EBS {SpatialVx} | R Documentation |
Elmore, Baldwin and Schultz Method for Field Significance for Spatial Bias Errors
Description
Apply the method of Elmore, Baldwin and Schultz (2006) for calculating field significance of spatial bias errors.
Usage
EBS(object, model = 1, block.length = NULL, alpha.boot = 0.05,
field.sig = 0.05, bootR = 1000, ntrials = 1000,
verbose = FALSE)
## S3 method for class 'EBS'
plot(x, ..., mfrow = c(1, 2), col, horizontal)
Arguments
object |
list object of class “SpatialVx”. |
x |
object of class “EBS” as returned by |
model |
number or character describing which model (if more than one in the “SpatialVx” object) to compare. |
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. |
mfrow |
mfrow parameter (see help file for |
col , horizontal |
optional arguments to |
verbose |
logical, should progress information be printed to the screen? |
... |
optional arguments to |
Details
this is a wrapper function for the spatbiasFS
function utilizing the “SpatialVx” object class to simplify the arguments.
Value
A list object of class “EBS” with the same attributes as the input object and additional attribute (called “arguments”)that is a named vector giving information provided by the user. Components of the list include:
block.boot.results |
object of class “LocSig”. |
sig.results |
list object containing information about the significance of the results. |
Author(s)
Eric Gilleland
References
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.
See Also
boot
, tsboot
, spatbiasFS
, LocSig
, poly.image
, image.plot
, make.SpatialVx
Examples
data( "GFSNAMfcstEx" )
data( "GFSNAMobsEx" )
data( "GFSNAMlocEx" )
id <- GFSNAMlocEx[,"Lon"] >=-95
id <- id & GFSNAMlocEx[,"Lon"] <= -75
id <- id & GFSNAMlocEx[,"Lat"] <= 32
##
## This next step is a bit awkward, but these data
## are not in the format of the SpatialVx class.
## These are being set up with arbitrarily chosen
## dimensions (49 X 48) for the spatial part. It
## won't matter to the analyses or plots.
##
Vx <- GFSNAMobsEx
Fcst <- GFSNAMfcstEx
Ref <- array(t(Vx), dim=c(49, 48, 361))
Mod <- array(t(Fcst), dim=c(49, 48, 361))
hold <- make.SpatialVx(Ref, Mod, loc=GFSNAMlocEx,
projection=TRUE, map=TRUE, loc.byrow = TRUE, subset=id,
field.type="Precipitation", units="mm",
data.name = "GFS/NAM", obs.name = "Reference", model.name = "Model" )
look <- EBS(hold, bootR=500, ntrials=500, verbose=TRUE)
plot( look )
## Not run:
# Same as above, but now we'll do it for all points.
# A little slower, but not terribly bad.
hold <- make.SpatialVx(Ref, Mod, loc = GFSNAMlocEx,
projection = TRUE, map = TRUE, loc.byrow = TRUE,
field.type = "Precipitation", reg.grid = FALSE, units = "mm",
data.name = "GFS/NAM", obs.name = "Reference", model.name = "Model" )
look <- EBS(hold, bootR=500, ntrials=500, verbose=TRUE)
plot( look )
## End(Not run)