convert2prob | R Documentation |
convert2prob
Converts the continuous ensemble forecast to
counts of ensemble members per category. The categories can be defined
relative to the ensemble distribution (using prob
) or relative to
absolute values for the category thresholds (using threshold
, see
details). prob2thresh
converts the relative threshold to absolute
thresholds for later processing. expandthresh
expands the vector or
matrix of thresholds to fit the input data.
convert2prob(
x,
prob = NULL,
threshold = NULL,
ref.ind = NULL,
multi.model = FALSE
)
prob2thresh(x, prob, ref.ind = NULL, multi.model = FALSE)
expandthresh(threshold, x)
x |
input vector or matrix |
prob |
thresholds for categorical forecasts (defaults to NULL) |
threshold |
absolute thresholds for categorical forecasts (defaults to NULL) |
ref.ind |
list of forecast/obs instances to be used to estimate percentile thresholds |
multi.model |
logical, are we dealing with initial condition (the default) or multi-model ensembles (see details)? |
In case both prob
and threshold
are set to
NULL
, the function returns the input x
without modification.
If prob
is set, a matrix with the number of occurrences per class for
a given quantile of the full distribution (e.g. temperature above/below the
median). If threshold
is set, the classes are defined based on the
absolute value (e.g. temperature above/below 13 deg. C). Multiple classes
are
Only certain formats of threshold
and prob
are supported.
prob
has to be a vector with percentile thresholds separating the
different classes. threshold
can be a vector, matrix or array with
the first entry corresponding to the different classes, and the last to the
different ensemble members (if present). Thereby, time/forecast varying
thresholds can potentially be supplied (although I am not sure this is
useful or needed).
If ref.ind
is specified, only the specified indices of the input
variables are used to estimate the percentile thresholds (prob
). If
used with threshold
, or without anything, ref.ind
has no effect.
If multi.model = TRUE
, the relative thresholds supplied by
prob
are ensemble member specific, i.e. are estimated for each
ensemble member separately. This is in particular applicable for
multi-model ensembles with model dependent biases.
Matrix of occurences per class (i.e. the number of ensemble members per class, or an indicator for the observations)
veriApply
tm <- toymodel()
## convert to tercile forecasts (only display first forecast and obs)
convert2prob(tm$fcst, prob = 1:2 / 3)[1, ]
convert2prob(tm$obs, prob = 1:2 / 3)[1, ]
## convert to category forecasts (smaller and larger than 1)
convert2prob(tm$fcst, threshold = 1)[1, ]
convert2prob(tm$obs, threshold = 1)[1, ]
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