quadrat.test(spatstat)
quadrat.test()所属R语言包：spatstat
Chi-Squared Dispersion Test for Spatial Point Pattern Based on
卡方色散测试基于空间点模式
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs a chi-squared test of Complete Spatial Randomness for a given point pattern, based on quadrat counts. Alternatively performs a chi-squared goodness-of-fit test of a fitted inhomogeneous Poisson model.
对于一个给定的点格局，样方数的基础上进行卡方检验的完整的空间随机性。或者执行非齐次泊松模型的拟合卡方拟合优度的拟合优度检验。
用法----------Usage----------
quadrat.test(X, ...)
## S3 method for class 'ppp'
quadrat.test(X, nx=5, ny=nx, ..., xbreaks=NULL, ybreaks=NULL, tess=NULL)
## S3 method for class 'ppm'
quadrat.test(X, nx=5, ny=nx, ..., xbreaks=NULL, ybreaks=NULL, tess=NULL)
## S3 method for class 'quadratcount'
quadrat.test(X, ...)
参数----------Arguments----------
参数：X
A point pattern (object of class "ppp") to be subjected to the goodness-of-fit test. Alternatively a fitted point process model (object of class "ppm") to be tested. Alternatively X can be the result of applying quadratcount to a point pattern.
点模式（类的对象"ppp"）受到的善良的拟合优度检验。另外一个装有点过程模型（类的对象"ppm"）来进行测试。或者X可以应用quadratcount阵列点的结果。
参数：nx,ny
Numbers of quadrats in the x and y directions. Incompatible with xbreaks and ybreaks.
的样方数在x和y的方向。不兼容的xbreaks和ybreaks。
参数：...
Ignored.
忽略。
参数：xbreaks
Optional. Numeric vector giving the x coordinates of the boundaries of the quadrats. Incompatible with nx.
可选。数值向量给x的样方的边界的坐标。不相容的nx。
参数：ybreaks
Optional. Numeric vector giving the y coordinates of the boundaries of the quadrats. Incompatible with ny.
可选。数值向量给y的样方的边界的坐标。不相容的ny。
参数：tess
Tessellation (object of class "tess") determining the quadrats. Incompatible with nx, ny, xbreaks, ybreaks.
Tessellation（曲面细分）（对象类"tess"）确定的样方。不相容的nx, ny, xbreaks, ybreaks。
Details
详细信息----------Details----------
These functions perform chi^2 tests of goodness-of-fit for a point process model, based on quadrat counts.
这些功能执行chi^2测试，适合善良的一个点过程模型，样方数的基础上的。
The function quadrat.test is generic, with methods for point patterns (class "ppp"), split point patterns (class "splitppp"), point process models (class "ppm") and quadrat count tables (class "quadratcount").
函数quadrat.test点模式（类"ppp"），分割点模式（类"splitppp"），点过程模型（类"ppm"）和样方数的方法是通用的，表（类"quadratcount"）。
if X is a point pattern, we test the null hypothesis that the data pattern is a realisation of Complete Spatial Randomness (the uniform Poisson point process). Marks in the point pattern are ignored.
如果X是一个点的模式，我们测试的零假设的数据模式是一个完整空间随机性（Poisson点过程的统一）的实现。在点模式的标志将被忽略。
if X is a split point pattern, then for each of the component point patterns (taken separately) we test the null hypotheses of Complete Spatial Randomness. See quadrat.test.splitppp for documentation.
如果X是一个分割点模式，然后每个组件的点模式（单独服用），我们测试的零假设的完全空间随机性。见quadrat.test.splitppp文件。
If X is a fitted point process model, then it should be a Poisson point process model. The data to which this model was fitted are extracted from the model object, and are treated as the data point pattern for the test. We test the null hypothesis that the data pattern is a realisation of the (inhomogeneous) Poisson point process specified by X.
如果X是一个装有点过程模型，那么它应该是一个泊松点过程模型“。该模型拟合中提取的数据模型对象，被当作数据点的测试模式。我们测试的零假设的数据模式是一个实现的（非均质）泊松点过程中指定的X。
In all cases, the window of observation is divided into tiles, and the number of data points in each tile is counted, as described in quadratcount. The quadrats are rectangular by default, or may be regions of arbitrary shape specified by the argument tess. The expected number of points in each quadrat is also calculated, as determined by CSR (in the first case) or by the fitted model (in the second case). Then we perform the chi^2 test of goodness-of-fit to the quadrat counts.
在所有情况下，观察窗口的被划分为若干瓦片，每一瓦片中的数据点的数目进行计数，如描述在quadratcount。样方矩形默认情况下，也可能是任意形状的区域指定的参数tess。预期数量的点，在每个样方的计算，确定由中国南车集团（第一种情况），或通过拟合模型（在第二种情况下）。然后我们执行chi^2测试善良的适合样方数。
The return value is an object of class "htest". Printing the object gives comprehensible output about the outcome of the test.
返回值是一个对象类"htest"。打印对象的可理解的输出测试的结果。
The return value also belongs to the special class "quadrat.test". Plotting the object will display the quadrats, annotated by their observed and expected counts and the Pearson residuals. See the examples.
返回值也属于特殊类"quadrat.test"。绘制的对象将显示的样方，注明所观察到的和预期的计数和Pearson残差。见的例子。
值----------Value----------
An object of class "htest". See chisq.test for explanation.
对象的类"htest"。见chisq.test解释。
The return value is also an object of the special class "quadrat.test", and there is a plot method for this class. See the examples.
返回值是对象的特殊类"quadrat.test"，有一个图，这个类的方法。见的例子。
（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>
参见----------See Also----------
quadrat.test.splitppp, quadratcount, quadrats, quadratresample, chisq.test, kstest.
quadrat.test.splitppp，quadratcount，quadrats，quadratresample，chisq.test，kstest。
To test a Poisson point process model against a specific alternative, use anova.ppm.
要测试泊松点过程模型针对一个特定的替代方案，使用anova.ppm。
实例----------Examples----------
data(simdat)
quadrat.test(simdat)
quadrat.test(simdat, 4, 3)
# quadrat counts[样方数]
qS <- quadratcount(simdat, 4, 3)
quadrat.test(qS)
# fitted model: inhomogeneous Poisson[拟合模型：非齐次泊松]
fitx <- ppm(simdat, ~x, Poisson())
quadrat.test(fitx)
te <- quadrat.test(simdat, 4)
residuals(te) # Pearson residuals[Pearson残差]
plot(te)
plot(simdat, pch="+", cols="green", lwd=2)
plot(te, add=TRUE, col="red", cex=1.4, lty=2, lwd=3)
sublab <- eval(substitute(expression(p[chi^2]==z),
list(z=signif(te$p.value,3))))
title(sub=sublab, cex.sub=3)
# quadrats of irregular shape[不规则形状的样方]
B <- dirichlet(runifpoint(6, simdat$window))
qB <- quadrat.test(simdat, tess=B)
plot(simdat, main="quadrat.test(simdat, tess=B)", pch="+")
plot(qB, add=TRUE, col="red", lwd=2, cex=1.2)
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。
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发表于 2012-9-30 14:03:08
