Apply field significance method of Elmore et al. (2006).
1 2 3 4 5 6 7 8 
X,Y 
m by n matrices giving the verification and forecast fields, resp., for each of m time points (rows) and n locations (columns). 
x,object 
list object as returned by 
loc 
optional (for subsequent plotting) n by 2 matrix giving the lon/lat coordinates for the locations. 
block.length 
numeric giving the block length to be used n the block bootstrap algorithm. If NULL, floor(sqrt(n)) is used. 
alpha.boot 
numeric between 0 and 1 giving the confidence level desired for the bootstrap algorithm. 
field.sig 
numeric between 0 and 1 giving the desired field significance level. 
bootR 
numeric integer giving the number of bootstrap replications to use. 
ntrials 
numeric integer giving the number of Monte Carol iterations to use. 
verbose 
logical, should progress information be printed to the screen? 
... 
not used. 
See Elmore et al. (2006) for details.
A list object with components:
data.name 
character vector giving the name of the verification and forecast spatiotemporal fields used, and the associated location object (if not NULL). 
block.boot.results 
object of class LocSig 
sig.results 
list object containing information about the significance of the results. 
field.significance,alpha.boot 
field significance level and bootstrap CI level as input by field.sig alpha.boot arguments. 
bootR,ntrials 
same as arguments above. 
Eric Gilleland and Kimberly L. Elmore
Elmore, K. L., Baldwin, M. E. and Schultz, D. M. (2006) Field significance revisited: Spatial bias errors in forecasts as applied to the Eta model. Mon. Wea. Rev., 134, 519–531.
MCdof
, LocSig
, tsboot
1 2 3 4 5 6 7 8 9 10  data(GFSNAMfcstEx)
data(GFSNAMobsEx)
data(GFSNAMlocEx)
id < GFSNAMlocEx[,"Lon"] >=95 & GFSNAMlocEx[,"Lon"] <= 75 & GFSNAMlocEx[,"Lat"] <= 32
loc < GFSNAMlocEx[id,]
GFSobsSub < GFSNAMobsEx[,id]
GFSfcstSub < GFSNAMfcstEx[,id]
look < spatbiasFS(GFSobsSub, GFSfcstSub, loc=loc, bootR=500, ntrials=500)
plot(look)
summary(look)

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