valueMeasure | R Documentation |
VALUE measure calculation for climate4R grids
valueMeasure(
y,
x,
measure.code,
index.code = NULL,
return.NApercentage = TRUE,
parallel = FALSE,
max.ncores = 16,
condition = NULL,
threshold = NULL,
which.wetdays = NULL,
ncores = NULL
)
y |
Grid (also station data) of observations |
x |
Grid (also station data) of the grid that is being validated |
measure.code |
characher of the measure code to be computed (use VALUE::show.measures) |
index.code |
Default is NULL. characher of the index code to be computed (use VALUE::show.indices). |
return.NApercentage |
Logical to also return or not a grid containing NA percentage information. |
parallel |
Logical. Should parallel execution be used? |
max.ncores |
Integer. Upper bound for user-defined number of cores. |
condition |
Inequality operator to be applied to the given |
threshold |
Numeric value. Threshold used as reference for the condition. Default is NULL. If a threshold value is supplied with no specificaction of the argument |
which.wetdays |
A string, default to NULL. Infer the measure/index taiking into account only the wet days of the temporal serie. As there are two temporal series (i.e., x and y), the subsetting can be done according to the observed (i.e., y) serie, to the intersection of the both wet days subsets or finally subset each serie according to its own wet days subset. The possible values are c("Observation","Intersection","Independent"). When performing an index instead of a measure the only possible value is "Independent". |
ncores |
Integer number of cores used in parallel computation. Self-selected number of
cores is used when |
Some measures are computed directly from the original time series (e.g. temporal correlation),
whereas others are computed upon previouly computed indices (e.g. mean bias).
Thus, argument index.code
must be provided for the latter case.
A grid of the index or a list containing the grid of the index and the grid of NA percenatage
M. Iturbide
require(transformeR)
require(climate4R.datasets)
data(EOBS_Iberia_tas)
data(CFS_Iberia_tas)
y <- EOBS_Iberia_tas
x <- CFS_Iberia_tas
bias <- valueMeasure(y, x, measure.code = "bias", index.code = "mean")
str(bias$Measure)
str(bias$NAmeanPercentage)
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