smooth.ppp(spatstat)
smooth.ppp()所属R语言包：spatstat
Spatial smoothing of observations at irregular points
在不规则的空间平滑的观测点
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs spatial smoothing of numeric values observed at a set of irregular locations. Uses Gaussian kernel smoothing and least-squares cross-validated bandwidth selection.
在不规则的位置的一组观察到的数值进行空间平滑。使用高斯核平滑和最小二乘交叉验证的带宽选择。
用法----------Usage----------
smooth.ppp(X, ..., weights = rep(1, npoints(X)), at="pixels")
markmean(X, ...)
markvar(X, ...)
参数----------Arguments----------
参数：X
A marked point pattern (object of class "ppp").
一个显着的点模式（类的对象"ppp"）。
参数：...
Arguments passed to bw.smoothppp and density.ppp to control the kernel smoothing and the pixel resolution of the result.
参数传递到bw.smoothppp和density.ppp控制的内核平滑和像素分辨率的结果。
参数：weights
Optional weights attached to the observations.
可选的权重，来观测。
参数：at
String specifying whether to compute the smoothed values at a grid of pixel locations (at="pixels") or only at the points of X (at="points").
字符串，用于指定是否计算平滑值的像素位置的网格（at="pixels"）或仅在点X（at="points"）。
Details
详细信息----------Details----------
The function smooth.ppp performs spatial smoothing of numeric values observed at a set of irregular locations. The functions markmean and markvar are wrappers for smooth.ppp which compute the spatially-varying mean and variance of the marks of a point pattern.
的功能smooth.ppp在不规则的位置的一组观察到的数值进行空间平滑。的功能markmean和markvarsmooth.ppp计算空间上变化的均值和方差的点图案的标志的包装。
Smoothing is performed by Gaussian kernel weighting. If the observed values are v[1],...,v[n] at locations x[1],...,x[n] respectively, then the smoothed value at a location u is (ignoring edge corrections)
是由高斯核加权平滑。如果观测值是v[1],...,v[n]的地点x[1],...,x[n]分别，然后平滑值的位置u是（忽略边缘改正）
where k is a Gaussian kernel. This is known as the Nadaraya-Watson smoother (Nadaraya, 1964, 1989; Watson, 1964). By default, the smoothing kernel bandwidth is chosen by least squares cross-validation (see below).
k是一个高斯内核。这是被称为的Nadaraya-沃森更平滑（Nadaraya，1964年，1989年;沃森，1964）。默认情况下，平滑的内核带宽选择通过最小二乘法交叉验证（见下文）。
The argument X must be a marked point pattern (object of class "ppp", see ppp.object). The points of the pattern are taken to be the observation locations x[i], and the marks of the pattern are taken to be the numeric values v[i] observed at these locations.
参数X必须是显着的点模式（类的对象"ppp"，看到ppp.object“）。的图案的点取为观察位置x[i]，和标记的图案被以数值v[i]观察到在这些位置。
The marks are allowed to be a data frame (in smooth.ppp and markmean). Then the smoothing procedure is applied to each column of marks.
标记是一个数据框（smooth.ppp和markmean）。然后，被施加到每一列的标记的平滑过程。
The numerator and denominator are computed by density.ppp. The arguments ... control the smoothing kernel parameters and determine whether edge correction is applied. The smoothing kernel bandwidth can be specified by either of the arguments sigma or varcov which are passed to density.ppp. If neither of these arguments is present, then by default the bandwidth is selected by least squares cross-validation, using bw.smoothppp.
的分子和分母计算density.ppp。 ...控制平滑的内核参数，并确定是否应用于边缘校正的参数。平滑的核心带宽，可以指定任一参数sigma或varcov传递给density.ppp。如果这些参数都不存在，则默认情况下，带宽选择通过最小二乘法交叉验证，使用bw.smoothppp。
The optional argument weights allows numerical weights to be applied to the data. If a weight w[i] is associated with location x[i], then the smoothed function is (ignoring edge corrections)
可选参数weights可以被应用到的数据数值的权重。如果重量w[i]相关联的与位置x[i]，然后平滑的功能是（忽略边缘修正）
An alternative to kernel smoothing is inverse-distance weighting, which is performed by idw.
核平滑的另一种方法是反距离权重，这是由idw。
值----------Value----------
If X has a single column of marks:
如果X有一个单一的列标记：
If at="pixels" (the default), the result is a pixel image (object of class "im"). Pixel values are values of the interpolated function.
如果at="pixels"（默认值），结果是一个像素的图像（类的对象"im"“）。像素值的值的内插函数。
If at="points", the result is a numeric vector of length equal to the number of points in X. Entries are values of the interpolated function at the points of X.
如果at="points"，结果是一个数值向量的长度相等的点的数量在X。文章的插值函数在该点的X的值。
If X has a data frame of marks:
如果X有一个数据框的标记：
If at="pixels" (the default), the result is a named list of pixel images (object of class "im"). There is one image for each column of marks. This list also belongs to the class listof, for which there is a plot method.
如果at="pixels"（默认值），结果是一个名为像素的图像列表的（对象类"im"“）。有一个图像的每个列标记。这的列表也属于类listof，其中有一个图方法。
If at="points", the result is a data frame with one row for each point of X, and one column for each column of marks. Entries are values of the interpolated function at the points of X.
如果at="points"，结果是一个数据框，其中一行X，和一列的每个列的标记的每个点。文章的插值函数在该点的X的值。
The return value has attributes "sigma" and "varcov" which report the smoothing bandwidth that was used.
返回值的属性"sigma"和"varcov"报告时所使用的平滑带宽。
（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----------
Theory of Probability and its Applications 9, 141–142.
Nonparametric estimation of probability densities and regression curves. Kluwer, Dordrecht.
Smooth regression analysis. Sankhya A 26, 359–372.
参见----------See Also----------
density.ppp, bw.smoothppp, ppp.object, im.object.
density.ppp，bw.smoothppp，ppp.object，im.object。
See idw for inverse-distance weighted smoothing.
见idw反距离加权平滑的。
To perform interpolation, see also the akima package.
为了进行插值，也见akima包。
实例----------Examples----------
# Longleaf data - tree locations, marked by tree diameter[长叶的数据 - 树的地方，树的直径为标志]
data(longleaf)
# Local smoothing of tree diameter (automatic bandwidth selection)[局部平滑的树直径（自动带宽选择）]
Z <- smooth.ppp(longleaf)
# Kernel bandwidth sigma=5[核心带宽σ= 5]
plot(smooth.ppp(longleaf, 5))
# mark variance[马克方差]
plot(markvar(longleaf, sigma=5))
# data frame of marks: trees marked by diameter and height[数据框的标记：标记的直径和高度的树木]
data(finpines)
plot(smooth.ppp(finpines, sigma=2))
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 14:14:54
