dfbetas.ppm(spatstat)
dfbetas.ppm()所属R语言包：spatstat
Parameter influence measure
参数影响的措施
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the deletion influence measure for each parameter in a fitted point process model.
在装配点过程模型的每个参数计算的缺失影响的措施。
用法----------Usage----------
## S3 method for class 'ppm'
dfbetas(model, ..., drop = FALSE, iScore=NULL,
iHessian=NULL, iArgs=list())
参数----------Arguments----------
参数：model
Fitted point process model (object of class "ppm").
合身点过程模型（对象类"ppm"“）。
参数：...
Ignored.
忽略。
参数：drop
Logical. Whether to include (drop=FALSE) or exclude (drop=TRUE) contributions from quadrature points that were not used to fit the model.
逻辑。是否包括（drop=FALSE）或排除（drop=TRUE）的贡献正交点，不是用来拟合模型。
参数：iScore,iHessian
Components of the score vector and Hessian matrix for the irregular parameters, if required. See Details.
为不规则的参数的得分向量和Hessian矩阵的组件，如果需要的话。查看详细信息。
参数：iArgs
List of extra arguments for the functions iScore, iHessian if required.
额外的参数列表的功能iScore，iHessian如果需要的话。
Details
详细信息----------Details----------
Given a fitted spatial point process model, this function computes the influence measure for each parameter, as described in Baddeley, Chang and Song (2011).
合身的空间点过程model，这个函数计算每个参数的影响的措施，如在灸手可热，张和松（2011）。
This is a method for the generic function dfbetas.
这是一个方法的通用函数dfbetas。
The influence measure for each parameter theta is a signed measure in two-dimensional space. It consists of a discrete mass on each data point (i.e. each point in the point pattern to which the model was originally fitted) and a continuous density at all locations. The mass at a data point represents the change in the fitted value of the parameter theta that would occur if this data point were to be deleted. The density at other non-data locations represents the effect (on the fitted value of theta) of deleting these locations (and their associated covariate values) from the input to the fitting procedure.
影响度量为每个参数theta是一个带符号的测量在两维空间中。它由每个数据点上的离散的质量（即每个点中的点的图案，model最初安装的话）和在所有位置的连续密度。在一个数据点代表的质量参数theta这会发生，如果这样的数据点以被删除的拟合值的变化。其他的非数据的位置处的密度表示的效果（拟合值theta）删去这些位置（以及它们的相关的协变量值），从输入的拟合程序。
If the point process model trend has irregular parameters that were fitted (using ippm) then the influence calculation requires the first and second derivatives of the log trend with respect to the irregular parameters. The argument iScore should be a list, with one entry for each irregular parameter, of R functions that compute the partial derivatives of the log trend (i.e. log intensity or log conditional intensity) with respect to each irregular parameter. The argument iHessian should be a list, with p^2 entries where p is the number of irregular parameters, of R functions that compute the second order partial derivatives of the log trend with respect to each pair of irregular parameters.
如果该点的过程模型的趋势有不规则的参数进行拟合（使用ippm）然后计算的影响需要log趋势相对于不规则的参数的第一和第二的衍生物。参数iScore应该是一个列表，其中有一项为每一个不规则的参数，计算每个不规则参数的偏导数的log的趋势（即log的强度或记录有条件强度）的R函数。参数iHessian应该是一个列表，p^2项p是不规则的参数，R函数来计算的二阶偏导数的log的趋势，对于每个对不规则的参数。
值----------Value----------
An object of class "msr" representing a signed or vector-valued measure.
类的一个对象"msr"代表签署的向量值的措施。
（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----------
Leverage and influence diagnostics for spatial point process models. Scandinavian Journal of Statistics, in press.
参见----------See Also----------
leverage.ppm, influence.ppm
leverage.ppm，influence.ppm
实例----------Examples----------
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X, ~x+y)
plot(dfbetas(fit))
plot(smooth.msr(dfbetas(fit)))
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 13:22:08
