krige.uk(SpatialTools)
krige.uk()所属R语言包：SpatialTools
Performs universal kriging
执行泛克里格
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs Universal Kriging using X, the n \times k design matrix for the regression coefficients of the observed data, y, the n \times 1 matrix of observed responses, V, the (positive definite) covariance matrix of the observed responses, Xp, the np \times k design matrix of the responses to be predicted, Vp, the np \times np covariance matrix of the responses to be predicted, and Vop, the n \times np matrix of covariances between the observed responses and the responses to be predicted. Uses Armadillo C++ template via RcppArmadillo to perform most of the operations.
执行泛克里格使用X，n \times k设计矩阵的回归系数的观测数据，y，n \times 1观察到的反应矩阵，V， （正定）协方差矩阵所观察到的反应，Xp，np \times k设计矩阵的反应进行预测，Vp，np \times np协方差矩阵的反应预测，Vop，n \times np观察到的反应和响应之间的协方差矩阵进行预测。使用的犰狳C + +模板通过RcppArmadillo执行的操作。
用法----------Usage----------
krige.uk(y, V, Vp, Vop, X, Xp, nsim = 0, Ve.diag = NULL, method = "eigen")
参数----------Arguments----------
参数：y
The vector of observed responses. Should be a matrix of size n \times 1 or a vector of length n.
观察到的反应的向量。应该是的矩阵大小n \times 1或向量的长度n。
参数：V
The covariance matrix of the observed responses. The size is n \times n.
所观察到的响应的协方差矩阵。的大小n \times n。
参数：Vp
The covariance matrix of the responses to be predicted. The size is np \times np
要被预测的协方差矩阵的答复。的大小是np \times np
参数：Vop
The cross-covariance between the observed responses and the responses to be predicted. The size is n \times np
所观察到的反应和要被预测的响应之间的互协方差。的大小是n \times np
参数：X
The design matrix of the observed data. The size is n \times k
的设计，所观察到的数据的矩阵。的大小是n \times k
参数：Xp
The design matrix of the responses to be predicted. The size is np \times k </table>
要被预测的响应的设计矩阵。的大小是np \times k</ TABLE>
参数：nsim
A non-negative value indicating the number of conditional simulations that should be returned. If this is less than 1, then no conditional simulation is done.
一个非负的值，该值指示数应归还的条件模拟。如果这是小于1，则没有条件进行仿真。
参数：Ve.diag
A vector of length n specifying the measurement error variances of the observed data.
长度为n的所观察到的数据指定的测量误差方差的向量。
参数：method
A character vector specifying the method used to decompose V. Options are "eigen", "chol", or "svd" (Eigen decomposition, Cholesky decomposition, or Singular value decomposition, respectively).
指定一个字符向量分解V所采用的方法。选项是“特征”，“哲”，或“SVD”（本征分解，Cholesky分解，或奇异值分解，分别）。
Details
详细信息----------Details----------
It is assumed that there are n observed data values and that we wish to make predictions at np locations. We assume that there are k regression coefficients (including the intercept). Both X and Xp should contain a column of 1's if an intercept is desired.
假设有n观察到的数据值，和np位置，我们希望做出预测。我们假定有k回归系数（包括截距）。这两个X和Xp应该包含一列1，如果需要的截距。
If doing conditional simulation, the Cholesky decomposition should not work when there are coincident locations between the observed data locations and the predicted data locations. Both the Eigen and Singular Value Decompositions should work.
如果这样做条件模拟，Cholesky分解时所观察到的数据位置和预测的数据之间有重合的位置，不应该工作。特征向量和奇异值分解的工作。
值----------Value----------
The function a list containing the following objects:
功能的列表，包含以下对象：
参数：pred
A vector of length np containing the predicted responses.
一个向量的长度np包含预测的反应。
参数：mspe
A vector of length np containing the mean-square prediction error of the predicted responses.
长度np含有的均方预测误差的预测的响应的向量。
参数：coeff
A vector of length k containing the estimated regression coefficients.
k的估计回归系数的矢量长度。
参数：vcov.coeff
A k \times k matrix containing the (estimated) covariance matrix of estimated the regression coefficients.
Ak \times k矩阵（估计）协方差矩阵的估计回归系数。
参数：simulations
An n \times nsim matrix containing the nsim realizations of the conditional realizations. Each column of the matrix represents a realization of the conditional normal distribution.
n \times nsim基质中含有nsim实现的条件实现。该矩阵的每一列表示实现有条件的正态分布。
（作者）----------Author(s)----------
Joshua French
参考文献----------References----------
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。
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发表于 2012-9-30 12:53:02
