fds3s(SECP)
fds3s()所属R语言包：SECP
Mass fractal dimension of sampling 3D clusters
质量抽样3D簇的分形维数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
fds3s() function uses a linear regression model for statistical estimation of the mass fractal dimension of sampling clusters on 3D square lattice with iso- & anisotropic sets cover.
fds3s()函数使用一个线性回归模型采样聚类的3D正方形格子异各向异性套盖的质量分形维数的统计估计。
用法----------Usage----------
fds3s(rfq=fssi30(x=95), bnd=isc3s(k=12, x=dim(rfq)))
参数----------Arguments----------
参数：rfq
relative sampling frequencies for sites of the percolation lattice.
相对于采样频率网站的渗透格。
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 sampling clusters is equal to the coefficient of linear regression between log(w) and log(r), where w is a relative sampling frequency of the total sites which are bounded elements of iso- & anisotropic sets cover.
质量分形维数的采样聚类等于log(w)和log(r)，其中w是一个相对的采样频率是有界的异元素的总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。
Each element of the matrix rfq is equal to the relative frequency with which the 3D square lattice site belongs to a cluster sample.
矩阵的每个元素rfq是相等的相对频率与。的3D正方晶格站点属于聚类样品。
值----------Value----------
A linear regression model for statistical estimation of the mass fractal dimension of sampling clusters on 3D square lattice with iso- & anisotropic sets cover.
采样聚类的3D正方形格子异各向异性套盖的质量分形维数的统计估计线性回归模型。
（作者）----------Author(s)----------
Pavel V. Moskalev
参见----------See Also----------
fds2s, fdc2s, fdc3s
fds2s，fdc2s，fdc3s
实例----------Examples----------
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
# Example 1: Isotropic set cover[例1：各向同性集合覆盖]
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
pc <- .311608
p1 <- pc - .01
p2 <- pc + .01
lx <- 33; ss <- (lx+1)/2
rf1 <- fssi30(n=100, x=lx, p=p1)
rf2 <- fssi30(n=100, x=lx, p=p2)
bnd <- isc3s(k=9, x=dim(rf1))
fd1 <- fds3s(rfq=rf1, bnd=bnd)
fd2 <- fds3s(rfq=rf2, bnd=bnd)
w1 <- fd1$model[,"w"]; w2 <- fd2$model[,"w"]
r1 <- fd1$model[,"r"]; r2 <- fd2$model[,"r"]
rr <- seq(min(r1)-.2, max(r1)+.2, length=100)
ww1 <- predict(fd1, newdata=list(r=rr), interval="conf")
ww2 <- 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 <- rf1[,ss,]; y2 <- rf2[,ss,]
par(mfrow=c(2,2), mar=c(3,3,3,1), mgp=c(2,1,0))
image(x, z, y1, zlim=c(0, 3*mean(y1)), cex.main=1,
main=paste("Isotropic set cover and a 3D clusters\n",
"frequency 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, zlim=c(0, 3*mean(y2)), cex.main=1,
main=paste("Isotropic set cover and a 3D clusters\n",
"frequency 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, w1, pch=3, ylim=range(c(w1,w2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s1,")", sep=""))
matlines(rr, ww1, lty=c(1,2,2), col=c("black","red","red"))
plot(r2, w2, pch=3, ylim=range(c(w1,w2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s2,")", sep=""))
matlines(rr, ww2, lty=c(1,2,2), col=c("black","red","red"))
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
# Example 2: Anisotropic set cover, dir=3[例2：各向异性集合覆盖，DIR = 3]
# # # # # # # # # # # # # # # # #[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
pc <- .311608
p1 <- pc - .01
p2 <- pc + .01
lx <- 33; ss <- (lx+1)/2
ssz <- seq(lx^2+lx+2, 2*lx^2-lx-1)
rf1 <- fssi30(n=100, x=lx, p=p1, set=ssz, all=FALSE)
rf2 <- fssi30(n=100, x=lx, p=p2, set=ssz, all=FALSE)
bnd <- asc3s(k=9, x=dim(rf1), dir=3)
fd1 <- fds3s(rfq=rf1, bnd=bnd)
fd2 <- fds3s(rfq=rf2, bnd=bnd)
w1 <- fd1$model[,"w"]; w2 <- fd2$model[,"w"]
r1 <- fd1$model[,"r"]; r2 <- fd2$model[,"r"]
rr <- seq(min(r1)-.2, max(r1)+.2, length=100)
ww1 <- predict(fd1, newdata=list(r=rr), interval="conf")
ww2 <- 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 <- rf1[,ss,]; y2 <- rf2[,ss,]
par(mfrow=c(2,2), mar=c(3,3,3,1), mgp=c(2,1,0))
image(x, z, y1, zlim=c(0, .3), cex.main=1,
main=paste("Anisotropic set cover and a 3D clusters\n",
"frequency 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, zlim=c(0, .3), cex.main=1,
main=paste("Anisotropic set cover and a 3D clusters\n",
"frequency 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, w1, pch=3, ylim=range(c(w1,w2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s1,")", sep=""))
matlines(rr, ww1, lty=c(1,2,2), col=c("black","red","red"))
plot(r2, w2, pch=3, ylim=range(c(w1,w2)), cex.main=1,
main=paste("0.95 confidence interval for the mass\n",
"fractal dimension is (",s2,")", sep=""))
matlines(rr, ww2, 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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-9-29 23:53:44
