covariance(SpatialExtremes)
covariance()所属R语言包：SpatialExtremes
Defines and computes covariance functions
定义和计算协方差函数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function defines and computes several covariance function either from a fitted “max-stable” model; either by specifying directly the covariance parameters.
这个函数定义和计算几个协方差函数从一个装有“最大稳定”的模式，通过直接指定协方差参数。
用法----------Usage----------
covariance(fitted, nugget, sill, range, smooth, smooth2 = NULL, cov.mod =
"whitmat", plot = TRUE, dist, xlab, ylab, col = 1, ...)
参数----------Arguments----------
参数：fitted
An object of class “maxstab”. Most often this will be the output of the fitmaxstab function. May be missing if sill, range, smooth and cov.mod are given.
的对象类“maxstab”。大多数情况下这将是的输出fitmaxstab功能。如果sill，range，smooth和cov.mod给出可能会丢失。
参数：nugget,sill,range,smooth,smooth2
The nugget, sill, scale and smooth parameters for the covariance function. May be missing if fitted is given.
块，窗台，规模和平稳的协方差函数的参数。如果fitted可能会丢失。
参数：cov.mod
Character string. The name of the covariance model. Must be one of "whitmat", "cauchy", "powexp", "bessel" or "caugen" for the Whittle-Matern, Cauchy, Powered Exponential, Bessel and Generalized Cauchy models. May be missing if fitted is given.
字符的字符串。的协方差模型的名称。必须是一个“whitmat”，“柯西中，”powexp“的惠特尔Matern，柯西，技术指数，贝塞尔和广义柯西模型的”贝塞尔“或”caugen“的。如果fitted可能会丢失。
参数：plot
Logical. If TRUE (default) the covariance function is plotted.
逻辑。如果TRUE（默认）的协方差函数绘制。
参数：dist
A numeric vector corresponding to the distance at which the covariance function should be evaluated. May be missing.
甲数值向量对应的协方差函数应评估时的距离。可能会丢失。
参数：xlab,ylab
The x-axis and y-axis labels. May be missing.
x轴和y轴的标签。可能会丢失。
参数：col
The color to be used for the plot.
要使用的颜色的图。
参数：...
Several option to be passed to the plot function.
要传递给plot函数的几个选项。
Details
详细信息----------Details----------
Currently, four covariance functions are defined: the Whittle-Matern, powered exponential (also known as "stable"), Cauchy and Bessel models. These covariance functions are defined as follows for h > 0
目前，四个协方差函数被定义为：的消减-Matern，动力指数（也被称为“稳定的”），柯西和贝塞尔模型。这些协方差函数定义为如下h > 0的
where σ, λ and κ are the sill, the range and shape parameters, Γ is the gamma function, K_κ and J_κ are both modified Bessel functions of order κ. In addition a nugget effect can be set that is there is a jump at the origin, i.e., γ(o) = ν + σ, where ν is the nugget effect.
其中σ，λ和κ是的窗台，范围和形状参数，Γ是伽玛的功能，K_κ的和J_κ为了κ都修正贝塞尔函数。此外，金块效果可设定是在原产地，即有一个跳跃，γ(o) = ν + σ，其中ν是块金效应。
值----------Value----------
This function returns the covariance function. Eventually, if dist is given, the covariance function is computed for each distance given by dist. If plot = TRUE, the covariance
这个函数返回的协方差函数。最后，dist的，协方差函数，计算出每个距离的dist。如果plot = TRUE，协方差
（作者）----------Author(s)----------
Mathieu Ribatet
实例----------Examples----------
## 1- Calling covariance using fixed covariance parameters[＃1 - 调用使用固定的协方差参数的协方差]
covariance(nugget = 0, sill = 1, range = 1, smooth = 0.5, cov.mod = "whitmat")
covariance(nugget = 0, sill = 0.5, range = 1, smooth = 0.5, cov.mod = "whitmat",
dist = seq(0,5, 0.2), plot = FALSE)
## 2- Calling covariance from a fitted model[＃2 - 调用从拟合模型的协方差]
##Define the coordinate of each location[＃定义的每个位置的坐标]
n.site <- 30
locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
colnames(locations) <- c("lon", "lat")
##Simulate a max-stable process - with unit Frechet margins[＃模拟一个最大稳定的过程 - 与单位的Frechet空间]
data <- rmaxstab(30, locations, cov.mod = "whitmat", nugget = 0, range =
3, smooth = 1)
##Fit a max-stable model[＃适合一个最大稳定的模型]
fitted <- fitmaxstab(data, locations, "whitmat", std.err.type = "none",
nugget = 0)
covariance(fitted, ylim = c(0, 1))
covariance(nugget = 0, sill = 1, range = 3, smooth = 1, cov.mod = "whitmat", add =
TRUE, col = 3)
title("Whittle-Matern covariance function")
legend("topright", c("Theo.", "Fitted"), lty = 1, col = c(3,1), inset =
.05)
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 12:41:05
