vcov.ppm(spatstat)
vcov.ppm()所属R语言包：spatstat
Variance-Covariance Matrix for a Fitted Point Process Model
方差 - 协方差矩阵的拟合点过程模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns the variance-covariance matrix of the estimates of the parameters of a fitted point process model.
返回的方差 - 协方差矩阵的拟合点过程模型的参数估计。
用法----------Usage----------
## S3 method for class 'ppm'
vcov(object, ..., what = "vcov", verbose = TRUE,
gam.action=c("warn", "fatal", "silent"),
matrix.action=c("warn", "fatal", "silent"),
hessian=FALSE)
参数----------Arguments----------
参数：object
A fitted point process model (an object of class "ppm".)
已安装点过程模型（对象类"ppm"。）
参数：...
Ignored.
忽略。
参数：what
Character string (partially-matched) that specifies what matrix is returned. Options are "vcov" for the variance-covariance matrix, "corr" for the correlation matrix, and "fisher" or "Fisher" for the Fisher information matrix.
，它指定矩阵，则返回的字符串（部分匹配）。选项是方差 - 协方差矩阵"vcov"，"corr"的相关矩阵，和"fisher"或"Fisher"Fisher信息矩阵。
参数：verbose
Logical. If TRUE, a message will be printed if various minor problems are encountered.
逻辑。如果TRUE，消息将被打印，如果遇到各种小问题。
参数：gam.action
String indicating what to do if object was fitted by gam.
字符串，表示做什么，如果object拟合gam。
参数：matrix.action
String indicating what to do if the matrix is ill-conditioned (so that its inverse cannot be calculated).
字符串，指示该怎么做，如果矩阵是病态的（所以无法计算其逆）。
参数：hessian
Logical. Use the negative Hessian matrix of the log pseudolikelihood instead of the Fisher information.
逻辑。使用Hessian矩阵的Fisher信息的的logpseudolikelihood，而不是消极。
Details
详细信息----------Details----------
This function computes the asymptotic variance-covariance matrix of the estimates of the canonical parameters in the point process model object. It is a method for the generic function vcov.
此函数计算的典型参数估计的渐近方差 - 协方差矩阵中的点过程模型object。这是一个方法的通用函数vcov。
object should be an object of class "ppm", typically produced by ppm.
object应该是的对象类"ppm"，通常是由ppm。
The canonical parameters of the fitted model object are the quantities returned by coef.ppm(object). The function vcov calculates the variance-covariance matrix for these parameters.
规范参数的拟合模型object是的数量返回coef.ppm(object)。的功能vcov计算这些参数的方差 - 协方差矩阵。
The argument what provides three options:
参数what提供了三个选项：
return the variance-covariance matrix of the parameter estimates
返回参数估计的方差 - 协方差矩阵
return the correlation matrix of the parameter estimates
返回的相关矩阵的参数估计
return the observed Fisher information matrix.
返回所观察到的Fisher信息矩阵。
In all three cases, the result is a square matrix. The rows and columns of the matrix correspond to the canonical parameters given by coef.ppm(object). The row and column names of the matrix are also identical to the names in coef.ppm(object).
在所有这三种情况下，其结果是一个方阵。的矩阵的行和列对应的规范给出的参数由coef.ppm(object)。在coef.ppm(object)的名称相同的矩阵的行和列名。
For models fitted by maximum pseudolikelihood (which is the default in ppm), the implementation works as follows.
对于安装“的最大pseudolikelihood（这是默认情况下，在ppm），执行如下的模型。
If the fitted model object is a Poisson process, the calculations are based on standard asymptotic theory for the maximum likelihood estimator (Kutoyants, 1998). The observed Fisher information matrix of the fitted model object is first computed, by summing over the Berman-Turner quadrature points in the fitted model. The asymptotic variance-covariance matrix is calculated as the inverse of the observed Fisher information. The correlation matrix is then obtained by normalising.
如果拟合模型object是一个泊松过程，计算是基于标准的渐近理论的最大似然估计（Kutoyants，1998年）。先计算所观察到的Fisher信息矩阵的拟合模型object，总结在伯曼 - 特纳正交点的拟合模型。渐近方差 - 协方差矩阵计算所观察到的Fisher信息的倒数。的相关矩阵，然后通过以下方式获得标准化。
If the fitted model is not a Poisson process (i.e. it is some other Gibbs point process) then the calculations are based on Coeurjolly and Rubak (2012). A consistent estimator of the variance-covariance matrix is computed by summing terms over all pairs of data points. If required, the Fisher information is calculated as the inverse of the variance-covariance matrix.
