FeatureMatchAnalyzer {SpatialVx}

Analyze Features of a Verification Set

Description

Analyze matched features of a verification set.

Usage

FeatureMatchAnalyzer(x, which.comps=c("cent.dist", "angle.diff", "area.ratio", "int.area",
                    "bdelta", "haus", "ph", "med", "msd", "fom", "minsep",
		    "bearing"), sizefac=1, alpha=0.1, k=4, p=2, c=Inf,
		    distfun="distmapfun", ...)

## S3 method for class 'matched.centmatch'
FeatureMatchAnalyzer(x, which.comps=c("cent.dist", "angle.diff",
		    "area.ratio", "int.area", "bdelta", "haus", "ph", "med",
		    "msd", "fom", "minsep", "bearing"), sizefac=1, alpha=0.1, k=4, p=2,
		    c=Inf, distfun="distmapfun", ...)

## S3 method for class 'matched.deltamm'
FeatureMatchAnalyzer(x, which.comps = c("cent.dist", "angle.diff",
		    "area.ratio", "int.area", "bdelta", "haus", "ph", "med", "msd",
		    "fom", "minsep", "bearing"), sizefac = 1, alpha = 0.1, k = 4, p = 2,
		    c = Inf, distfun = "distmapfun", ..., y = NULL, matches = NULL,
		    object = NULL)

## S3 method for class 'FeatureMatchAnalyzer'
summary(object, ...)

## S3 method for class 'FeatureMatchAnalyzer'
plot(x, ..., type = c("all", "ph", "med", "msd", 
    "fom", "minsep", "cent.dist", "angle.diff", "area.ratio",
    "int.area", "bearing", "bdelta", "haus"))

## S3 method for class 'FeatureMatchAnalyzer'
print(x, ...)

FeatureComps(Y, X, which.comps=c("cent.dist", "angle.diff", "area.ratio", "int.area",
    "bdelta", "haus", "ph", "med", "msd", "fom", "minsep", "bearing"),
    sizefac=1, alpha=0.1, k=4, p=2, c=Inf, distfun="distmapfun", deg = TRUE,
    aty = "compass", loc = NULL, ...)

## S3 method for class 'FeatureComps'
distill(x, ...)

Arguments

x, y, matches

x, y and matches are list objects with components as output by deltamm or similar function. Only one is used, and it first checks for matches, then y, and finally x. It expects a component named mm.new.labels that gives the number of matched objects. In the case of the plot and print method functions, x is a list object as returned by FeatureMatchAnalyzer.

distill: output from FeatureComps.

X, Y

list object giving a pixel image as output from solutionset from package spatstat for the verification and forecast fields, resp. These arguments are passed directly to the locperf function.

object

list object returned of class “FeatureMatchAnalyzer”, this is the returned value from the self-same function.

which.comps, type

character vector indicating which properties of the features are to be analyzed (which.comps) or plotted (type).

sizefac

single numeric by which area calculations should be multiplied in order to get the desired units. If unity (default) results are in terms of grid squares.

alpha

numeric value for the FOM measure (see the help file for locperf.

k

numeric indicating which quantile to use if the partial Hausdorff measure is to be used.

p

numeric giving the value of the parameter p for the Baddeley metric.

c

numeric giving the cut-off value for the Baddeley metric.

distfun

character naming a distance functions to use in calculating the various binary image measures. Default is Euclidean distance.

deg, aty

optional arguments to the bearing function.

loc

two-column matrix giving location coordinates for centroid distance. If NULL, uses an indices based on the dimension of the field.

...

Additional arguments to deltametric from package spatstat. In case of the summary method function, additional optional arguments may be passed, which include silent (logical, should the information be printed to the screen or not?), interest (numeric vector defining an interest value for calculating total interest for each matched object, if NULL, this is not performed), con (name of function that takes three arguments, the first two are matrices whose rows are objects and columns are matched feature properties, where the former is a matrix of matched feature property values (e.g., angle difference) and the latter is a matrix of interest values determined by the interest argument (whereby each row is identical), the third argument to con must be called which.comps, and it gives the short-form feature property names (i.e., same as which.comps argument); see details section). In the case of the plot method function, these are optional arguments to the function barplot.

Not used by distill.

Details

FeatureMatchAnalyzer operates on objects of class “matched”. It is set up to calculate the values discussed in sec. 4 of Davis et al. (2006) for a single verification set (i.e., mean and standard deviation are not computed because it is only a single case). If criteria is 1, then features separated by a distance D < the sum of the sizes of the two features (size of a feature is defined as the square root of its area) are considered a match. If criteria is 2, then a match is made if D < the average of the sizes of the two features. Finally, criteria 3 decides a match as being anything less than a pre-determined constant.

FeatureComps is the primary function called by FeatureMatchAnalyzer, and is designed as a more stand-alone type of function. Several of the measures that can be calculated are simply the binary image measures/metrics available via, e.g., locperf. It calculates comparisons between two matched features (i.e., between the verification and forecast fields).

distill reduces a “FeatureComps” list object to a named numeric vector containing (in this order) the components that exist from "cent.dist", "angle.diff", "area.ratio", "int.area", "bdelta", "haus", "ph", "med", "msd", "fom", and "minsep". This is used, for example, by interester, which is why the order is important.

The summary method function for FeatureMatchAnalyzer allows for passing a function, con, to determine confidence for each interest value. The idea being to set the interest to zero when the particular interest value does not make sense. For example, angle difference makes no sense if both objects are circles. Currently, no functions are included in this package for actually doing this, and so the functionality itself has not been tested.

The print method function for FeatureMatchAnalyzer first converts the object to a simple named matrix, then prints the matrix out. The resulting matrix is returned invisibly.

Value

FeatureMatchAnalyzer returns a list of list objects. The specific components depend on the 'which.comps' argument, and are the same as those returned by FeatureComps. These can be any of the following.

cent.dist

numeric giving the centroid (Euclidean) distance.

angle.diff

numeric giving the orientation (major axis) angle difference.

area.ratio

numeric giving the area ratio, which is always between 0 and 1 because this is defined by Davis et al. (2006) to be the area of the smaller feature divided by that of the larger feature regardless of which field the feature belongs to.

int.area

numeric giving the intersection area of the features.

bdelta

numeric giving Baddeley's delta metric between the two features.

haus, ph, med, msd, fom, minsep

numeric, see locperf for specific information.

bearing

numeric giving the bearing from the forecast object centroid to the observed object centroid.

The summary method for FeatureMatchAnalyzer invisibly returns a matrix with the same information, but where each matched object is a row and each column is the specific statistic. Or, if optional interest argument is passed, a list with components:

print returns a named vector invisibly.

Author(s)

Eric Gilleland

References

Davis, C. A., Brown, B. G. and Bullock, R. G. (2006) Object-based verification of precipitation forecasts, Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 1772–1784.

See Also

Functions to identify features: FeatureFinder

Functions to merge and/or match objects: deltamm, centmatch, MergeForce

Functions to compute feature properties: locperf, deltametric, bearing

Function to calculate fuzzy logic interest values: interester

Examples

data( "ExampleSpatialVxSet" )

x <- ExampleSpatialVxSet$vx
xhat <- ExampleSpatialVxSet$fcst

hold <- make.SpatialVx( x, xhat, field.type="Example",
    units = "units", data.name = "Example", 
    obs.name = "x", model.name = "xhat" )

look <- FeatureFinder(hold, smoothpar=1.5)
look2 <- centmatch(look)

tmp <- FeatureMatchAnalyzer(look2)
tmp
summary(tmp)
plot(tmp)