cut.ppp(spatstat)
cut.ppp()所属R语言包：spatstat
Classify Points in a Point Pattern
点一个点模式分类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Classifies the points in a point pattern into distinct types according to the numerical marks in the pattern, or according to another variable.
分类成不同的类型的点图案中的点根据在模式中的数值标记，或根据另一个变量。
用法----------Usage----------
## S3 method for class 'ppp'
cut(x, z=marks(x), ...)
参数----------Arguments----------
参数：x
A two-dimensional point pattern. An object of class "ppp".
一个二维点模式。对象的类"ppp"。
参数：z
Data determining the classification. A numeric vector, a factor, a pixel image, a tessellation, or a string giving the name of a column of marks.
数据确定的分类。一个数值向量的一个因素，像素的图像，镶嵌，或一个字符串，给出一个列标记的名称。
参数：...
Arguments passed to cut.default. They determine the breakpoints for the mapping from numerical values in z to factor values in the output. See cut.default.
传递参数到cut.default。他们确定断点z因素值在输出数值的映射。见cut.default。
Details
详细信息----------Details----------
This function has the effect of classifying each point in the point pattern x into one of several possible types. The classification is based on the dataset z, which may be either
此函数具有的分类中的每个点的点图案x到几种可能的类型之一的效果。分类的依据是在数据集上z，它可以是任一
a factor (of length equal to the number of points in z) determining the classification of each point in x. Levels of the factor determine the classification.
的一个因素（长度等于在z）确定的分类x中的每个点的点的数量。因子的水平确定的分类。
a numeric vector (of length equal to the number of points in z). The range of values of z will be divided into bands (the number of bands is determined by ...) and z will be converted to a factor using cut.default.
一个数值向量（长度等于到的点的数量在z）。 z值的范围将分为带（带的数量是由...）和z将被转换到使用cut.default的一个因素。
a pixel image (object of class "im"). The value of z at each point of x will be used as the classifying variable.
像素的图像（类的对象"im"“）。 z在每个点x将用作分级变量的值。
a tessellation (object of class "tess", see tess). Each point of x will be classified according to the tile of the tessellation into which it falls.
一个Tessellation（曲面细分）（对象类"tess"，看到tess“）。每个点的x将根据它下降到镶嵌瓷砖的分类。
a character string, giving the name of one of the columns of marks(x), if this is a data frame.
一个字符串，的名称的列marks(x)之一，如果这是一个数据框。
The default is to take z to be the vector of marks in x (or the first column in the data frame of marks of x, if it is a data frame). If the marks are numeric, then the range of values of the numerical marks is divided into several intervals, and each interval is associated with a level of a factor. The result is a marked point pattern, with the same window and point locations as x, but with the numeric mark of each point discretised by replacing it by the factor level. This is a convenient way to transform a marked point pattern which has numeric marks into a multitype point pattern, for example to plot it or analyse it. See the examples.
默认情况下是采取z是商标的向量x（或标记的数据框中的第一列x，如果它是一个数据框）。如果标记是数字的，那么数值标记的值的范围内被分成多个间隔，和每个时间间隔相关联的电平的一个因素。其结果是一个显着的点图案，具有相同的窗口，并x点位置，但与每个点的离散化由因子水平通过替换它的数字标记。这是一个方便的方式改造成多类型的点模式，例如绘制或分析有数字的标记，一个标记点模式。见的例子。
To select some points from a point pattern, use the subset operator [.ppp instead.
要选择一些点，从点模式的子集，使用操作符[.ppp。
值----------Value----------
A multitype point pattern, that is, a point pattern object (of class "ppp") with a marks vector that is a factor.
一个多类型的模式，也就是一个点模式对象（类"ppp"）marks向量，这是一个因素。
（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>
参见----------See Also----------
cut, ppp.object, tess
cut，ppp.object，tess
实例----------Examples----------
# (1) cutting based on numeric marks of point pattern[（1）切割的基础上数字标记点格局]
data(longleaf)
# Longleaf Pines data[长叶松树数据]
# the marks are positive real numbers indicating tree diameters.[的商标是正实数，表示树的直径。]
## Not run: [＃不运行：]
plot(longleaf)
## End(Not run)[＃（不执行）]
# cut the range of tree diameters into three intervals[削减范围内的树直径分为三个区间]
long3 <- cut(longleaf, breaks=3)
## Not run: [＃不运行：]
plot(long3)
## End(Not run)[＃（不执行）]
# adult trees defined to have diameter at least 30 cm[定义为具有直径至少为30厘米的成年树]
long2 <- cut(longleaf, breaks=c(0,30,100), labels=c("Sapling", "Adult"))
plot(long2)
plot(long2, cols=c("green","blue"))
# (2) cutting based on another numeric vector[（2）切割的基础上另一个数字矢量]
# Divide Swedish Pines data into 3 classes[分割瑞典松树数据分为3类]
# according to nearest neighbour distance[根据最近邻距离]
data(swedishpines)
plot(cut(swedishpines, nndist(swedishpines), breaks=3))
# (3) cutting based on tessellation[（3）切割细分]
# Divide Swedish Pines study region into a 4 x 4 grid of rectangles[瑞典松树研究区域分割成4×4的矩形网格]
# and classify points accordingly[和分类分]
tes <- tess(xgrid=seq(0,96,length=5),ygrid=seq(0,100,length=5))
plot(cut(swedishpines, tes))
plot(tes, lty=2, add=TRUE)
# (4) multivariate marks[（4）多元商标]
data(finpines)
cut(finpines, "height", breaks=4)
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。
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发表于 2012-9-30 13:20:55
