condrgp(SpatialExtremes)
condrgp()所属R语言包：SpatialExtremes
Conditional simulation of Gaussian random fields
条件模拟高斯随机领域
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function generates conditional simulation of Gaussian random fields from the simple kriging predictor.
这个函数生成条件模拟高斯随机领域，从简单克里格法预测。
用法----------Usage----------
condrgp(n, coord, data.coord, data, cov.mod = "powexp", mean = 0, sill =
1, range = 1, smooth = 1, grid = FALSE, control = list())
参数----------Arguments----------
参数：n
Integer. The number of conditional simulations.
整数。有条件的模拟的数目。
参数：coord
A numeric vector or matrix specifying the coordinates where the process has to be generated. If coord is a matrix, each row specifies one location.
数值向量或矩阵的指定的位置的坐标的过程以生成。如果coord是一个矩阵，每一行指定一个位置。
参数：data.coord
A numeric vector or matrix specifying the coordinates where the process is conditioned.
甲数值向量或矩阵的指定的条件的过程的坐标。
参数：data
A numeric vector giving the conditioning observations.
一个数值向量给的空调的观察。
参数：cov.mod
A character string specifying the covariance function family. Must be one of "whitmat", "powexp", "cauchy" or "bessel" for the Whittle-Mater, the powered exponential, the Cauchy or Bessel covariance families.
一个字符串指定的协方差函数族。必须有一个“whitmat”中，“powexp”，“柯西”或“贝塞尔”惠特尔母校，动力指数，柯西或贝塞尔协方差家庭的。
参数：mean,sill,range,smooth
The mean, sill, range and smooth of the Gaussian process.
平均，窗台，范围和平稳高斯过程。
参数：grid
Logical. Does coord specifies a grid?
逻辑。 coord指定一个网格？
参数：control
A named list passing options to the simulation method of Gaussian processes — see rgp.
命名的参数传递到高斯过程的仿真方法 - rgp。
值----------Value----------
A list with components:
组件列表：
参数：coord
The coordinates at which the process was simulated;
该过程进行模拟的坐标;
参数：cond.sim
The simulated process;
模拟的过程;
参数：data.coord
The coordinates of the conditioning locations;
调理的位置的坐标;
参数：data
The conditioning observations;
空调观测;
参数：cov.mod
The covariance function family;
协方差函数族;
参数：grid
Does coord specifies a grid?
coord指定一个网格？
（作者）----------Author(s)----------
Mathieu Ribatet
参见----------See Also----------
kriging, rgp.
kriging，rgp。
实例----------Examples----------
## Several conditional simulations[＃几个条件模拟]
n.site <- 50
n.sim <- 512
x.obs <- runif(n.site, -100, 100)
x.sim <- seq(-100, 100, length = n.sim)
data <- rgp(1, x.obs, "whitmat", sill = 1, range = 10, smooth = 0.75)
sim <- condrgp(5, x.sim, x.obs, data, "whitmat", sill = 1, range =
10, smooth = 0.75)
matplot(x.sim, t(sim$cond.sim), type = "l", lty = 1, xlab = "x", ylab =
expression(Y[cond](x)))
points(x.obs, data, pch = 21, bg = 1)
title("Five conditional simulations")
## Comparison between one conditional simulations and the kriging[＃一个条件模拟和克立格法的比较]
## predictor on a grid[＃在网格上预测]
x.obs <- matrix(runif(2 * n.site, -100, 100), ncol = 2)
x <- y <- seq(-100, 100, length = 100)
x.sim <- cbind(x, y)
data <- rgp(1, x.obs, "whitmat", sill = 1, range = 50, smooth = 0.75)
krig <- kriging(data, x.obs, x.sim, "whitmat", sill = 1, range = 50,
smooth = 0.75, grid = TRUE)
sim <- condrgp(1, x.sim, x.obs, data, "whitmat", sill = 1, range = 50,
smooth = 0.75, grid = TRUE)
z.lim <- range(c(sim$cond.sim, data, krig$krig.est))
breaks <- seq(z.lim[1], z.lim[2], length = 65)
col <- heat.colors(64)
idx <- as.numeric(cut(data, breaks))
op <- par(mfrow = c(1,2))
image(x, y, krig$krig.est, col = col, breaks = breaks)
points(x.obs, bg = col[idx], pch = 21)
title("Kriging predictor")
image(x, y, sim$cond.sim, col = col, breaks = breaks)
points(x.obs, bg = col[idx], pch = 21)
title("Conditional simulation")
## Note how the background colors of the above points matches the ones[＃注意上述各点的背景色匹配的]
## returned by the image function[＃传回的图像功能]
par(op)
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 2012-9-30 12:40:47
