EBS {SpatialVx}

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 EBS.

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 par). If NULL, then the parameter is not re-set.

col, horizontal

optional arguments to image.plot from fields.

verbose

logical, should progress information be printed to the screen?

...

optional arguments to image.plot from fields.

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)