R: Calculate temporal dynamics of model performance
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.
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.