Nothing
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Function:
# fds2s() and fds3s() functions use a linear regression model for
# statistical estimation of the mass fractal dimension of sampling
# clusters on 2D & 3D square lattice.
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Arguments:
# rfq - matrix of relative sampling frequencies
# for sites of the 2D & 3D percolation lattice;
# bnd - list of boundary coordinates and radii
# for the isotropic 2D & 3D set cover.
# Variables:
# w, r - relative frequency sums and lengths for
# the iso- & anisotropic 2D & 3D set cover.
# Value:
# - linear regression model for statistical estimation of the mass
# fractal dimension of sampling clusters on 2D & 3D square lattice.
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
require(SPSL)
fds2s <- function(rfq=fssi20(x=95),
bnd=isc2s(k=12, x=dim(rfq))) {
w <- rep(0, times=ncol(bnd))
for (i in seq(ncol(bnd)))
w[i] <- sum(rfq[bnd["x1",i]:bnd["x2",i],
bnd["y1",i]:bnd["y2",i]])
r <- log(bnd["r",])
w <- log(w)
return(lm(w ~ r))
}
fds3s <- function(rfq=fssi30(x=95),
bnd=isc3s(k=12, x=dim(rfq))) {
w <- rep(0, times=ncol(bnd))
for (i in seq(ncol(bnd)))
w[i] <- sum(rfq[bnd["x1",i]:bnd["x2",i],
bnd["y1",i]:bnd["y2",i],
bnd["z1",i]:bnd["z2",i]])
r <- log(bnd["r",])
w <- log(w)
return(lm(w ~ r))
}
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