mincontrast(spatstat)
mincontrast()所属R语言包：spatstat
Method of Minimum Contrast
最低对比度的方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A general low-level algorithm for fitting theoretical point process models to point pattern data by the Method of Minimum Contrast.
一般的低级别的算法配件点过程理论模型，以点图形数据的最小对比度的方法。
用法----------Usage----------
mincontrast(observed, theoretical, startpar, ...,
ctrl=list(q = 1/4, p = 2, rmin=NULL, rmax=NULL),
fvlab=list(label=NULL, desc="minimum contrast fit"),
explain=list(dataname=NULL, modelname=NULL, fname=NULL))
参数----------Arguments----------
参数：observed
Summary statistic, computed for the data. An object of class "fv".
参数：theoretical
An R language function that calculates the theoretical expected value of the summary statistic, given the model parameters. See Details.
一个R语言函数，其计算的摘要统计量的理论预期值，给定的模型参数。查看详细信息。
参数：startpar
Vector of initial values of the parameters of the point process model (passed to theoretical).
向量的点过程模型的参数的初始值（传递到theoretical）。
参数：...
Additional arguments passed to the function theoretical and to the optimisation algorithm optim.
额外的参数传递给函数theoretical和优化算法optim的。
参数：ctrl
Optional. List of arguments controlling the optimisation. See Details.
可选。控制优化的参数。查看详细信息。
参数：fvlab
Optional. List containing some labels for the return value. See Details.
可选。列表，其中包含一些标签的返回值。查看详细信息。
参数：explain
Optional. List containing strings that give a human-readable description of the model, the data and the summary statistic.
可选。列表，其中包含的字符串给人类可读的描述模型，数据的汇总统计。
Details
详细信息----------Details----------
This function is a general algorithm for fitting point process models by the Method of Minimum Contrast. If you want to fit the Thomas process, see thomas.estK. If you want to fit a log-Gaussian Cox process, see lgcp.estK. If you want to fit the Matern cluster process, see matclust.estK.
这个函数是一个通用的算法拟合点过程模型的最小对比度的方法。如果你想，以适应托马斯过程，请参阅thomas.estK。如果你想以适应数高斯Cox过程，请参阅lgcp.estK。如果你想适合的Matern的聚类过程，请参阅matclust.estK。
The Method of Minimum Contrast (Diggle and Gratton, 1984) is a general technique for fitting a point process model to point pattern data. First a summary function (typically the K function) is computed from the data point pattern. Second, the theoretical expected value of this summary statistic under the point process model is derived (if possible, as an algebraic expression involving the parameters of the model) or estimated from simulations of the model. Then the model is fitted by finding the optimal parameter values for the model to give the closest match between the theoretical and empirical curves.
最小对比度（Diggle和格拉顿，1984年）的方法点模式数据拟合点过程模型是一个通用的技术。首先汇总函数（通常K功能），计算从数据上看模式。二，本摘要统计理论预期值点过程模型推导出（如果可能的话，涉及的模型参数的代数表达式）或模拟模型的估计。然后将模型嵌合通过寻找模型的最佳参数值之间的理论和实证曲线，得到最接近的匹配。
The argument observed should be an object of class "fv" (see fv.object) containing the values of a summary statistic computed from the data point pattern. Usually this is the function K(r) computed by Kest or one of its relatives.
参数observed应该是一个类的对象"fv"（见fv.object），其中包含从数据上看模式的汇总统计计算的值。这是通常的功能K(r)计算Kest或它的一个亲戚。
The argument theoretical should be a user-supplied function that computes the theoretical expected value of the summary statistic. It must have an argument named par that will be the vector of parameter values for the model (the length and format of this vector are determined by the starting values in startpar). The function theoretical should also expect a second argument (the first argument other than par) containing values of the distance r for which the theoretical value of the summary statistic K(r) should be computed. The value returned by theoretical should be a vector of the same length as the given vector of r values.
参数theoretical应该是一个用户提供的函数，计算理论的预期中值的汇总统计。它必须有一个参数叫做par，这将是向量的模型的参数值（这个向量的长度和格式确定由初始值startpar）。函数theoretical也应该想到的第二个参数（第一个参数其他比par）的距离r理论值的汇总统计K(r)应该包含被计算出来。所返回的值theoreticalr值作为给定的矢量具有相同的长度应该是一个矢量。
The argument ctrl determines the contrast criterion (the objective function that will be minimised). The algorithm minimises the criterion
的参数ctrl决定了对比准则（目标函数，将最小化）。该算法最小化的标准
where theta is the vector of parameters of the model, Fhat(r) is the observed value of the summary statistic computed from the data, F(theta,r) is the theoretical expected value of the summary statistic, and p,q are two exponents. The default is q = 1/4, p=2 so that the contrast criterion is the integrated squared difference between the fourth roots of the two functions (Waagepetersen, 2006).
其中theta的向量参数的模型，Fhat(r)是观测值的汇总统计的数据计算出来的，F(theta,r)的理论预期值的汇总统计，并p,q是两个指数。默认值是q = 1/4，p=2这样的对比标准是综合性的两个函数之间的第四根（Waagepetersen，2006年）的平方差。
The other arguments just make things print nicely. The argument fvlab contains labels for the component fit of the return value. The argument explain contains human-readable strings describing the data, the model and the summary statistic.
其他参数，就很好的东西打印。参数fvlab包含的组件fit的返回值的标签。参数explain包含人类可读的字符串描述数据，模型和汇总统计。
The "..." argument of mincontrast can be used to pass extra arguments to the function theoretical and/or to the optimisation function optim. In this case, the function theoretical should also have a "..." argument and should ignore it (so that it ignores arguments intended for optim).
"..."mincontrast参数可用于传递额外的参数的功能theoretical和/或优化功能optim。在这种情况下，该功能theoretical也应该有一个"..."参数，应该忽略它（所以它忽略了意为optim的论点）。
值----------Value----------
An object of class "minconfit". There are methods for printing and plotting this object. It contains the following components:
对象的类"minconfit"。有这个对象的打印和绘图的方法。它包含以下组件：
参数：par
Vector of fitted parameter values.
拟合参数值的向量。
参数：fit
Function value table (object of class "fv") containing the observed values of the summary statistic (observed) and the theoretical values of the summary statistic computed from the fitted model parameters.
函数值表（对象类"fv"）的观测值的汇总统计（observed）与理论值拟合模型参数的汇总统计计算。
参数：opt
The return value from the optimizer optim.
参数：crtl
The control parameters of the algorithm.
的控制参数的算法。
参数：info
List of explanatory strings.
说明字符串列表。
（作者）----------Author(s)----------
Rasmus Waagepetersen
<a href="mailto:rw@math.auc.dk">rw@math.auc.dk</a>,
adapted for <span class="pkg">spatstat</span> by
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>
参考文献----------References----------
Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society, series B 46, 193 – 212.
Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
An estimation function approach to inference for inhomogeneous Neyman-Scott processes. Submitted.
参见----------See Also----------
kppm, lgcp.estK, matclust.estK, thomas.estK,
kppm，lgcp.estK，matclust.estK，thomas.estK，
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 13:48:19
