# Elmore, Baldwin and Schultz Method for Field Significance for Spatial Bias Errors Apply the method of Elmore, Baldwin and Schultz (2006) for calculating field significance of spatial bias errors. ```r 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. ## Returns 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. ## 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. ## Author(s) Eric Gilleland ## See Also `boot::boot`, `boot:tsboot`, `spatbiasFS`, `LocSig`, `make.SpatialVx` ## Examples ```r 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) ```

EBS function