lurking(spatstat)
lurking()所属R语言包：spatstat
Lurking variable plot
潜伏变量图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plot spatial point process residuals against a covariate
的图空间点过程残差对协变量
用法----------Usage----------
lurking(object, covariate, type="eem",
cumulative=TRUE,
clipwindow=default.clipwindow(object),
rv,
plot.sd, plot.it=TRUE,
typename,
covname,
oldstyle=FALSE, check=TRUE,
...,
splineargs=list(spar=0.5))
参数----------Arguments----------
参数：object
The fitted point process model (an object of class "ppm") for which diagnostics should be produced. This object is usually obtained from ppm. Alternatively, object may be a point pattern (object of class "ppp").
拟合点过程模型（类的一个对象"ppm"）的诊断应。这个对象通常是从ppm。另外，object可能是一个点模式（类的对象"ppp"“）。
参数：covariate
The covariate against which residuals should be plotted. Either a numeric vector, a pixel image, or an expression. See Details below.
协对残差绘制。可以是数字型的向量，一个像素的图像，或expression。请参见下面的详细信息。
参数：type
String indicating the type of residuals or weights to be computed. Choices include "eem", "raw", "inverse" and "pearson". See diagnose.ppm for all possible choices.
表示残差或要计算的权重的类型的字符串。选项包括"eem"，"raw"，"inverse"和"pearson"。见diagnose.ppm所有可能的选择。
参数：cumulative
Logical flag indicating whether to plot a cumulative sum of marks (cumulative=TRUE) or the derivative of this sum, a marginal density of the smoothed residual field (cumulative=FALSE).
逻辑标志，指示是否绘制累积分数之（cumulative=TRUE）或衍生工具的款项，边缘密度平滑后的剩余磁场（cumulative=FALSE）。
参数：clipwindow
If not NULL this argument indicates that residuals shall only be computed inside a subregion of the window containing the original point pattern data. Then clipwindow should be a window object of class "owin".
如果不是NULL这种说法表明，残留物只应计算的一个子区域内的窗口，其中包含原点的图形数据。 clipwindow应该是一个窗口对象的类"owin"。
参数：rv
Usually absent. If this argument is present, the point process residuals will not be calculated from the fitted model object, but will instead be taken directly from rv.
通常不存在。如果这种说法是存在的，这一点加工残余物将不被计算拟合模型object，而是会直接取自rv。
参数：plot.sd
Logical value indicating whether error bounds should be added to plot. The default is TRUE for Poisson models and FALSE for non-Poisson models. See Details.
逻辑值的误差范围是否应该被添加到图。默认值是TRUE泊松模型和FALSE非泊松模型。查看详细信息。
参数：plot.it
Logical value indicating whether plots should be shown. If plot.it=FALSE, only the computed coordinates for the plots are returned. See Value.
逻辑值，该值指示是否应显示图。如果plot.it=FALSE，只计算坐标传回的图。看到价值。
参数：typename
Usually absent. If this argument is present, it should be a string, and will be used (in the axis labels of plots) to describe the type of residuals.
通常不存在。如果这种说法是存在的，它应该是一个字符串，将使用（图中的轴标签）来描述残差的类型。
参数：covname
A string name for the covariate, to be used in axis labels of plots.
一个字符串，名字为协变量，可用于轴标签的图。
参数：oldstyle
Logical flag indicating whether error bounds should be plotted using the approximation given in the original paper (oldstyle=TRUE), or using the correct asymptotic formula (oldstyle=FALSE).
逻辑标志指示的误差范围是否应使用近似的原始文件（绘制oldstyle=TRUE），或使用正确的渐近公式（oldstyle=FALSE）。
参数：check
Logical flag indicating whether the integrity of the data structure in object should be checked.
逻辑标志，指示是否应检查在object的数据结构的完整性。
参数：...
Arguments passed to plot.default and lines to control the plot behaviour.
参数传递给plot.default和lines控制的图的行为。
参数：splineargs
A list of arguments passed to smooth.spline for the estimation of the derivatives in the case cumulative=FALSE.
