rhohat(spatstat)
rhohat()所属R语言包：spatstat
Smoothing Estimate of Covariate Transformation
平滑估计的协方差转型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes a smoothing estimate of the intensity of a point process, as a function of a (continuous) spatial covariate.
计算的点处理的强度，作为一个（连续的）空间的协变量的函数的平滑估计。
用法----------Usage----------
rhohat(object, covariate, ...,
method=c("ratio", "reweight", "transform"),
smoother=c("kernel", "local"),
dimyx=NULL, eps=NULL,
n = 512, bw = "nrd0", adjust=1, from = NULL, to = NULL,
bwref=bw,
covname, confidence=0.95)
参数----------Arguments----------
参数：object
A point pattern (object of class "ppp"), a quadrature scheme (object of class "quad") or a fitted point process model (object of class "ppm").
点模式（类的对象"ppp"），一个正交计划（类的对象"quad"）或拟合点过程模型（类的对象"ppm"）。
参数：covariate
Either a function(x,y) or a pixel image (object of class "im") providing the values of the covariate at any location. Alternatively one of the strings "x" or "y" signifying the Cartesian coordinates.
无论是function(x,y)或像素图像（对象类"im"）提供的协变量的值，在任何位置。另外的字符串之一"x"或"y"标志着在直角坐标系。
参数：method
Character string determining the smoothing method. See Details.
字符串确定平滑方法。查看详细信息。
参数：smoother
Character string determining the smoothing algorithm. See Details.
字符串确定的平滑算法。查看详细信息。
参数：dimyx,eps
Arguments passed to as.mask to control the pixel resolution at which the covariate will be evaluated.
参数传递给as.mask控制像素的分辨率在协变量进行评估。
参数：bw
Smoothing bandwidth or bandwidth rule (passed to density.default).
平滑带宽或带宽的规则（传递到density.default）。
参数：adjust
Smoothing bandwidth adjustment factor (passed to density.default).
平滑带宽调整的系数（传递到density.default）。
参数：n, from, to
Arguments passed to density.default to control the number and range of values at which the function will be estimated.
参数传递给density.default控制的数量和范围，将估计值的功能。
参数：bwref
Optional. An alternative value of bw to use when smoothing the reference density (the density of the covariate values observed at all locations in the window).
可选。 bw使用平滑的参考密度（密度在所有位置的窗口中观察到的协变量值）时的一个替代值。
参数：...
Additional arguments passed to density.default or locfit.
额外的参数传递给density.default或locfit。
参数：covname
Optional. Character string to use as the name of the covariate.
可选。要使用的字符串作为协变量的名称。
参数：confidence
Confidence level for confidence intervals. A number between 0 and 1.
置信区间的置信水平。 0和1之间的一个数字。
Details
详细信息----------Details----------
If object is a point pattern, this command assumes that object is a realisation of a Poisson point process with intensity function lambda(u) of the form
如果object是一个点的模式，该命令将假定这object是一个强度功能lambda(u)的形式实现的泊松点过程中，
where Z is the spatial covariate function given by covariate, and rho(z) is a function to be estimated. This command computes estimators of rho(z) proposed by Baddeley and Turner (2005) and Baddeley et al (2012).
Z是covariate和rho(z)是一个函数进行估计的空间协变量的函数。该命令计算估计rho(z)巴德利和Turner（2005年）和巴德利等人（2012年）提出的。
The covariate Z must have continuous values.
协Z必须具有连续值。
If object is a fitted point process model, suppose X is the original data point pattern to which the model was fitted. Then this command assumes X is a realisation of a Poisson point process with intensity function of the form
object如果是一个拟合点过程模型，假设X的原始数据点图案的模型拟合。然后，该命令将假定X是一个实现函数的形式与强度的泊松点过程
where kappa(u) is the intensity of the fitted model object. A smoothing estimator of rho(z) is computed.
kappa(u)是拟合模型object的强度。 rho(z)的平滑估计计算。
The estimation procedure is determined by the character strings method and smoother. The estimation procedure involves computing several density estimates and combining them. The algorithm used to compute density estimates is determined by smoother:
估计程序的决定由字符串method和smoother。估计过程涉及数密度估计计算，并结合他们。用来计算密度估计的算法是由smoother：
If smoother="kernel", each the smoothing procedure is based on fixed-bandwidth kernel density estimation, performed by density.default.
