Gcom(spatstat)
Gcom()所属R语言包：spatstat
Model Compensator of Nearest Neighbour Function
近邻功能的模型补偿器
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given a point process model fitted to a point pattern dataset, this function computes the compensator of the nearest neighbour distance distribution function G based on the fitted model (as well as the usual nonparametric estimates of G based on the data alone). Comparison between the nonparametric and model-compensated G functions serves as a diagnostic for the model.
给定一个点嵌合的点图案数据集的过程模型，该函数计算近邻距离分布函数G基于拟合的模型（以及通常的非参数估计G基于补偿器单独的数据）。作为一个诊断模型的比较之间的非参数和模型补偿G功能。
用法----------Usage----------
Gcom(object, r = NULL, breaks = NULL, ...,
correction = c("border", "Hanisch"),
conditional = !is.poisson(object),
restrict=FALSE,
trend = ~1, interaction = Poisson(),
rbord = reach(interaction),
ppmcorrection="border",
truecoef = NULL, hi.res = NULL)
参数----------Arguments----------
参数：object
Object to be analysed. Either a fitted point process model (object of class "ppm") or a point pattern (object of class "ppp") or quadrature scheme (object of class "quad").
对象，以进行分析。一个安装点过程模型（对象类"ppm"）或阵列点（类"ppp"）或正交计划（对象的类的对象"quad"）。
参数：r
Optional. Vector of values of the argument r at which the function G(r) should be computed. This argument is usually not specified. There is a sensible default.
可选。向量参数的值r的功能G(r)应计算。这种说法是没有规定。有一个合理的默认。
参数：breaks
Optional alternative to r for advanced use.
可选替代r先进的。
参数：correction
Edge correction(s) to be employed in calculating the compensator. Options are "border", "Hanisch" and "best".
边缘校正（S），在计算补偿。选项"border"，"Hanisch"和"best"。
参数：conditional
Optional. Logical value indicating whether to compute the estimates for the conditional case. See Details.
可选。逻辑值，该值指示是否为有条件的情况下计算的估计。查看详细信息。
参数：restrict
Logical value indicating whether to compute the restriction estimator (restrict=TRUE) or the reweighting estimator (restrict=FALSE, the default). Applies only if conditional=TRUE. See Details.
逻辑值，该值指示是否计算的限制估计（restrict=TRUE）或权重调整估计（restrict=FALSE，默认值）。适用只有conditional=TRUE。查看详细信息。
参数：trend,interaction,rbord
Optional. Arguments passed to ppm to fit a point process model to the data, if object is a point pattern. See ppm for details.
可选。参数传递给ppm，以适应一个点过程模型的数据，如果object是一个点模式。见ppm的详细信息。
参数：...
Extra arguments passed to ppm.
额外的参数传递给ppm。
参数：ppmcorrection
The correction argument to ppm.
correctionppm参数。
参数：truecoef
Optional. Numeric vector. If present, this will be treated as if it were the true coefficient vector of the point process model, in calculating the diagnostic. Incompatible with hi.res.
可选。数字矢量。如果有的话，这将被视为是真正的系数向量的点过程模型，计算诊断。不相容的hi.res。
参数：hi.res
Optional. List of parameters passed to quadscheme. If this argument is present, the model will be re-fitted at high resolution as specified by these parameters. The coefficients of the resulting fitted model will be taken as the true coefficients. Then the diagnostic will be computed for the default quadrature scheme, but using the high resolution coefficients.
可选。参数传递给quadscheme。如果这种说法是存在的，该模型将被重新安装在高分辨率所指定这些参数。所得拟合模型的系数将被视为真正的系数。然后诊断将默认的正交计划计算，但使用高分辨率的系数。
Details
详细信息----------Details----------
This command provides a diagnostic for the goodness-of-fit of a point process model fitted to a point pattern dataset. It computes different estimates of the nearest neighbour distance distribution function G of the dataset, which should be approximately equal if the model is a good fit to the data.
此命令提供了一个诊断，适合安装阵列点数据集的点过程模型的恩惠。计算最近邻距离分布函数G的数据集，这应该是大致相等的，如果模型是一个不错的选择的数据不同的估计。
The first argument, object, is usually a fitted point process model (object of class "ppm"), obtained from the model-fitting function ppm.
的第一个参数，object，通常是一个安装点过程模型（类的对象"ppm"），从模型的拟合函数ppm。
For convenience, object can also be a point pattern (object of class "ppp"). In that case, a point process model will be fitted to it, by calling ppm using the arguments trend (for the first order trend), interaction (for the interpoint interaction) and rbord (for the erosion distance in the border correction for the pseudolikelihood). See ppm for details of these arguments.
