synthesize {antaresProcessing} | R Documentation |
This function takes as input an object of class antaresData
containing
detailed results of a simulation and creates a synthesis of the results.
The synthesis contains the average value of each variable over Monte-Carlo
scenarios and eventually other aggregated statistics
synthesize(x, ..., prefixForMeans = "", useTime = TRUE)
x |
an object of class |
... |
Additional parameters indicating which additional statistics to produce. See details to see how to specify them. |
prefixForMeans |
Prefix to add to the columns containing average values. If it is different than "", a "_" is automatically added. |
useTime |
use times columns for synthesize. |
Additional statistics can be asked in three different ways:
A character string in "min", "max", "std", "median" or "qXXX" where "XXX" is a real number between 0 and 100. It will add for each column respectively the minimum or maximum value, the standard deviation, the median or a quantile.
A named argument whose value is a function or one of the previous
aliases. For instance med = median
will calculate the median of
each variable. The name of the resulting column will be prefixed by
"med_". Similarly, l = "q5"
will compute the 5
each variable and put the result in a column with name prefixed by "l_"
A named argument whose value is a list. It has to contain an element
fun
equal to a function or an alias and optionally an element
only
containing the names of the columns to which to apply the function.
For instance med = list(fun = median, only = c("LOAD", "MRG. PRICE"))
will compute the median of variables "LOAD" and "MRG. PRICE". The result
will be stored in columns "med_LOAD" and "med_MRG. PRICE".
The computation of custom statistics can take some time, especially with hourly data. To improve performance, prefer the third form and compute custom statistics only on a few variables.
Synthetic version of the input data. It has the same structure as x
except that column mcYear
has been removed. All variables are
averaged across Monte-Carlo scenarios and eventually some additional columns
have been added corresponding to the requested custom statistics.
## Not run: mydata <- readAntares("all", timeStep = "annual") synthesize(mydata) # Add minimum and maximum for all variables synthesize(mydata, "min", "max") # Compute a custom statistic for all columns synthesize(mydata, log = function(x) mean(log(1 + x))) # Same but only for column "LOAD" synthesize(mydata, log = list(fun = function(x) mean(log(1 + x)), only = "LOAD")) # Compute the proportion of time balance is positive synthesize(mydata, propPos = list(fun = function(x) mean(x > 0), only = "BALANCE")) # Compute 95% confidence interval for the marginal price synthesize(mydata, l = list(fun = "q2.5", only = "MRG. PRICE"), u = list(fun = "q97.5", only = "MRG. PRICE")) ## End(Not run)