minboundmatch {SpatialVx}

Minimum Boundary Separation Feature Matching

Description

Match identified features within a spatial verification set via their minimum boundary separation.

Usage

minboundmatch(x, type = c("single", "multiple"), mindist = Inf, verbose = FALSE, ...)

Arguments

x

An object of class “features”.

type

character string stating either “single” or “multiple”. In the former case, each feature in one field will be matched to only one feature in the other, which will be taken to be the features who have the smallest minimum boundary separation. In the case of “multiple”, the mindist argument should be set to something small enough so that not every feature will be matched to every other feature. Also, the MergeForce function may be useful in this case.

mindist

single numeric giving the minimum boundary separation distance (measured by grid squares) beyond which features should not be matched.

verbose

logical, should progress information be printed to the screen?

...

Optional arguments to the distmap function from package spatstat.

Details

the minimum boundary separation is calculated by first finding the distance map for every feature in the observed field, masking it by each feature in the forecast field, and then finding the minimum of the resulting masked distance map. If type is “single”, then the features are matched by the smallest minimum boundary separation per feature in each field. If type is “multiple”, then every feature is matched so long as their minimum boundary separation (measured in grid squares) is less than or equal to mindist.

Value

A list object of class “matched” is returned. If the type argument is “multiple”, then an implicite.merges component is included, which will work with the MergeForce function.

Author(s)

Eric Gilleland

See Also

deltamm, centmatch, MergeForce

Examples

x <- y <- matrix(0, 100, 100)
x[2:3,c(3:6, 8:10)] <- 1
y[c(4:7, 9:10),c(7:9, 11:12)] <- 1

x[30:50,45:65] <- 1
y[c(22:24, 99:100),c(50:52, 99:100)] <- 1

hold <- make.SpatialVx( x, y, field.type = "contrived", units = "none",
    data.name = "Example", obs.name = "x", model.name = "y" )

look <- FeatureFinder(hold, smoothpar=0.5)

look2 <- minboundmatch( look )

look2 <- MergeForce( look2 )

par( mfrow = c(1,2) )
plot( look2 )

look3 <- minboundmatch( look, type = "multiple", mindist = 50 )
look3 <- MergeForce( look2 )
plot( look3 )

look4 <- minboundmatch( look, type = "multiple", mindist = 20 )
look4 <- MergeForce( look4 )
plot( look4 )