SpatialVx (version 0.3)

EBS: 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, ..., set.pw = FALSE, 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.

set.pw

logical, should the plotting panels be set by the function?

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.

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.

Details

this is a wrapper function for the spatbiasFS function utilizing the SpatialVx object class to simplify the arguments.

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=c("GFS/NAM", "Reference", "Model"))

look <- EBS(hold, bootR=500, ntrials=500, verbose=TRUE)
plot(look, set.pw=TRUE)

# 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=c("GFS/NAM", "Reference", "Model"))

look <- EBS(hold, bootR=500, ntrials=500, verbose=TRUE)
plot(look, set.pw=TRUE)