列表的参数传递给smooth.spline的估计的衍生工具的情况下cumulative=FALSE的。
Details
详细信息----------Details----------
This function generates a "lurking variable" plot for a fitted point process model. Residuals from the model represented by object are plotted against the covariate specified by covariate. This plot can be used to reveal departures from the fitted model, in particular, to reveal that the point pattern depends on the covariate.
这个函数生成一个“潜伏变量的拟合点过程模型图。残差模型为代表的object暗算协指定的covariate。此图可以用来揭示偏离拟合模型，特别是，以显示点图案取决于协变量。
First the residuals from the fitted model (Baddeley et al, 2004) are computed at each quadrature point, or alternatively the "exponential energy marks" (Stoyan and Grabarnik, 1991) are computed at each data point. The argument type selects the type of residual or weight. See diagnose.ppm for options and explanation.
首先，从拟合模型（巴德利等人，2004年）的残差计算在每个正交点，或者指数能量马克（斯托扬和Grabarnik的，1991），计算在每个数据点。选择的参数type残留或重的类型。见diagnose.ppm的选项和解释。
A lurking variable plot for point processes (Baddeley et al, 2004) displays either the cumulative sum of residuals/weights (if cumulative = TRUE) or a kernel-weighted average of the residuals/weights (if cumulative = FALSE) plotted against the covariate. The empirical plot (solid lines) is shown together with its expected value assuming the model is true (dashed lines) and optionally also the pointwise two-standard-deviation limits (dotted lines).
一种潜在的变量积点过程（巴德利等人，2004）显示的累计总和的残留物/重量（cumulative = TRUE）或内核加权平均残差/重量（如果cumulative = FALSE）暗算协变量。实证的曲线（实线）一起示出其预期的值假定模型是真实的（虚线），和任选的逐点的两个标准偏差的限制（虚线）。
To be more precise, let Z(u) denote the value of the covariate at a spatial location u.
为了更精确，让Z(u)表示的协变量的值在一个空间位置u。
If cumulative=TRUE then we plot H(z) against z, where H(z) is the sum of the residuals over all quadrature points where the covariate takes a value less than or equal to z, or the sum of the exponential energy weights over all data points where the covariate takes a value less than or equal to z.
如果cumulative=TRUE然后我们绘制H(z)对z，其中H(z)是余数的总和正交点的协变量的值小于或等于 X>，或其中协变量的值小于或等于z的所有数据点的指数能量重量超过的总和。
If cumulative=FALSE then we plot h(z) against z, where h(z) is the derivative of H(z), computed approximately by spline smoothing.
如果cumulative=FALSE然后我们绘制h(z)对z，其中h(z)是衍生的H(z)，近似计算的样条平滑。
For the point process residuals E(H(z)) = 0, while for the exponential energy weights E(H(z)) = area of the subset of the window satisfying Z(u) ≤ z.
对于点过程残差E(H(z)) = 0，而指数能量重量E(H(z)) = 的子集的窗口的面积满足Z(u) ≤ z。
If the empirical and theoretical curves deviate substantially from one another, the interpretation is that the fitted model does not correctly account for dependence on the covariate. The correct form (of the spatial trend part of the model) may be suggested by the shape of the plot.
如果经验和理论曲线大幅偏离彼此的解释是，拟合模型没有正确帐户的协变量的依赖。的正确形式的空间趋势模型的一部分，可以提出形状的图。
If plot.sd = TRUE, then superimposed on the lurking variable plot are the pointwise two-standard-deviation error limits for H(x) calculated for the inhomogeneous Poisson process. The default is plot.sd = TRUE for Poisson models and plot.sd = FALSE for non-Poisson models.
如果plot.sd = TRUE，对潜伏变量图的再叠加是逐点两个标准偏差限制H(x)计算的非齐次泊松过程。默认值是plot.sd = TRUE泊松模型和plot.sd = FALSE非泊松模型。
By default, the two-standard-deviation limits are calculated from the exact formula for the asymptotic variance of the residuals under the asymptotic normal approximation, equation (37) of Baddeley et al (2006). However, for compatibility with the original paper of Baddeley et al (2005), if oldstyle=TRUE, the two-standard-deviation limits are calculated using the innovation variance, an over-estimate of the true variance of the residuals.