如果拟合模型是一个泊松过程（也就是说，它是一些其他的吉布斯点过程），那么计算是基于Coeurjolly和Rubak（2012年）。总结的所有数据点对的方差 - 协方差矩阵的计算方法是一致的估计。如果需要，Fisher信息的方差 - 协方差矩阵的逆计算。
For models fitted by the Huang-Ogata method (method="ho" in the call to ppm), the implementation uses the Monte Carlo estimate of the Fisher information matrix that was computed when the original model was fitted.
的型号配黄绪方法（method="ho"在调用ppm），在实现中使用的蒙特卡洛估计的Fisher信息矩阵计算时，原来的模型拟合。
The argument verbose makes it possible to suppress some diagnostic messages.
参数verbose使得它可以抑制一些诊断信息。
The asymptotic theory is not correct if the model was fitted using gam (by calling ppm with use.gam=TRUE). The argument gamaction determines what to do in this case. If gamaction="fatal", an error is generated. If gamaction="warn", a warning is issued and the calculation proceeds using the incorrect theory for the parametric case, which is probably a reasonable approximation in many applications. If gamaction="silent", the calculation proceeds without a warning.
渐近理论是不正确的，如果模型拟合通过调用gam使用ppm（use.gam=TRUE“）。参数gamaction在这种情况下，决定做什么。如果gamaction="fatal"，就会产生错误。如果gamaction="warn"，警告发出和计算过程的参数的情况下，这可能是一个合理的近似，在许多应用中使用不正确的理论。如果gamaction="silent"，计算进行没有警告。
If hessian=TRUE then the negative Hessian (second derivative) matrix of the log pseudolikelihood, and its inverse, will be computed. For non-Poisson models, this is not a valid estimate of variance, but is useful for other calculations.
如果hessian=TRUE然后负Hessian矩阵（二阶导数）矩阵logpseudolikelihood，和它的逆，将计算的。对于非泊松模型，这是不是一个有效的方差的估计，但可用于其他计算。
Note that standard errors and 95 percent confidence intervals for the coefficients can also be obtained using coef(summary(object)).
请注意，标准误差及系数的95％置信区间，也可以以下方式获得使用coef(summary(object))。
值----------Value----------
A square matrix.
一个方阵。
错误消息----------Error messages----------
An error message that reports system is computationally singular indicates that the determinant of the Fisher information matrix was either too large or too small for reliable numerical calculation. This can occur because of numerical overflow or collinearity in the covariates. To check this, rescale the coordinates of the data points and refit the model. See the Examples.
错误消息，报告系统的计算奇异的Fisher信息矩阵的行列式太大或太小，可靠的数值计算。这可能是由于数值溢出或协变量的共线性。要进行检查，重新调整数据点的坐标，并重新安装的模式。请参阅范例。
In a Gibbs model, a singular matrix may also occur if the fitted model is a hard core process: this is a feature of the variance estimator.
在Gibbs模型中，一个奇异矩阵，也可能发生，如果合适的模型是一个硬核的过程：这是一个功能的方差估计。
（作者）----------Author(s)----------
Original code for Poisson point process was written by
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>.
New code for stationary Gibbs point processes was generously contributed by
Ege Rubak and Jean-Francois Coeurjolly.
New code for generic Gibbs process written by Adrian Baddeley.
参考文献----------References----------
Fast covariance estimation for innovations computed from a spatial Gibbs point process. Research Report, Centre for Stochastic Geometry and Bioimaging, Denmark, 2012. www.csgb.dk
Statistical Inference for Spatial Poisson Processes, Lecture Notes in Statistics 134. New York: Springer 1998.
实例----------Examples----------
X <- rpoispp(42)
fit <- ppm(X, ~ x + y)
vcov(fit)
vcov(fit, what="Fish")
# example of singular system[例如，奇异系统]
data(demopat)
m <- ppm(demopat, ~polynom(x,y,2))
## Not run: [＃不运行：]
try(v <- vcov(m))
## End(Not run)[＃（不执行）]
# rescale x, y coordinates to range [0,1] x [0,1] approximately[重新调整X，Y坐标范围为[0,1]×[0,1]约]
demopat <- rescale(demopat, 10000)
m <- ppm(demopat, ~polynom(x,y,2))
v <- vcov(m)
# Gibbs example[吉布斯例如]
fitS <- ppm(swedishpines, ~1, Strauss(9))
coef(fitS)
sqrt(diag(vcov(fitS)))
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 14:20:09
