fdc3s(SECP)
fdc3s()所属R语言包：SECP
Mass fractal dimension of a 3D cluster
质量分形维数的3D聚类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
fdc3s() function uses a linear regression model for statistical estimation of the mass fractal dimension of a cluster on 3D square lattice with iso- & isotropic sets cover.
fdc3s()函数使用了线性回归模型的3D正方形格子上的丛集异的各向同性套盖的质量分形维数的统计估计。
用法----------Usage----------
fdc3s(acc=ssi30(x=95), bnd=isc3s(k=12, x=dim(acc)))
参数----------Arguments----------
参数：acc
an accessibility matrix for 3D square percolation lattice.
3D方渗透格的可达性矩阵。
参数：bnd
bounds for the iso- or anisotropic set cover.
异或各向异性集合覆盖的界限。
Details
详细信息----------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.
的质量的分形维数为聚类是等于log(n)和log(r)，其中n是一个绝对频率是有界的异元素的总簇网站之间的线性回归系数和各向异性套覆盖。
The isotropic set cover on 3D square lattice is formed from scalable cubes with variable sizes 2r+1 and a fixed point in the lattice center.
3D正方形格子的各向同性盖是由可扩展的多维数据集的可变大小2r+1和一个固定点的晶格中心。
The anisotropic set cover on 3D square lattice is formed from scalable cuboids with variable sizes r+1 and a fixed face along the lattice boundary.
3D正方形格子是由可伸缩的长方体，尺寸可变的各向异性集合覆盖r+1和一个固定面的晶格边界。
The percolation is simulated on 3D square lattice with uniformly weighted sites and the constant parameter p.
的渗透是模拟3D正方形格子均匀加权的网站和常量参数p。
The isotropic cluster is formed from the accessible sites connected with initial sites subset.
的各向同性聚类形成从与初始网站子集连接的可访问的站点。
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.
如果acc[e]<p然后e是可访问的网站;如果acc[e]==1然后e是不可访问的网站，如果acc[e]==2然后e属于一个网站聚类。
值----------Value----------
A linear regression model for statistical estimation of the mass fractal dimension of a cluster on 3D square lattice with iso- & anisotropic sets cover.
线性回归模型的聚类3D正方晶格与异各向异性套盖的质量分形维数的统计估计。
（作者）----------Author(s)----------
Pavel V. Moskalev
参见----------See Also----------
fdc2s, fds2s, fds3s
fdc2s，fds2s，fds3s
实例----------Examples----------
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
# Example 1: Isotropic set cover[例1：各向同性集合覆盖]
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
pc <- .311608
p1 <- pc - .02
p2 <- pc + .02
lx <- 33; ss <- (lx+1)/2
set.seed(20120627); ac1 <- ssi30(x=lx, p=p1)
set.seed(20120627); ac2 <- ssi30(x=lx, p=p2)
bnd <- isc3s(k=9, x=dim(ac1))
fd1 <- fdc3s(acc=ac1, bnd=bnd)
fd2 <- fdc3s(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 <- z <- seq(lx)
y1 <- ac1[,ss,]; y2 <- ac2[,ss,]
par(mfrow=c(2,2), mar=c(3,3,3,1), mgp=c(2,1,0))
image(x, z, y1, cex.main=1,
main=paste("Isotropic set cover and\n",
"a 3D cluster in the y=",ss," slice with\n",
"(1,0)-neighborhood and p=",
round(p1, digits=3), sep=""))
rect(bnd["x1",], bnd["z1",], bnd["x2",], bnd["z2",])
abline(h=ss, lty=2); abline(v=ss, lty=2)
image(x, z, y2, cex.main=1,
main=paste("Isotropic set cover and\n",
"a 3D cluster in the y=",ss," slice with\n",
"(1,0)-neighborhood and p=",
round(p2, digits=3), sep=""))
rect(bnd["x1",], bnd["z1",], bnd["x2",], bnd["z2",])
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"))
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
# Example 1: Anisotropic set cover, dir=3[例1：各向异性集合覆盖，DIR = 3]
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
pc <- .311608
p1 <- pc - .02
p2 <- pc + .02
lx <- 33; ss <- (lx+1)/2
ssz <- seq(lx^2+lx+2, 2*lx^2-lx-1)
set.seed(20120627); ac1 <- ssi30(x=lx, p=p1, set=ssz, all=FALSE)
set.seed(20120627); ac2 <- ssi30(x=lx, p=p2, set=ssz, all=FALSE)
bnd <- asc3s(k=9, x=dim(ac1), dir=3)
fd1 <- fdc3s(acc=ac1, bnd=bnd)
fd2 <- fdc3s(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 <- z <- seq(lx)
y1 <- ac1[,ss,]; y2 <- ac2[,ss,]
par(mfrow=c(2,2), mar=c(3,3,3,1), mgp=c(2,1,0))
image(x, z, y1, cex.main=1,
main=paste("Anisotropic set cover and\n",
"a 3D cluster in the y=",ss," slice with\n",
"(1,0)-neighborhood and p=",
round(p1, digits=3), sep=""))
rect(bnd["x1",], bnd["z1",], bnd["x2",], bnd["z2",])
abline(v=ss, lty=2)
image(x, z, y2, cex.main=1,
main=paste("Anisotropic set cover and\n",
"a 3D cluster in the y=",ss," slice with\n",
"(1,0)-neighborhood and p=",
round(p2, digits=3), sep=""))
rect(bnd["x1",], bnd["z1",], bnd["x2",], bnd["z2",])
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"))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-29 23:53:27
