Description Usage Arguments Details See Also Examples
This return a blockmatrix
object containing
the gaussian cross-correlation matrices.
1 | CCGammaToBlockmatrix(data, lag = 0, p = 3, ...)
|
data |
data frame or 'zoo' R object containing daily
precipitation time series for several gauges (one gauge
time series per column). See |
lag |
numeric (expressed as number of days) used for the element [1,1] of the returned blockmatrix. |
p |
numeric order $p$ of the auto-regeression |
... |
further argments of |
This a wrapper for CCGamma
with the option
only.matrix=TRUE
and the function value is
transformed into a blockmatrix
object.
CCGamma
,continuity_ratio
,omega_inv
,omega
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 38 39 40 41 42 43 44 45 46 47 48 | library(RMAWGEN)
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]
p <- 1 ## try p <- 2 !!!
CCGamma <- CCGammaToBlockmatrix(data=prec_mes,lag=0,p=p,tolerance=0.001)
## Not Run in the examples, uncomment to run the following line
# CCGamma_1 <- CCGammaToBlockmatrix(data=prec_mes,lag=1,p=p,tolerance=0.001)
### Alternatively, recommended .....
## Not Run in the examples, uncomment to run the following line
# CCGamma <- CCGammaToBlockmatrix(data=prec_mes,lag=0,p=p+1,tolerance=0.001)
# CCGamma0 <- CCGamma[1:p,1:p]
# CCGamma1 <- CCGamma[(1:p),(1:p)+1]
# CCGamma0_inv <- solve(CCGamma0)
## Not Run in the examples, uncomment to run the following line
#a1 <- blockmatmult(CCGamma0,CCGamma0_inv)
# a2 <- blockmatmult(CCGamma1,CCGamma0_inv)
# CCGamma_1t <- t(CCGamma1)
#CCGamma_0t <- t(CCGamma0)
# A <- t(solve(CCGamma_0t,CCGamma_1t))
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