clf.test(spatstat)
clf.test()所属R语言包：spatstat
Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests
经验Cressie Loosmore福特和最大绝对偏差测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Perform the Cressie (1991)/Loosmore and Ford (2006) test or the Maximum Absolute Deviation test for a spatial point pattern.
执行经验Cressie（1991）/ Loosmore和福特（2006）试验或测试的空间点格局的最大绝对偏差。
用法----------Usage----------
clf.test(X, ..., rinterval = NULL, use.theo=FALSE)
mad.test(X, ..., rinterval = NULL, use.theo=FALSE)
参数----------Arguments----------
参数：X
Either a point pattern (object of class "ppp", "lpp" or other class), a fitted point process model (object of class "ppm", "kppm" or other class) or an envelope object (class "envelope").
无论是点模式（对象类"ppp"，"lpp"或其他类），一个厨房点过程模型（对象类"ppm"，"kppm"或其他类）或信封对象（类"envelope"）。
参数：...
Arguments passed to envelope. Useful arguments include fun to determine the summary function, nsim to specify the number of Monte Carlo simulations, and verbose=FALSE to turn off the messages.
传递参数到envelope。有用的参数，包括fun，以确定汇总函数，nsim指定的Monte Carlo模拟，verbose=FALSE关闭的消息。“
参数：rinterval
Interval of values of the summary function argument r over which the maximum absolute deviation, or the integral, will be computed for the test. A numeric vector of length 2.
摘要函数的参数r超过最大绝对偏差或积分，将被计算为测试值的时间间隔。一个数字矢量长度为2。
参数：use.theo
Logical value determining whether to compare the summary function for the data to its theoretical value for CSR (use.theo=TRUE) or to the sample mean of simulations from CSR (use.theo=FALSE).
逻辑值，决定是否要比较的数据的汇总函数的理论价值CSR（use.theo=TRUE）或样品的意思从CSR（use.theo=FALSE）的模拟。
Details
详细信息----------Details----------
These functions perform hypothesis tests for goodness-of-fit of a point pattern dataset to a point process model, based on Monte Carlo simulation from the model.
这些函数执行善良的契合点模式数据集中到一个点过程模型，基于蒙特卡罗模拟的模型的假设检验。
clf.test performs the test advocated by Loosmore and Ford (2006) which is also described in Cressie (1991, page 667, equation (8.5.42)).
clf.test主张Loosmore和福特（2006）中也描述了经验Cressie（1991年，页667，等式（42年8月5日））来进行测试。
mad.test performs the "global" or "Maximum Absolute Deviation" test described by Ripley (1977, 1981).
mad.test执行“全球”或“最大绝对偏差的测试里普利（1977年，1981年）。
The type of test depends on the type of argument X.
测试的类型取决于的类型参数X。
If X is some kind of point pattern, then a test of Complete Spatial Randomness (CSR) will be performed. That is, the null hypothesis is that the point pattern is completely random.
如果X是一些种点模式，然后将进行测试的完整的空间随机性（CSR）。也就是说，原假设是点模式是完全随机的。
If X is a fitted point process model, then a test of goodness-of-fit for the fitted model will be performed. The model object contains the data point pattern to which it was originally fitted. The null hypothesis is that the data point pattern is a realisation of the model.
如果X是一个拟合点过程模型，然后将进行测试的善良适合拟合模型。的模型对象中包含的数据点，它最初安装的模式。的零假设是数据点的模式是一个实现了该模型。
If X is an envelope object generated by envelope, then it should have been generated with savefuns=TRUE or savepatterns=TRUE so that it contains simulation results. These simulations will be treated as realisations from the null hypothesis.
如果X是envelope，那么它应该已经生成的savefuns=TRUE或savepatterns=TRUE，以便它包含仿真结果的包络生成的对象。这些模拟将被视为实现从零假设。
In all cases, the algorithm will first call envelope to generate or extract the simulated summary functions. The number of simulations that will be generated or extracted, is determined by the argument nsim, and defaults to 99. The summary function that will be computed is determined by the argument fun (or the first unnamed argument in the list ...) and defaults to Kest (except when X is an envelope object generated with savefuns=TRUE, when these functions will be taken).
在所有的情况下，该算法会先请envelope生成或提取的模拟汇总函数。会产生或提取的模拟，确定的说法nsim，并默认为99。将计算的汇总函数的参数是由fun（或第一个未命名的参数在列表中...）和默认为Kest（除X是一个信封对象生成savefuns=TRUE，当这些功能将拍摄）。
The choice of summary function fun affects the power of the test. It is normally recommended to apply a variance-stabilising transformation (Ripley, 1981). If you are using the K function, the normal practice is to replace this by the L function (Besag, 1977) computed by Lest. If you are using the F or G functions, the recommended practice is to apply Fisher's variance-stabilising transformation asin(sqrt(x)) using the argument transform. See the Examples.
选择的汇总函数fun会影响电源的测试。通常建议应用方差稳定变换（雷普利，1981）。如果您使用的是K功能，通常的做法是更换的L的功能（Besag，1977）计算的Lest。如果您使用的是F或G函数，推荐的做法是运用费舍尔的方差稳定变换asin(sqrt(x))使用参数transform。请参阅范例。
值----------Value----------
An object of class "htest". Printing this object gives a report on the result of the test. The p-value is contained in the component p.value.
对象的类"htest"。打印这个对象给出了测试结果的报告。 p-值包含在组件p.value中。
（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 Andrew Hardegen.
参考文献----------References----------
Discussion of Dr Ripley's paper. Journal of the Royal Statistical Society, Series B, 39, 193–195.
Statistics for spatial data. John Wiley and Sons, 1991.
Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925–1931.
Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B, 39, 172 – 212.
Spatial statistics. John Wiley and Sons.
参见----------See Also----------
envelope
envelope
实例----------Examples----------
clf.test(cells, Lest)
m <- mad.test(cells, Lest, verbose=FALSE, rinterval=c(0, 0.1))
m
# extract the p-value[提取的p-值]
m$p.value
# variance stabilised G function[方差稳定的G功能]
clf.test(cells, Gest, transform=expression(asin(sqrt(.))), verbose=FALSE)
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注：
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发表于 2012-9-30 13:16:34
