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spatstat.linnet (version 3.2-2)

spatstat.linnet-internal: Internal spatstat.linnet functions

Description

Internal spatstat.linnet functions.

Usage


ApplyConnected(X, Engine, r, ..., rule, auxdata)
DoCountEnds(X, D, toler)
DoCountCrossEnds(X, I, J, DIJ, toler)
FDMKERNEL(lppobj, dtt, dtx, M, nsave, weights,
          stepnames, setuponly, verbose)
# S3 method for linfun
as.linfun(X, ...)
# S3 method for lintess
as.owin(W, ...)
# S3 method for lppm
getglmdata(object, ...)
# S3 method for lppm
getglmfit(object, ...)
# S3 method for lppm
getglmsubset(object, ...)
# S3 method for lppm
hasglmfit(object)
default.linnet.tolerance(L)
evaluateNetCovariate(covariate, locations, ...)
evaluateNetCovariateAlongNetwork(covariate, locations, ..., types)
evaluateNetCovariateAtPoints(covariate, locations, ..., allow.column)
exactlppm(X, baseline, ..., subset)
# S3 method for exactlppm
is.poisson(x)
# S3 method for exactlppm
is.stationary(x)
makeLinnetTolerance(toler)
# S3 method for exactlppm
predict(object, locations,...)
# S3 method for exactlppm
print(x, ...)
# S3 method for lintess
print(x, ...)
# S3 method for summary.linim
print(x, ...)
# S3 method for summary.linnet
print(x, ...)
# S3 method for summary.lintess
print(x, ...)
# S3 method for lintess
summary(object, ...)
# S3 method for exactlppm
response(object)
# S3 method for lintess
nobjects(x)
# S3 method for lintess
Window(X, ...)
# S3 method for linnet
Window(X, ..., check=TRUE) <- value
# S3 method for lpp
Window(X, ..., check=TRUE) <- value
densitypointsLPP(x, sigma, ...,
                 weights, nsigma, leaveoneout, fast,
                 fastmethod, floored,
                 dx, dt, iterMax, verbose, debug)
flatdensityfunlpp(X, ..., disconnect, weights, what)
flatdensityatpointslpp(X, ..., leaveoneout, disconnect, weights, what)
local2lpp(L, seg, tp, X, df.only)
looHeatLPP(U0, Amatrix, npts, niter, nsave,
           lixelweight, lixelmap, verbose) 
looVoronoiLPP(X)
validate.lpp.coords(X, fatal, context)
# S3 method for lppm
as.ppm(object)
pointsAlongNetwork(L, delta)
lineardiscEngine(L, x, r, want)
linearEuclidEngine(X, fun, ..., r, reweight, denom,
                   samplesize, showworking, correction)
linearKengine(X, ..., r, reweight, denom, samplesize,
              correction, ratio, showworking)
linearKmulti(X, I, J, r, ..., correction)
linearKmulti.inhom(X, I, J, lambdaI, lambdaJ, r, ..., correction,
             normalise, sigma)
linearpcfengine(X, ..., r, reweight, denom, samplesize, correction, ratio)
linearpcfmulti(X, I, J, r, ..., correction)
linearpcfmulti.inhom(X, I, J, lambdaI, lambdaJ, r, ...,
                     correction, normalise,
                     sigma, adjust.sigma, bw, adjust.bw)
linearKmultiEngine(X, I, J, ...,
                   r, reweight, denom, samplesize, correction, showworking)
linearPCFmultiEngine(X, I, J, ...,
                   r, reweight, denom, samplesize, correction, showworking)
resampleNetworkDataFrame(df, template)
# S3 method for lpp
resolve.lambda(X, lambda, subset, ...,
       update, leaveoneout, everywhere, loo.given, sigma, lambdaname)
sortalongsegment(df)
# S3 method for lppm
spatialCovariateEvidence(model, covariate, ..., lambdatype, 
          eps, dimyx, xy, rule.eps,
          delta, nd, interpolate, jitter, jitterfactor,
          modelname, covname, dataname, subset, clip.predict)
# S3 method for exactlppm
spatialCovariateEvidence(model, covariate, ...,
          lambdatype, interpolate, jitter, jitterfactor,
          modelname, covname, dataname, subset, clip.predict)
vnnFind(seg, tp, ns, nv, from, to, seglen, huge, tol, kmax)
ldtEngine(nv, ns, from, to, seglen, huge,
          coUXord, vnndist, vnnwhich, vnnlab)
resolve.heat.steps(sigma, ..., dx, dt,
                   niter, iterMax, nsave,
                   seglengths, maxdegree, AMbound, L,
                   finespacing, fineNsplit, fineNlixels,
                   W, eps, dimyx, xy, 
                   allow.adjust, warn.adjust,
                   verbose, stepnames)
rmaxEuclidean(L, verbose, show)
qkdeEngine(x, sigma, ..., at, what,
           leaveoneout, diggle, raw, edge2D, edge,
           weights, varcov, positive, shortcut,
           precomputed, savecomputed)
# S3 method for lppm
updateData(model, X, ...)
Math(x, ...)
Ops(e1, e2)
Complex(z)
Summary(..., na.rm = FALSE)




LinimOp(e1, e2, op)
LinimListOp(e1, e2, op)
traceTessLinnet(A, L)

Arguments

Value

The return values of these functions are not documented, and may change without warning.

Details

These internal spatstat.linnet functions should not be called directly by the user. Their names and capabilities may change without warning from one version of spatstat.linnet to the next.