如果smoother="kernel"，每一个平滑过程是基于固定带宽的内核密度估计，进行density.default。
If smoother="local", the smoothing procedure is based on local likelihood density estimation, performed by locfit.
如果smoother="local"，平滑程序是基于局部似然密度估计，进行locfit。
The method determines how the density estimates will be combined to obtain an estimate of rho(z):
决定如何密度估计将合并，以获得method估计rho(z)：
If method="ratio", then rho(z) is estimated by the ratio of two density estimates. The numerator is a (rescaled) density estimate obtained by smoothing the values Z(y[i]) of the covariate Z observed at the data points y[i]. The denominator is a density estimate of the reference distribution of Z.
如果method="ratio"，那么rho(z)估计两个密度估计的比例。分子是（重新调整的）密度估计得到平滑的值Z(y[i])协Z观察到的数据点y[i]。分母为密度估计的参考分布Z。
If method="reweight", then rho(z) is estimated by applying density estimation to the values Z(y[i]) of the covariate Z observed at the data points y[i], with weights inversely proportional to the reference density of Z.
如果method="reweight"，那么rho(z)估计密度估计的值Z(y[i])协Z观察到的数据点y[i]，重量成反比到的参考密度Z。
If method="transform", the smoothing method is variable-bandwidth kernel smoothing, implemented by applying the Probability Integral Transform to the covariate values, yielding values in the range 0 to 1, then applying edge-corrected density estimation on the interval [0,1], and back-transforming.
如果method="transform"，平滑的方法是可变带宽的内核平滑，实施，应用概率积分变换的协变量值，屈服值，范围在0到1，然后将边缘修正后的密度估计的时间间隔[0,1]，然后返回转化。
值----------Value----------
A function value table (object of class "fv") containing the estimated values of rho for a sequence of values of Z. Also belongs to the class "rhohat" which has special methods for print, plot and predict.
函数值表（类的对象"fv"）的估计值rho为序列值Z。也属于类"rhohat"具有特殊的方法print，plot和predict。
分类和离散协变量----------Categorical and discrete covariates----------
This technique assumes that the covariate has continuous values. It is not applicable to covariates with categorical (factor) values or discrete values such as small integers. For a categorical covariate, use quadratcount(X, tess=covariate)
这种技术假设协变量有连续的值。它是不适用的协变量分类（因子）的值或离散值，如小整数。对于分类协变量，使用quadratcount(X, tess=covariate)
（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----------
Nonparametric estimation of the dependence of a point process on spatial covariates. Statistics and Its Interface 5 (2), 221–236.
Modelling spatial point patterns in R. In: A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan, editors, Case Studies in Spatial Point Pattern Modelling, Lecture Notes in Statistics number 185. Pages 23–74. Springer-Verlag, New York, 2006. ISBN: 0-387-28311-0.
参见----------See Also----------
rho2hat, methods.rhohat
rho2hat，methods.rhohat
实例----------Examples----------
X <- rpoispp(function(x,y){exp(3+3*x)})
rho <- rhohat(X, "x")
rho <- rhohat(X, function(x,y){x})
plot(rho)
curve(exp(3+3*x), lty=3, col=2, add=TRUE)
rhoB <- rhohat(X, "x", method="reweight")
rhoC <- rhohat(X, "x", method="transform")
fit <- ppm(X, ~x)
rr <- rhohat(fit, "y")
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。
|
发表于 2012-9-30 14:06:34
