Description Usage Arguments Value Note Author(s)
Either creates a VAR model or chooses a VAR model by using VAR or VARselect commands of vars
package
1 2 3 4 5 |
data |
see |
suffix |
see |
sep |
separator element. See |
p |
lag considered for the auto-regression see |
type |
see |
season |
see |
exogen |
see |
lag.max |
see |
ic |
see |
activateVARselect |
logical variables. If |
na.rm |
logical variables. If |
n_GPCA_iteration |
number of iterations of Gaussianization process for data. Default is 0 (no Gaussianization) |
n_GPCA_iteration_residuals |
number of iterations of Gaussianization process for data. Default is 0 (no Gaussianization) |
extremes |
see |
a varest2
or GPCAvarest2
object representing a VAR model or a GPCA-varest
object which also contains the GPCA transformation parameters
It inherits input parameters of VAR
, VARselect
and addsuffixes
. The variable data
contains the measured data on which the vector auto-regressive models is estimated.
It is a matrix where each row is a realization of the vector random variable.
In some application of this package, the random variables may be the daily maximum and minimum temperature anomalies for different stations.
Often the the columns of data
are called with the IDs of the stations whithout specifying the type of variable (e.g. minimun or maximum temperature anomalies).
This means that two or more columns may have the same name. Therefore the function addsuffixes
, which is called from this function, adds suitable suffixes to the column names.
Emanuele Cordano, Emanuele Eccel
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