为方便起见，object也可以是一个点模式（类的对象"ppp"“）。在这种情况下，一个点过程模型将安装它，通过调用ppm使用的参数trend（第一批订单的趋势），interaction INTERPOINT互动和rbord在边界的pseudolikelihood改正的侵蚀距离。见ppm这些参数的详细信息。
The algorithm first extracts the original point pattern dataset (to which the model was fitted) and computes the standard nonparametric estimates of the G function. It then also computes the model-compensated G function. The different functions are returned as columns in a data frame (of class "fv"). The interpretation of the columns is as follows (ignoring edge corrections):
该算法首先提取原始点图案集（该模型的拟合），并计算标准的非参数估计的G功能。然后，它也可以计算的模型补偿G函数。返回不同的功能是作为一个数据框中的列（类"fv"）。列的解释如下（忽略边缘改正）：
the nonparametric border-correction estimate of G(r),
非参数边界校正估计G(r)，
where d[i] is the distance from the i-th data point to its nearest neighbour, and b[i] is the distance from the i-th data point to the boundary of the window W.
其中d[i]是i个数据点的距离其最近的邻居，b[i]i个数据点的边界的距离窗口W。
the model compensator of the border-correction estimate
边界校正估计的模型补偿
where lambda(u,x) denotes the conditional intensity of the model at the location u, and d(u,x) denotes the distance from u to the nearest point in x, while b(u) denotes the distance from u to the boundary of the windowW.
lambda(u,x)表示有条件强度的模型的位置u，d(u,x)表示从u在x的最近点的距离的，而 b(u)表示从u到的窗口W的边界的距离。
the nonparametric Hanisch estimate of G(r)
的非参数Hanisch的估计，G(r)
where
哪里
in which W[-r] denotes the erosion of the window W by a distance r.
其中W[-r]表示的距离W窗口r的侵蚀。
the corresponding model-compensated function
相应的模型补偿功能
where d(u) = d(u, x) is the ("empty space") distance from location u to the nearest point of x.
d(u) = d(u, x)（“空的空间”）的距离位置u的最近点x。
If the fitted model is a Poisson point process, then the formulae above are exactly what is computed. If the fitted model is not Poisson, the formulae above are modified slightly to handle edge effects.
如果拟合模型是一个的泊松点的过程，然后上面的公式究竟是什么计算。如果拟合模型是泊松分布，上述公式稍作修改，处理边缘效应。
The modification is determined by the arguments conditional and restrict. The value of conditional defaults to FALSE for Poisson models and TRUE for non-Poisson models. If conditional=FALSE then the formulae above are not modified. If conditional=TRUE, then the algorithm calculates the restriction estimator if restrict=TRUE, and calculates the reweighting estimator if restrict=FALSE. See Appendix E of Baddeley, Rubak and Moller (2011). Thus, by default, the reweighting estimator is computed for non-Poisson models.
的修改，确定的参数conditional和restrict。 conditional默认值的FALSE泊松模型和TRUE非泊松模型。如果conditional=FALSE然后上面的公式不被修改。如果conditional=TRUE，然后该算法计算的限制估计，如果restrict=TRUE，并计算的重新加权估计，如果restrict=FALSE。请参见附录E的巴德利，Rubak和Moller（2011）的。因此，默认情况下，权重调整估计非泊松模型计算。
The border-corrected and Hanisch-corrected estimates of G(r) are approximately unbiased estimates of the G-function, assuming the point process is stationary. The model-compensated functions are unbiased estimates of the mean value of the corresponding nonparametric estimate, assuming the model is true. Thus, if the model is a good fit, the mean value of the difference between the nonparametric and model-compensated estimates is approximately zero.
边界修正和Hanisch的校正估计G(r)是G功能近似无偏估计，假设点的过程是平稳的。该模型补偿的功能是相应的非参数估计的平均值的无偏估计，假设模型是真实的。因此，如果该模型是一个很好的配合，非参数和模型补偿的估计之间的差的平均值接近于零。
To compute the difference between the nonparametric and model-compensated functions, use Gres.
要计算的非参数和模型补偿功能之间的区别，使用Gres。
值----------Value----------
A function value table (object of class "fv"), essentially a data frame of function values. There is a plot method for this class. See fv.object.
类的对象的函数值表（"fv"），本质上是一个数据框的函数值。有一个图，这个类的方法。见fv.object。
（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>
Ege Rubak and Jesper Moller.
参考文献----------References----------
Score, pseudo-score and residual diagnostics for spatial point process models. Statistical Science 26, 613–646.
参见----------See Also----------
Related functions: Gest, Gres.
相关功能：Gest，Gres。
Alternative functions: Kcom, psstA, psstG, psst.
替代功能：Kcom，psstA，psstG，psst。
Model fitting: ppm.
模型拟合：ppm。
实例----------Examples----------
data(cells)
fit0 <- ppm(cells, ~1) # uniform Poisson[统一的泊松]
G0 <- Gcom(fit0)
G0
plot(G0)
# uniform Poisson is clearly not correct[均匀泊松显然是不正确的]
# Hanisch estimates only[Hanisch估计只有]
plot(Gcom(fit0), cbind(han, hcom) ~ r)
fit1 <- ppm(cells, ~1, Strauss(0.08))
plot(Gcom(fit1), cbind(han, hcom) ~ r)
# Try adjusting interaction distance[尝试调整作用距离]
fit2 <- update(fit1, Strauss(0.10))
plot(Gcom(fit2), cbind(han, hcom) ~ r)
G3 <- Gcom(cells, interaction=Strauss(0.12))
plot(G3, cbind(han, hcom) ~ r)
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 13:31:00
