Description Usage Arguments Value Note Author(s) See Also Examples
This function calculates ...
1 2 |
data |
data frame or 'zoo' R object containing daily
precipitation time series for several gauges (one gauge
time series per column). See |
CCGamma0 |
correlation block-matrix with lag of 0
days. Object returned by
|
CCGamma1 |
correlation block-matrix with lag of 1
days. Object returned by
|
p |
numeric order $p$ of the auto-regeression, see
|
sample |
character string indicated if the
coefficients must be estimated differently for subset of
the year, e.g. monthly. Admitted values are |
origin |
character string (yyyy-dd-mm) indicated the
date of the first row of |
... |
other arguments of
|
A S3 object of class
"YuleWalkerCoefficientBlockmatrices"
(or
"YuleWalkerCoefficientBlockmatricesPerEachMonth"
in
case sample="monthly"
) which is a list containing
the block matrices A
,Sigma_u
of the
Yule-Walker Equation and the object CCGammaInfo
containing probabilities of no precipitation occurence and
returned by function CCGamma
applied with
lag=0
. In case sample="monthly"
) functioion
return a
"YuleWalkerCoefficientBlockmatricesPerEachMonth"
, i.
e. a list of "YuleWalkerCoefficientBlockmatrices"
for each month.
This function uses Yule-Walker equations for VAR to
estimate the coefficient block-matrices blockmatrix
A
and Sigma_u
. The input of this function are
the correletion block-matrices CCGamma0
and
CCGamma1
. If they are missing (and then NULL
)
, they are also calculated from the original dataset
(argument data
). In this last case, the coefficients
can be estiomated differently for each monthly setting
sample
equal to "monthly"
.
Emanuele Cordano
CCGammaToBlockmatrix
,generatePrecipitationAmount
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | library(RMRAINGEN)
data(trentino)
year_min <- 1961
year_max <- 1990
period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period,station]
## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
accepted[it] <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}
prec_mes <- prec_mes[,accepted]
## the dateset is reduced!!!
prec_mes <- prec_mes[,1:2]
# ## Not Run in the examples, uncomment to run the following line
# coeff <- CoeffYWeq(data=prec_mes,p=1,tolerance=0.001)
#
#
# Alternatively the coefficients of Vector Auto-Regressive Model
# can be separately calculated for each month
# ## Not Run in the examples, uncomment to run the following line
#origin <- paste(year_min,1,1,sep="-")
#
#
#coeff_monthly <- CoeffYWeq(data=prec_mes,p=1,tolerance=0.001,sample="monthly",origin=origin)
|
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