condrmaxlin(SpatialExtremes)
condrmaxlin()所属R语言包:SpatialExtremes
Conditional simulation of max-linear random fields
条件模拟的最大线性随机领域
译者:生物统计家园网 机器人LoveR
描述----------Description----------
This function generates (approximate) conditional simulation of unit Frechet max-linear random fields. It can be used to get approximate conditional simulation for max-stable processes.
(大约)这个函数生成条件模拟单元导数最大线性随机领域。它可以被用来获得近似条件模拟的最大稳定的过程。
用法----------Usage----------
condrmaxlin(n, coord, data.coord, data, cov.mod = "gauss", ..., grid =
FALSE, p = 10000)
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 locations.
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,每一行指定一个地点。
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 max-stable model. See section Details.
一个字符串指定的最大稳定的模型。请参见详细信息。
参数:...
The parameters of the max-stable model. See section Details.
的最大稳定模型的参数。请参见详细信息。
参数:grid
Logical. Does coord defines a grid?
逻辑。 coord定义了一个网格?
参数:p
An integer. The number of unit Frechet random variables used in the max-linear approximation.
一个整数。单位导数随机变量的最大线性近似。
Details
详细信息----------Details----------
Any unit Frechet max-stable processes Z(x) can be approximated by a unit Frechet max-linear process, i.e.,
任何单位和导数最大稳定的过程Z(x)可以近似为一个单元导数最大线性的过程,即,
where f_j are non-negative deterministic functions, p is a sufficiently large integer and Z_j are independent unit Frechet random variables. Note that to ensure unit Frechet margins, the following condition has to be satisfied
f_j都是非负的确定性函数,p是一个足够大的整数,Z_j是独立的单元导数的随机变量。需要注意的是保证机组导数的利润,必须满足以下条件
for all x.
所有x。
Currently only the discretized Smith model is implemented for which f_j(x) = c(p) φ(x - u_j ; Σ) where φ( . ; Σ) is the zero mean (multivariate) normal density with covariance matrix Σ, u_j is a sequence of deterministic points appropriately chosen and c(p) is a constant ensuring unit Frechet margins.
目前,只有离散Smith模型实施的f_j(x) = c(p) φ(x - u_j ; Σ),其中φ( . ; Σ)是零均值(多元)正常密度的协方差矩阵Σ,u_j是一个序列的确定性点适当地选择和c(p)是一个常数确保单位的Frechet空间。
值----------Value----------
A matrix containing observations from the required max-stable model. Each column represents one stations. If grid = TRUE, the function returns an array of dimension nrow(coord) x nrow(coord) x n.
矩阵的观测所需的最高稳定的模型。每一列代表一个站。如果grid = TRUE,该函数返回一个数组的维NROW(经纬度)所述NROW(经纬度)X N。
警告----------Warnings----------
It may happen that some conditional observations are not honored because the approximation of a max-stable process by a max-linear one isn't accurate enough! Sometimes taking a larger p solves the issue.
它可能发生,一些有条件的意见不被遵守,因为近似的一个最大稳定过程的一个最大线性不够准确!有时候,一个更大的p解决了这个问题。
(作者)----------Author(s)----------
Mathieu Ribatet
参考文献----------References----------
Random Fields. Advances in Applied Probability.
参见----------See Also----------
rmaxstab, condrmaxlin
rmaxstab,condrmaxlin
实例----------Examples----------
## One dimensional conditional simulations[一维条件模拟]
n.cond.site <- 10
cond.coord <- runif(n.cond.site, -10, 10)
data <- rmaxlin(1, cond.coord, var = 3, p = 10000)
x <- seq(-10, 10, length = 250)
cond.sim <- condrmaxlin(5, x, cond.coord, data, var = 3)
matplot(x, t(log(cond.sim)), type = "l", lty = 1, pch = 1)
points(cond.coord, log(data))
## Two dimensional conditional simulation[二维条件模拟]
cond.coord <- matrix(runif(2 * n.cond.site, -10, 10), ncol = 2)
data <- rmaxstab(1, cond.coord, "gauss", cov11 = 4, cov12 = 0, cov22 = 4)
x <- y <- seq(-10, 10, length = 75)
cond.sim <- condrmaxlin(4, cbind(x, y), cond.coord, data, cov11 = 4,
cov12 = 0, cov22 = 4, grid = TRUE, p = 2000)
## Note p is set to 2000 for CPU reasons but is likely to be too small[附注p设置为2000 CPU的原因,但很可能是太小]
op <- par(mfrow = c(2, 2), mar = rep(1, 4))
for (i in 1:4){
image(x, y, log(cond.sim[,,i]), col = heat.colors(64), xaxt = "n", yaxt
= "n", bty = "n")
contour(x, y, log(cond.sim[,,i]), add = TRUE)
text(cond.coord[,1], cond.coord[,2], round(log(data), 2), col = 3)
}
par(op)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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