exactMPLEstrauss(spatstat)
exactMPLEstrauss()所属R语言包：spatstat
Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process
固定施特劳斯过程的精确的最大Pseudolikelihood估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes, to very high accuracy, the Maximum Pseudolikelihood Estimates of the parameters of a stationary Strauss point process.
计算，以非常高的精度，最大Pseudolikelihood估计施特劳斯的固定点过程的参数。
用法----------Usage----------
exactMPLEstrauss(X, R, ngrid = 2048, plotit = FALSE)
参数----------Arguments----------
参数：X
Data to which the Strauss process will be fitted. A point pattern dataset (object of class "ppp").
数据，施特劳斯的过程中会被安装。点模式集（类的对象"ppp"）。
参数：R
Interaction radius of the Strauss process. A non-negative number.
互动半径的斯特劳斯过程。甲非负数。
参数：ngrid
Grid size for calculation of integrals. An integer, giving the number of grid points in the x and y directions.
积分计算的网格大小。一个整数，x和y方向网格点的数量。
参数：plotit
Logical. If TRUE, the log pseudolikelihood is plotted on the current device.
逻辑。如果TRUE，绘制的logpseudolikelihood当前设备上。
Details
详细信息----------Details----------
This function is intended mainly for technical investigation of algorithm performance. Its practical use is quite limited.
此功能的目的主要是为技术调查算法的性能。它的实际应用是非常有限的。
It fits the stationary Strauss point process model to the point pattern dataset X by maximum pseudolikelihood (with the border edge correction) using an algorithm with very high accuracy. This algorithm is more accurate than the default behaviour of the model-fitting function ppm because the discretisation is much finer.
适合固定施特劳斯的点过程模型的点模式的数据集X最大pseudolikelihood的（与边框边缘修正）使用的算法具有很高的准确性。该算法更准确的默认行为模型的拟合函数ppm因为离散化是更精细。
Ripley (1988) and Baddeley and Turner (2000) derived the log pseudolikelihood for the stationary Strauss process, and eliminated the parameter beta, obtaining an exact formula for the partial log pseudolikelihood as a function of the interaction parameter gamma only. The algorithm evaluates this expression to a high degree of accuracy, using numerical integration on a ngrid * ngrid lattice, uses optim to maximise the log pseudolikelihood with respect to gamma, and finally recovers beta.
雷普利（1988）和巴德利和Turner（2000）推导出的logpseudolikelihood固定施特劳斯过程，，和消除参数beta，获得一个精确的公式的相互作用参数作为一个功能的部分的logpseudolikelihood<X >只。该算法的计算结果表达一个高准确度，利用数值积分在gamma格，使用ngrid * ngrid最大限度地的logpseudolikelihood与optim，终于恢复<X >。
The result is a vector of length 2, containing the fitted coefficients log(beta) and log(gamma). These values correspond to the entries that would be obtained with coef(ppm(X, ~1, Strauss(R))).
的结果是一个向量的长度为2，含有的拟合系数log(beta)和log(gamma)。这些值对应的条目，将得到与coef(ppm(X, ~1, Strauss(R)))。
The fitted coefficients are typically accurate to within 10^(-6) as shown in Baddeley and Turner (2013).
的拟合系数通常精确到10^(-6)所示Baddeley和特纳（2013年）。
值----------Value----------
Vector of length 2.
向量的长度为2。
（作者）----------Author(s)----------
Adrian Baddeley
<a href="mailto:Adrian.Baddeley@csiro.au">Adrian.Baddeley@csiro.au</a>
<a href="http://www.maths.uwa.edu.au/~adrian/">http://www.maths.uwa.edu.au/~adrian/</a>
and Rolf Turner
<a href="mailto:r.turner@auckland.ac.nz">r.turner@auckland.ac.nz</a>
参考文献----------References----------
Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics 42, 283–322.
Manuscript in preparation.
Statistical inference for spatial processes. Cambridge University Press.
参见----------See Also----------
ppm
ppm
实例----------Examples----------
exactMPLEstrauss(cells, 0.1)
coef(ppm(cells, ~1, Strauss(0.1)))
coef(ppm(cells, ~1, Strauss(0.1), nd=128))
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。
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发表于 2012-9-30 13:26:55
