plots {tiger} | R Documentation |
Create various plot to understand the temporal dynamics of model performance
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, 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, legend.pos = "topleft", ...) 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))) scatterplot(measures, show.measures=1:num.measures) p.validityIndex(result, validity.max)
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 ) |
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 |
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 |
... |
additional parameters passed to plot |
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.
used for the side effect of plotting results
Dominik Reusser
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.
The package vignette
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)