bermantest(spatstat)
bermantest()所属R语言包：spatstat
Berman's Tests for Point Process Model
伯曼的测试点过程模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Tests the goodness-of-fit of a Poisson point process model using methods of Berman (1986).
测试的善良，对拟合一个泊松点过程模型的使用方法伯曼（1986）。
用法----------Usage----------
bermantest(...)
## S3 method for class 'ppp'
bermantest(X, covariate,
which = c("Z1", "Z2"),
alternative = c("two.sided", "less", "greater"), ...)
## S3 method for class 'ppm'
bermantest(model, covariate,
which = c("Z1", "Z2"),
alternative = c("two.sided", "less", "greater"), ...)
参数----------Arguments----------
参数：X
A point pattern (object of class "ppp").
点模式（类的对象"ppp"）。
参数：model
A fitted point process model (object of class "ppm").
已安装点过程模型（对象类"ppm"“）。
参数：covariate
The spatial covariate on which the test will be based. An image (object of class "im") or a function.
测试将根据空间的协变量。图像（类"im"）或函数对象。
参数：which
Character string specifying the choice of test.
字符串指定所选择的测试。
参数：alternative
Character string specifying the alternative hypothesis.
字符串指定其他的假设。
参数：...
Ignored.
忽略。
Details
详细信息----------Details----------
These functions perform a goodness-of-fit test of a Poisson point process model fitted to point pattern data. The observed distribution of the values of a spatial covariate at the data points, and the predicted distribution of the same values under the model, are compared using either of two test statistics Z[1] and Z[2] proposed by Berman (1986).
这些函数执行一个善良的配点模式数据的泊松点过程模型的拟合优度检验。所观察到的分布的一个空间的协变量的数据点，并根据该模型的相同的值的预测分布的值，使用两个测试统计Z[1]和Z[2]所提出伯曼（比较1986）。
The function bermantest is generic, with methods for point patterns ("ppp") and point process models ("ppm").
函数bermantest是通用的，与方法点模式（"ppp"）和点过程模型（"ppm"）的。
If X is a point pattern dataset (object of class "ppp"), then bermantest(X, ...) performs a goodness-of-fit test of the uniform Poisson point process (Complete Spatial Randomness, CSR) for this dataset.
如果X是一个点图案集（类的对象"ppp"），然后bermantest(X, ...)执行一个善良的拟合优度检验的统一Poisson点过程（完整的空间随机性，CSR）此数据集。
If model is a fitted point process model (object of class "ppm") then bermantest(model, ...) performs a test of goodness-of-fit for this fitted model. In this case, model should be a Poisson point process.
model如果是一个拟合点过程模型（类的对象"ppm"）然后bermantest(model, ...)善良的适合这个模型拟合进行测试。在这种情况下，model应该是一个泊松点过程。
The test is performed by comparing the observed distribution of the values of a spatial covariate at the data points, and the predicted distribution of the same covariate under the model. Thus, you must nominate a spatial covariate for this test.
试验进行比较观察到的空间协变量的数据点，和相同的协变量模型下的预测分布的值分布。因此，你必须提名本次测试的协变量的空间。
The argument covariate should be either a function(x,y) or a pixel image (object of class "im" containing the values of a spatial function. If covariate is an image, it should have numeric values, and its domain should cover the observation window of the model. If covariate is a function, it should expect two arguments x and y which are vectors of coordinates, and it should return a numeric vector of the same length as x and y.
参数covariate应该是的function(x,y)或像素图像（类的对象"im"如果covariate包含值的空间功能。是一个图像，它应该有数字值，其领域涵盖了观察窗的model如果covariate是一个函数，它应该期望两个参数x和y这是向量的坐标，并且它应该返回一个数值向量相同的长度x和y。
First the original data point pattern is extracted from model. The values of the covariate at these data points are collected.
首先，原始数据点模式提取model。 covariate在这些数据点的值被收集。
Next the values of the covariate at all locations in the observation window are evaluated. The point process intensity of the fitted model is also evaluated at all locations in the window.
接下来的值covariate在观察窗口在所有地点进行评估。点过程强度的拟合模型也计算在窗口中的所有位置。
If which="Z1", the test statistic Z[1] is computed as follows. The sum S of the covariate values at all data points is evaluated. The predicted mean mu and variance sigma^2 of S are computed from the values of the covariate at all locations in the window. Then we compute Z[1]=(S-mu)/sigma.
如果which="Z1"，检验统计量Z[1]的计算方式如下。的总和S所有数据点的协变量值进行评估。预测平均mu和方差sigma^2S计算出的协变量的值在窗口中的所有位置。然后我们计算Z[1]=(S-mu)/sigma。
If which="Z2", the test statistic Z[2] is computed as follows. The values of the covariate at all locations in the observation window, weighted by the point process intensity, are compiled into a cumulative distribution function F. The probability integral transformation is then applied: the values of the covariate at the original data points are transformed by the predicted cumulative distribution function F into numbers between 0 and 1. If the model is correct, these numbers are i.i.d. uniform random numbers. The standardised sample mean of these numbers is the statistic Z[2].
如果which="Z2"，检验统计量Z[2]的计算方式如下。的值covariate在观察窗口中的所有位置，由点过程强度加权，被编译成一个累积分布函数F。的概率积分变换，然后应用：covariate的值，在被转换的原始数据点是由所述被预测的累积分布函数F到0和1之间的数字。如果模型是正确的，这些数字是独立同分布均匀分布的随机数。这些数字的平均值的标准样本的统计Z[2]。
In both cases the null distribution of the test statistic is the standard normal distribution, approximately.
在这两种情况下，空分布的检验统计量是标准正态分布，约。
The return value is an object of class "htest" containing the results of the hypothesis test. The print method for this class gives an informative summary of the test outcome.
返回值是一个类的对象"htest"包含的假设检验的结果。这一类的print方法的测试结果给出了一个翔实的总结。
值----------Value----------
An object of class "htest" (hypothesis test) and also of class "bermantest", containing the results of the test. The return value can be plotted (by plot.bermantest) or printed to give an informative summary of the test.
一个对象的类"htest"（假设检验）和类"bermantest"，包含测试结果。可以绘制（plot.bermantest）的返回值，或，印刷给一个翔实的总结的测试。
警告----------Warning----------
The meaning of a one-sided test must be carefully scrutinised: see the printed output.
必须仔细检查的意义是一种片面的测试：看的打印输出。
（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----------
Testing for spatial association between a point process and another stochastic process. Applied Statistics 35, 54–62.
参见----------See Also----------
kstest, quadrat.test, ppm
kstest，quadrat.test，ppm
实例----------Examples----------
# Berman's data[伯曼的数据]
data(copper)
X <- copper$SouthPoints
L <- copper$SouthLines
D <- distmap(L, eps=1)
# test of CSR[测试的企业社会责任]
bermantest(X, D)
bermantest(X, D, "Z2")
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 13:13:16