默认情况下，两个标准差的范围进行计算的精确公式的渐近方差的渐近正态近似，方程（37），亚伦 - 巴德利等人（2006）的残差下。然而，巴德利等人（2005）的原始文件的兼容性，如果oldstyle=TRUE，两个标准差的范围进行计算，使用创新的差异，高估的真实方差的残差。
The argument object must be a fitted point process model (object of class "ppm") typically produced by the maximum pseudolikelihood fitting algorithm ppm).
参数object必须是一个安装点过程模型（类的对象"ppm"）的的最大pseudolikelihood拟合算法通常是由ppm）。
The argument covariate is either a numeric vector, a pixel image, or an R language expression. If it is a numeric vector, it is assumed to contain the values of the covariate for each of the quadrature points in the fitted model. The quadrature points can be extracted by quad.ppm(object).
参数covariate是一个数值向量，像素图像，或R语言表达。如果它是一个数值向量，它被假定为包含协变量的值的每个正交点拟合模型。正交，可以提取quad.ppm(object)。
If covariate is a pixel image, it is assumed to contain the values of the covariate at each location in the window. The values of this image at the quadrature points will be extracted.
如果covariate是一个像素的图像，它被假定为包含在窗口中的每个位置处的值的协变量。此图像的值，将被提取的正交点。
Alternatively, if covariate is an expression, it will be evaluated in the same environment as the model formula used in fitting the model object. It must yield a vector of the same length as the number of quadrature points. The expression may contain the terms x and y representing the cartesian coordinates, and may also contain other variables that were available when the model was fitted. Certain variable names are reserved words; see ppm.
或者，如果covariate是一个expression，它将会在同一环境中评估作为模型公式中使用拟合模型object。就必须产生的正交点的数目相同的长度的矢量。表达式可能包含的术语x和y代表直角坐标系，也可以含有其它变量时可用模型拟合。某些变量名是保留字; ppm。
Note that lurking variable plots for the x and y coordinates are also generated by diagnose.ppm, amongst other types of diagnostic plots. This function is more general in that it enables the user to plot the residuals against any chosen covariate that may have been present.
需要注意的是潜伏变量图x和y，其中包括其他类型的诊断图diagnose.ppm的坐标也产生了。此功能是更普遍的，因为它使用户能够对可能已经存在的任何选定的协变量绘制残差。
For advanced use, even the values of the residuals/weights can be altered. If the argument rv is present, the residuals will not be calculated from the fitted model object but will instead be taken directly from the object rv. If type = "eem" then rv should be similar to the return value of eem, namely, a numeric vector with length equal to the number of data points in the original point pattern. Otherwise, rv should be similar to the return value of residuals.ppm, that is, rv should be an object of class "msr" (see msr) representing a signed measure.
高级的应用，甚至可以改变的值的残差/重量。如果参数rv存在，将不被计算的残差拟合模型object，而将采取直接从对象rv。如果type = "eem"然后rv的返回值应该是相似的eem，即一个数值向量，其长度等于原来的点图案中的数据点的数目。否则，rv应该是类似的返回值residuals.ppm，rv应该是一个类的对象，“"msr"（见msr）较签署措施。
值----------Value----------
A list containing two dataframes empirical and theoretical. The first dataframe empirical contains columns covariate and value giving the coordinates of the lurking variable plot. The second dataframe theoretical contains columns covariate, mean and sd giving the coordinates of the plot of the theoretical mean and standard deviation.
列表包含两个dataframes empirical和theoretical。第一个数据框empirical列covariate和value的潜伏变量图的坐标。第二个数据框theoretical包含列covariate，mean和sd的坐标的的理论均值和标准差的图。
（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----------
Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67, 617–666.
Properties of residuals for spatial point processes. To appear.
Second-order characteristics for stochastic structures connected with Gibbs point processes. Mathematische Nachrichten, 151:95–100.
参见----------See Also----------
residuals.ppm, diagnose.ppm, residuals.ppm, qqplot.ppm, eem, ppm
residuals.ppm，diagnose.ppm，residuals.ppm，qqplot.ppm，eem，ppm
实例----------Examples----------
data(nztrees)
lurking(nztrees, expression(x))
fit <- ppm(nztrees, ~x, Poisson())
lurking(fit, expression(x))
lurking(fit, expression(x), cumulative=FALSE)
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 13:44:34
