improvedLHS(lhs)
improvedLHS()所属R语言包：lhs
Improved Latin Hypercube Sample
改进的拉丁超立方体样品
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Draws a Latin Hypercube Sample from a set of uniform distributions for use in creating a Latin Hypercube Design. This function attempts to optimize the sample with respect to an optimum euclidean distance between design points.
绘制一个拉丁超立方体样品，用于创建一个拉丁超立方设计的一组均匀分布。这个函数尝试优化样品相对于最佳的设计点之间的欧氏距离。
用法----------Usage----------
improvedLHS(n, k, dup=1)
参数----------Arguments----------
参数：n
The number of partitions (simulations or design points)
分区的数量（模拟或设计要点）
参数：k
The number of replications (variables)
复制数（变量）
参数：dup
A factor that determines the number of candidate points used in the search. A multiple of the number of remaining points than can be added.
在搜索中使用的候选点的数量的一个因素，它决定。剩余点比的倍数的数量可以增加。
Details
详细信息----------Details----------
Latin hypercube sampling (LHS) was developed to generate a distribution of collections of parameter values from a multidimensional distribution. A square grid containing possible sample points is a Latin square iff there is only one sample in each row and each column. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions. When sampling a function of k variables, the range of each variable is divided into n equally probable intervals. n sample points are then drawn such that a Latin Hypercube is created. Latin Hypercube sampling generates more efficient estimates of desired parameters than simple Monte Carlo sampling.
拉丁超立方体抽样（LHS）的开发是为了产生一个分布的多维分布的参数值的集合。包含可能的样本点的方形网格是拉丁方，当且仅当有只有一个样品在每一行和每一列。一个拉丁超立方体是这一概念的推广到任意维数。当采样k变量的函数，每个变量的范围分为n同样可能的时间间隔。 n的采样点，然后绘制，拉丁超立方。拉丁超立方抽样所需的参数不是简单的蒙特卡罗抽样产生更有效的估计。
This program generates a Latin Hypercube Sample by creating random permutations of the first n integers in each of k columns and then transforming those integers into n sections of a standard uniform distribution. Random values are then sampled from within each of the n sections. Once the sample is generated, the uniform sample from a column can be transformed to any distribution by using the quantile functions, e.g. qnorm(). Different columns can have different distributions.
该程序生成一个随机排列在每个n列的第一个k整数，然后将这些整数n段的一个标准均匀分布的拉丁超立方采样。随机值，然后采样从各n部分。一旦样品被生成时，从列的均匀样品可被转化到任何分布分量的功能，例如，通过使用qnorm（）。不同的列可以有不同的分布。
This function attempts to optimize the sample with respect to an optimum euclidean distance between design points.
这个函数尝试优化样品相对于最佳的设计点之间的欧氏距离。
值----------Value----------
An n by k Latin Hypercube Sample matrix with values uniformly distributed on [0,1]
nk拉丁超立方体样品基质的值均匀分布在[0,1]
（作者）----------Author(s)----------
Rob Carnell
参考文献----------References----------
Improved Distributed Hypercube Sampling American Institute of Aeronautics and Astronautics Paper 1274.
John Burkardt and modified 16 Feb 2005 http://www.csit.fsu.edu/~burkardt/m_src/ihs/ihs.m
参见----------See Also----------
randomLHS, geneticLHS, maximinLHS, and optimumLHS to generate Latin Hypercube Samples. optAugmentLHS, optSeededLHS, and augmentLHS to modify and augment existing designs.
randomLHS，geneticLHS，maximinLHS和optimumLHS拉丁超立方体样品。 optAugmentLHS，optSeededLHS和augmentLHS修改和增加现有的设计。
实例----------Examples----------
improvedLHS(4, 3, 2)
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 2012-9-21 19:24:49
