Function for creating an Exponential basis function for a spatial smooth using the CReSS method.

This function calculates a local radial exponential basis matrix for use in runSALSA2D.

Usage

LRF.e(radiusIndices, dists, radii, aR)

Arguments

radiusIndices: Vector of length startKnots identifying which radii (splineParams[1]$radii) will be used to initialise the model
dists: Matrix of distances between data locations and knot locations (n x k). May be Euclidean or geodesic distances.
radii: Sequence of range parameters for the CReSS basis from local (small) to global (large). Determines the range of the influence of each knot.
aR: Index of knot locations. The index contains numbers selected by SALSA from 1 to the number of legal knot locations na.omit(knotgrid). Used to specify which columns of dists should be used to construct the basis matrix.

Value

Returns a basis matrix with one column for each knot in aR and one row for every observation (i.e. same number of rows as dists)

Details

Calculate a local radial basis matrix for use in runSALSA2D. The distance matrix input may be Euclidean or geodesic distances.

Examples


# load data
data(ns.data.re)
# load knot grid data
data(knotgrid.ns)

splineParams<-makesplineParams(data=ns.data.re, varlist=c('observationhour'))

#set some input info for SALSA
ns.data.re$response<- ns.data.re$birds

# make distance matrices for datatoknots and knottoknots
distMats<-makeDists(cbind(ns.data.re$x.pos, ns.data.re$y.pos), na.omit(knotgrid.ns), knotmat=FALSE)

# choose sequence of radii
r_seq<-getRadiiChoices(8, distMats$dataDist)
#> Error in getRadiiChoices(8, distMats$dataDist): argument "basis" is missing, with no default

# using the fourth radius and picking 5 knots
basis<-LRF.e(radiusIndices=rep(4, 5), dists=distMats$dataDist, radii = r_seq, 
        aR=c(3, 10, 15, 28, 31))
#> Error in LRF.e(radiusIndices = rep(4, 5), dists = distMats$dataDist, radii = r_seq,     aR = c(3, 10, 15, 28, 31)): object 'r_seq' not found