plots: Evaluation plots for temporal dynamics of model performance

In tiger: TIme series of Grouped ERrors

Description

Create various plot to understand the temporal dynamics of model performance

Usage

box.plots(result, solution, show.measures = 1:num.measures,
             new.order = 1:solution, show.synthetic.peaks = FALSE,
             synthetic.peaks.col = c(2:8, 2:8), show.timestep = NA,
	     show.cell = NA,
                 ref = NULL, ref.new.order = new.order, ref.solutions =
                 solution, col.best.match = "black",
                 clusterPalette = rainbow(solution))
errors.in.time(xval, result, solution, rain.data = NULL, show.months
                 = FALSE, new.order = 1:solution, x.range =
                 1:length(xval), pmax = max(c(result$measured,
                 result$modelled), na.rm = TRUE), data.colors =
                 data.frame(measured = c("grey"), modelled =
                 c("black"), rain = c("black")), clusterPalette =
                 rainbow(solution), color.cut.off = 0, frac.max = 0.7,
                 frac.min = 0.4, grid.nx = 0, legend.pos = "topleft",
                 show.data = TRUE, show.errors = TRUE, show.data.model
                 = show.data, show.data.measured = show.data, ...)
peaks.in.clusters(result, solution,  new.order = 1:solution)
peaks.on.som(result, solution, clusterPalette=rainbow(solution), 
		 cell.size = 0.9, mfrow=c(2,ceiling(n.errors/2)), 
		 new.order=1:solution)
peaks.measures(result, show.measures = 1:num.measures,
                 synthetic.peaks.col = c(2:8, 2:8), mfrow = c(2, 3),
                 col.best.match = "black", do.out = rep(TRUE,
                 length(show.measures)), single.errors = FALSE,
                 show.legend = TRUE, show.main = TRUE, y.range = NULL)
scatterplot(measures, show.measures=1:num.measures)
p.validityIndex(result, validity.max)

Arguments

result	object returned from tiger
measures	data.frame from which to create a scatter plot. e.g. result\$measures.uniform
solution	number of clusters to use for further evaluations (see also validityIndex)
single.errors	Boolean, indicating weather different synthetic errors should be combined into a single plot or shown in multiple plots
show.legend	Boolean, indicating whether to show the legend
show.main	Boolean, indicating whether to show performance measure names as plot title
show.measures	vector of indices indicating for which performance measures to show the plots
new.order	New numbering to assign to clusters. See also change.order.clusters
show.synthetic.peaks	Show values of the synthetic peaks on top of the box plots.
synthetic.peaks.col	Colors to use for synthetic peaks.
do.out	vector of booleans indicating whether to exclude outliers when showing the plot
cell.size	fraction of the cell square to be filled with color
show.cell	the scores for a certain cell on the SOM can be ploted as blue line on the box plot (see examples)
x.range	Indizes of x-values to be plotted
y.range	Range for y axis
pmax	maximum discharge for definition of the plot range
frac.min	minimum of the y-range covered by color bars for cluster occurence
frac.max	maximum of the y-range covered by color bars for cluster occurence
clusterPalette	colors to use for the clusters
color.cut.off	Value of cluster occurence below which the color bar is set to transparent (for better readability)
legend.pos	Position of the legend
data.colors	Color definition for rainfall and runoff
show.timestep	timestep for which the values for the performance measures are to be plotted as black lines in the box plot
xval	Values to be plotted on the x-axis (e.g. POSIX-date)
show.months	Boolean indicating whether to add month ticks to x axis
mfrow	see par
ref	Reference solution to be ploted in grey on the box plot
ref.new.order	New numbering to assign to clusters for reference solution on the box plot
ref.solutions	Number of clusters for reference solution for which to plot the box plot
validity.max	Do not plot solutions with cluster numbers resulting above in a validty index above validity.max
col.best.match	Color to use for plotting the line indicating the position of the best match
rain.data	vector with rainfall data
show.data	boolean, indicating whether to show discharge data
show.data.measured	boolean, indicating whether to show measured discharge data
show.data.model	boolean, indicating whether to show modeled discharge data
show.errors	boolean, indicating whether to show error type bars
grid.nx	number of grid lines to be ploted (see grid)
...	additional parameters passed to plot

Details

box.plots: for each performance measure, a box plot is created showing the values for each cluster

errors.in.time: occurence of the errors cluster along the time dimension

peaks.in.clusters: table of the position of the synthetic peak errors in the clusters.

peaks.measures: responce of the performance measures to the synthetic peak errors.

scatterplot: scatter plot of the performance measures

See package vignette for further details about which plot does what.

Value

used for the side effect of plotting results

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)

new.order <- c(6,3,2,5,4,1)
correlated <- correlated(tiger.single, keep=c("CE","RMSE" ))

opar <- par(mfrow=c(3,5))
box.plots(tiger.single, solution=6, new.order=new.order, show.synthetic.peaks=TRUE)
box.plots(tiger.single, solution=6, new.order=new.order, show.cell=data.frame(x=1,y=1))
par(opar)
errors.in.time(xval=d.dates, result= tiger.single, solution=6, 
		show.months=TRUE, new.order=new.order)
peaks.in.clusters(tiger.single, solution=6, new.order=new.order)
peaks.measures(tiger.single, show.measures=correlated$measures.uniform$to.keep)
scatterplot(tiger.single$measures.uniform, show.measures=correlated$measures.uniform$to.keep)

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