anova.lppm(spatstat)
anova.lppm()所属R语言包：spatstat
ANOVA for Fitted Point Process Models on Linear Network
线性网络上的合身点过程模型的方差分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs analysis of deviance for two or more fitted point process models on a linear network.
两个或多个安装点过程模型的线性网络上的偏差进行分析。
用法----------Usage----------
## S3 method for class 'lppm'
anova(object, ..., test=NULL, override=FALSE)
参数----------Arguments----------
参数：object
A fitted point process model on a linear network (object of class "lppm").
已安装点过程模型的线性网络（类的对象"lppm"“）。
参数：...
One or more fitted point process models on the same linear network.
一个或多个安装点过程模型，在相同的线性网络。
参数：test
Character string, partially matching one of "Chisq", "F" or "Cp".
字符串，部分匹配的"Chisq"，"F"或"Cp"。
参数：override
Logical flag indicating whether to proceed even when there is no statistical theory to support the calculation.
逻辑标志，指示是否进行统计时，有没有理论支持计算。
Details
详细信息----------Details----------
This is a method for anova for fitted point process models on a linear network (objects of class "lppm", usually generated by the model-fitting function lppm).
这是一个方法anova安装点过程模型的线性网络（类的对象"lppm"，通常由模型的拟合函数lppm）。
If the fitted models are all Poisson point processes, then this function performs an Analysis of Deviance of the fitted models. The output shows the deviance differences (i.e. 2 times log likelihood ratio), the difference in degrees of freedom, and (if test="Chi") the two-sided p-values for the chi-squared tests. Their interpretation is very similar to that in anova.glm.
如果安装的所有型号都泊松点过程，那么这个函数的越轨行为的拟合模型进行分析。输出显示越轨行为的差异（即2倍的对数似然比），自由度的差异，（如果test="Chi"）双面卡方检验的p值。他们的解释是，在anova.glm非常相似。
If some of the fitted models are not Poisson point processes, then there is no statistical theory available to support a similar analysis. The function issues a warning, and (by default) returns a NULL value.
如果一些的拟合模型是泊松点过程，那么有没有的统计理论支持了类似的分析。功能发出警告，（默认情况下）返回一个NULL值。
However if override=TRUE, then a kind of analysis of deviance table will be printed. The "deviance" differences in this table are equal to 2 times the differences in the maximised values of the log pseudolikelihood (see ppm). At the time of writing, there is no statistical theory to support inferential interpretation of log pseudolikelihood ratios. The override option is provided for research purposes only!
但是，如果override=TRUE，然后分析偏差表是一种将被打印。在此表中的“越轨行为”的差异是等于2倍最大化值的logpseudolikelihood的差异（见ppm“）。在写作的时候，有推理解释的logpseudolikelihood比无统计学理论支持。 override选项仅用于研究目的！
值----------Value----------
An object of class "anova", or NULL.
对象类"anova"或NULL。
错误和警告----------Errors and warnings----------
There may be an error message that the models are not “nested”. For an Analysis of Deviance the models must be nested, i.e. one model must be a special case of the other. For example the point process model with formula ~x is a special case of the model with formula ~x+y, so these models are nested. However the two point process models with formulae ~x and ~y are not nested.
有可能会得到一个错误信息，该模型不是“嵌套”。越轨行为的分析模型，必须嵌套，即一个模型必须是一个特殊的情况下，其他。例如，用公式~x点过程模型是一个特殊的情况下，模型与公式~x+y，所以这些模型嵌套。然而，两个点过程模型与公式~x和~y不嵌套。
If you get this error message and you believe that the models should be nested, the problem may be the inability of R to recognise that the two formulae are nested. Try modifying the formulae to make their relationship more obvious.
如果您收到此错误消息，并且相信，该机型都要被嵌套，这个问题可能是无力的R认识到，这两个公式是嵌套的。尝试修改的公式来使他们的关系更加明显。
There may be an error message from anova.glmlist that “models were not all fitted to the same size of dataset”. This generally occurs when the point process models are fitted on different linear networks.
有可能是一个错误消息，anova.glmlist“模型并非所有安装在同样大小的数据集”。这通常发生在当点过程模型被安装在不同的线性网络。
（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>
参考文献----------References----------
Statistical methodology for events on a network. Master's thesis, School of Mathematics and Statistics, University of Western Australia.
Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. To appear in Scandinavian Journal of Statistics.
Fitting Poisson point process models to events on a linear network. Manuscript in preparation.
参见----------See Also----------
lppm
lppm
实例----------Examples----------
example(lpp)
mod0 <- lppm(X, ~1)
modx <- lppm(X, ~x)
anova(mod0, modx, test="Chi")
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 13:08:45
