This function enhances
conditionalQuantile by also considering
how other variables vary over the same intervals. Conditional quantiles are
very useful on their own for model evaluation, but provide no direct
information on how other variables change at the same time. For example, a
conditional quantile plot of ozone concentrations may show that low
concentrations of ozone tend to be under-predicted. However, the cause of
the under-prediction can be difficult to determine. However, by considering
how well the model predicts other variables over the same intervals, more
insight can be gained into the underlying reasons why model performance is
conditionalEval(mydata, obs = "obs", mod = "mod", var.obs = "var.obs", var.mod = "var.mod", type = "default", bins = 31, statistic = "MB", xlab = "predicted value", ylab = "statistic", col = brewer.pal(5, "YlOrRd"), col.var = "Set1", var.names = NULL, auto.text = TRUE, ...)
A data frame containing the field
The name of the observations in
The name of the predictions (modelled values) in
Other variable observations for which statistics should be
calculated. Can be more than length one e.g.
Other variable predictions for which statistics should be
calculated. Can be more than length one e.g.
It is also possible to choose
Number of bins used in
Statistic(s) to be plotted. Can be “MB”,
“NMB”, “r”, “COE”, “MGE”, “NMGE”,
“RMSE” and “FAC2”, as described in
label for the x-axis, by default
label for the y-axis, by default
Colours to be used for plotting the uncertainty bands and median line. Must be of length 5 or more.
Colours for the additional variables to be compared. See
Variable names to be shown on plot for plotting
Other graphical parameters passed onto
conditionalEval function provides information on how other
variables vary across the same intervals as shown on the conditional
quantile plot. There are two types of variable that can be considered by
setting the value of
statistic can be
another variable in the data frame. In this case the plot will show the
different proportions of
statistic across the range of predictions.
statistic = "season" will show for each interval of
mod the proportion of predictions that were spring, summer, autumn or
winter. This is useful because if model performance is worse for example at
high concentrations of
mod then knowing that these tend to occur
during a particular season etc. can be very helpful when trying to
understand why a model fails. See
cutData for more
details on the types of variable that can be
example would be
statistic = "ws" (if wind speed were available in
the data frame), which would then split wind speed into four quantiles and
plot the proportions of each.
conditionalEval can simultaneously plot the model performance
of other observed/predicted variable pairs according to different
model evaluation statistics. These statistics derive from the
modStats function and include “MB”, “NMB”,
“r”, “COE”, “MGE”, “NMGE”, “RMSE” and
“FAC2”. More than one statistic can be supplied e.g.
= c("NMB", "COE"). Bootstrap samples are taken from the corresponding
values of other variables to be plotted and their statistics with 95%
confidence intervals calculated. In this case, the model performance
of other variables is shown across the same intervals of
than just the values of single variables. In this second case the model
would need to provide observed/predicted pairs of other variables.
For example, a model may provide predictions of NOx and wind speed (for
which there are also observations available). The
function will show how well these other variables are predicted for the same
intervals of the main variables assessed in the conditional quantile e.g.
ozone. In this case, values are supplied to
var.obs (observed values
for other variables) and
var.mod (modelled values for other
variables). For example, to consider how well the model predicts NOx and
var.obs = c("nox.obs", "ws.obs") and
c("nox.mod", "ws.mod") would be supplied (assuming
ws.obs, ws.mod are present in the data frame). The analysis could show for
example, when ozone concentrations are under-predicted, the model may also
be shown to over-predict concentrations of NOx at the same time, or
under-predict wind speeds. Such information can thus help identify the
underlying causes of poor model performance. For example, an
under-prediction in wind speed could result in higher surface NOx
concentrations and lower ozone concentrations. Similarly if wind speed
predictions were good and NOx was over predicted it might suggest an
over-estimate of NOx emissions. One or more additional variables can be
A special case is
statistic = "cluster". In this case a data frame is
provided that contains the cluster calculated by
importTraj. Alternatively users could supply their own
pre-calculated clusters. These calculations can be very useful in showing
whether certain back trajectory clusters are associated with poor (or good)
model performance. Note that in the case of
statistic = "cluster"
there will be fewer data points used in the analysis compared with the
ordinary statistics above because the trajectories are available for every
three hours. Also note that
statistic = "cluster" cannot be used
together with the ordinary model evaluation statistics such as MB. The
output will be a bar chart showing the proportion of each interval of
mod by cluster number.
Far more insight can be gained into model performance through conditioning
type. For example,
type = "season" will plot conditional
quantiles and the associated model performance statistics of other variables
by each season.
type can also be a factor or character field e.g.
representing different models used.
See Wilks (2005) for more details of conditional quantile plots.
Wilks, D. S., 2005. Statistical Methods in the Atmospheric Sciences, Volume 91, Second Edition (International Geophysics), 2nd Edition. Academic Press.
## Examples to follow, or will be shown in the openair manual