Description Usage Arguments Value Author(s) Examples
MVR solves the equation
Y = Psi X
and estimates
Psi
by inverting the equation. Predictions give the varlue of Y, given this matrix and some input. MVR is useful for data where Y contains several time series where the spatial coherence/covariance is important to reproduce. For instance, Y may be a combination of stations, the two wind components from one station, or a set of different elements from a group of stations.
1 2 3 4 5 6 |
Y |
An object with climate data: field, eof, or pca. |
X |
Same as Y or any zoo object. |
SVD |
Use a singular value decomposition as a basis for the PCA. |
i.eofs |
Which EOFs to include in the CCA. |
LINPACK |
an option for |
object |
The result from CCA. |
newdata |
The same as X. |
A CCA object: a list containing a.m, b.m, u.k, v.k, and r, describing the Canonical Correlation variates, patterns and correlations. a.m and b.m are the patterns and u.k and v.k the vectors (time evolution).
R.E. Benestad
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 | ## Not run:
# Example for using EOF and MVR
slp <- slp.NCEP(lat=c(-40,40),anomaly=TRUE)
sst <- sst.NCEP(lat=c(-40,40),anomaly=TRUE)
eof.1 <- EOF(slp,mon=1)
eof.2 <- EOF(sst,mon=1)
mvr <- MVR(eof.1,eof.2)
plot(mvr)
# Example for using PCA and MVR
oslo <- station(src="NACD",loc="Oslo")
bergen <- station.nacd("Bergen")
stockholm <- station.nacd("Stockholm")
copenhagen <- station.nacd("Koebenhavn")
helsinki <- station.nacd("Helsinki")
reykjavik <- station.nacd("Stykkisholmur")
edinburgh <- station.nacd("Edinburgh")
debilt <- station.nacd("De_Bilt")
uccle <- station.nacd("Uccle")
tromso <- station.nacd("Tromsoe")
falun <- station.nacd("Falun")
stensele <- station.nacd("Stensele")
kuopio <- station.nacd("Kuopio")
valentia <- station.nacd("Valentia")
X <- combine(oslo,bergen,stockholm,copenhagen,helsinki,reykjavik,
edinburgh,debilt,uccle,tromso,falun,stensele,kuopio,valentia)
pca <- PCA(X)
slp <- slp.NCEP(lon=c(-20,30),lat=c(30,70))
eof <- EOF(slp)
mvr <- MVR(pca,eof)
plot(mvr)
# Find the teleconnection pattern to the NAO
data("NAOI")
data("sunspots")
data("NINO3.4")
X <- merge(NAOI,sunspots,NINO3.4,all=FALSE)
mvr <- MVR(pca,X)
# Find the pattern for NAOI:
teleconnection <- predict(mvr,newdata= c(1,0,0))
map(teleconnection,cex=2)
## End(Not run)
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