# fdc2s: Mass fractal dimension of a 2D cluster In SECP: Statistical Estimation of Cluster Parameters

 fdc2s R Documentation

## Mass fractal dimension of a 2D cluster

### Description

`fdc2s()` function uses a linear regression model for statistical estimation of the mass fractal dimension of a cluster on 2D square lattice with iso- & anisotropic sets cover.

### Usage

```fdc2s(acc=ssi20(x=95), bnd=isc2s(k=12, x=dim(acc)))
```

### Arguments

 `acc` an accessibility matrix for 2D square percolation lattice. `bnd` bounds for the iso- or anisotropic set cover.

### Details

The mass fractal dimension for a cluster is equal to the coefficient of linear regression between `log(n)` and `log(r)`, where `n` is an absolute frequency of the total cluster sites which are bounded elements of iso- & anisotropic sets cover.

The isotropic set cover on 2D square lattice is formed from scalable squares with variable sizes `2r+1` and a fixed point in the lattice center.

The anisotropic set cover on 2D square lattice is formed from scalable rectangles with variable sizes `r+1` and a fixed edge along the lattice boundary.

The percolation is simulated on 2D square lattice with uniformly weighted sites and the constant parameter `p`.

The isotropic cluster is formed from the accessible sites connected with initial sites subset.

If `acc[e]<p` then `e` is accessible site; if `acc[e]==1` then `e` is non-accessible site; if `acc[e]==2` then `e` belong to a sites cluster.

### Value

A linear regression model for statistical estimation of the mass fractal dimension of a cluster on 2D square lattice with iso- & anisotropic sets cover.

### Author(s)

Pavel V. Moskalev

### References

Moskalev P.V., Grebennikov K.V. and Shitov V.V. (2011) Statistical estimation of percolation cluster parameters. Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, No.1 (January-June), pp.29-35, arXiv:1105.2334v1; in Russian.

### See Also

fdc3s, fds2s, fds3s

### Examples

```# # # # # # # # # # # # # # # # #
# Example 1: Isotropic set cover
# # # # # # # # # # # # # # # # #
pc <- .592746
p1 <- pc - .03
p2 <- pc + .03
lx <- 33; ss <- (lx+1)/2
set.seed(20120627); ac1 <- ssi20(x=lx, p=p1)
set.seed(20120627); ac2 <- ssi20(x=lx, p=p2)
bnd <- isc2s(k=9, x=dim(ac1))
fd1 <- fdc2s(acc=ac1, bnd=bnd)
fd2 <- fdc2s(acc=ac2, bnd=bnd)
n1 <- fd1\$model[,"n"]; n2 <- fd2\$model[,"n"]
r1 <- fd1\$model[,"r"]; r2 <- fd2\$model[,"r"]
rr <- seq(min(r1)-.2, max(r1)+.2, length=100)
nn1 <- predict(fd1, newdata=list(r=rr), interval="conf")
nn2 <- predict(fd2, newdata=list(r=rr), interval="conf")
s1 <- paste(round(confint(fd1)[2,], digits=3), collapse=", ")
s2 <- paste(round(confint(fd2)[2,], digits=3), collapse=", ")
x <- y <- seq(lx)
par(mfrow=c(2,2), mar=c(3,3,3,1), mgp=c(2,1,0))
image(x, y, ac1, cex.main=1,
main=paste("Isotropic set cover and a 2D cluster of\n",
"sites with (1,0)-neighborhood and p=",
round(p1, digits=3), sep=""))
rect(bnd["x1",], bnd["y1",], bnd["x2",], bnd["y2",])
abline(h=ss, lty=2); abline(v=ss, lty=2)
image(x, y, ac2, cex.main=1,
main=paste("Isotropic set cover and a 2D cluster of\n",
"sites with (1,0)-neighborhood and p=",
round(p2, digits=3), sep=""))
rect(bnd["x1",], bnd["y1",], bnd["x2",], bnd["y2",])
abline(h=ss, lty=2); abline(v=ss, lty=2)
plot(r1, n1, pch=3, ylim=range(c(n1,n2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s1,")", sep=""))
matlines(rr, nn1, lty=c(1,2,2), col=c("black","red","red"))
plot(r2, n2, pch=3, ylim=range(c(n1,n2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s2,")", sep=""))
matlines(rr, nn2, lty=c(1,2,2), col=c("black","red","red"))

## Not run:
# # # # # # # # # # # # # # # # #
# Example 2: Anisotropic set cover, dir=2
# # # # # # # # # # # # # # # # #
pc <- .592746
p1 <- pc - .03
p2 <- pc + .03
lx <- 33; ss <- (lx+1)/2; ssy <- seq(lx+2, 2*lx-1)
set.seed(20120627); ac1 <- ssi20(x=lx, p=p1, set=ssy, all=FALSE)
set.seed(20120627); ac2 <- ssi20(x=lx, p=p2, set=ssy, all=FALSE)
bnd <- asc2s(k=9, x=dim(ac1), dir=2)
fd1 <- fdc2s(acc=ac1, bnd=bnd)
fd2 <- fdc2s(acc=ac2, bnd=bnd)
n1 <- fd1\$model[,"n"]; n2 <- fd2\$model[,"n"]
r1 <- fd1\$model[,"r"]; r2 <- fd2\$model[,"r"]
rr <- seq(min(r1)-.2, max(r1)+.2, length=100)
nn1 <- predict(fd1, newdata=list(r=rr), interval="conf")
nn2 <- predict(fd2, newdata=list(r=rr), interval="conf")
s1 <- paste(round(confint(fd1)[2,], digits=3), collapse=", ")
s2 <- paste(round(confint(fd2)[2,], digits=3), collapse=", ")
x <- y <- seq(lx)
par(mfrow=c(2,2), mar=c(3,3,3,1), mgp=c(2,1,0))
image(x, y, ac1, cex.main=1,
main=paste("Anisotropic set cover and a 2D cluster of\n",
"sites with (1,0)-neighborhood and p=",
round(p1, digits=3), sep=""))
rect(bnd["x1",], bnd["y1",], bnd["x2",], bnd["y2",])
abline(v=ss, lty=2)
image(x, y, ac2, cex.main=1,
main=paste("Anisotropic set cover and a 2D cluster of\n",
"sites with (1,0)-neighborhood and p=",
round(p2, digits=3), sep=""))
rect(bnd["x1",], bnd["y1",], bnd["x2",], bnd["y2",])
abline(v=ss, lty=2)
plot(r1, n1, pch=3, ylim=range(c(n1,n2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s1,")", sep=""))
matlines(rr, nn1, lty=c(1,2,2), col=c("black","red","red"))
plot(r2, n2, pch=3, ylim=range(c(n1,n2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s2,")", sep=""))
matlines(rr, nn2, lty=c(1,2,2), col=c("black","red","red"))

## End(Not run)
```

SECP documentation built on May 11, 2022, 9:05 a.m.