., Schaefli, B., and Zehe, E.: Analysing the temporal dynamics of model performance for hydrological models, Hydrol. Earth Syst. Sci. Discuss., 5, 3169-3211, 2008.

See Also

The package vignette

Examples

data(tiger.example)
modelled <- tiger.single$modelled
measured <- tiger.single$measured
peaks <- synth.peak.error(rise.factor=2, recession.const=0.02, rise.factor2=1.5)
## Not run: 
result2 <- tiger(modelled=modelled, measured=measured, window.size=240, synthetic.errors=peaks)
errors.in.time(d.dates, result2, solution=6, show.months=TRUE)
## End(Not run)

peaks2 <- synth.peak.error(rise.factor=2, recession.const=0.02,
     rise.factor2=1.5, err1.factor=c(1.3,1.5,2.0),
     err2.factor = c(0.02,0.03,0.06), 
     err3.factor=c(2,4,10), 
     err4.factor = c(9,22,40), 
     err5.factor = c(0.2,0.3,0.5),
     err6.factor =c(2,3,5),
     err9.factor=c(1.5,3,6)
   )

## Not run: result3 <- tiger.peaks(result2, peaks2)

   peaks.in.clusters(result2, solution=6)
   x11()
   peaks.in.clusters(result3, solution=6)
## End(Not run)


[Package tiger version 0.2 Index]