Lcross.inhom(spatstat)
Lcross.inhom()所属R语言包：spatstat
Inhomogeneous Cross Type L Function
非均匀交叉型L功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For a multitype point pattern, estimate the inhomogeneous version of the cross-type L function.
对于多类型的点模式，估计非齐次版本的十字型L函数。
用法----------Usage----------
Lcross.inhom(X, i, j, ...)
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of the inhomogeneous cross type L function Lij(r) will be computed. It must be a multitype point pattern (a marked point pattern whose marks are a factor). See under Details.
所观察到的点图案，从其中的估计的非均匀交叉型L函数Lij(r)将被计算。它必须是一个多类型的点模式（一个标记点图案的标记是一个因素）。请参阅“详细信息”下。
参数：i
The type (mark value) of the points in X from which distances are measured. A character string (or something that will be converted to a character string). Defaults to the first level of marks(X).
X距离的测量点的类型（标记值）。一个字符串（或东西都将被转换为一个字符串）。默认的第一级marks(X)。
参数：j
The type (mark value) of the points in X to which distances are measured. A character string (or something that will be converted to a character string). Defaults to the second level of marks(X).
距离的测量点X的类型（标记值）。一个字符串（或东西都将被转换为一个字符串）。默认的第二个层次的marks(X)。
参数：...
Other arguments passed to Kcross.inhom.
其他参数传递给Kcross.inhom。
Details
详细信息----------Details----------
This is a generalisation of the function Lcross to include an adjustment for spatially inhomogeneous intensity, in a manner similar to the function Linhom.
这是一个一般化的功能Lcross包括空间非均匀的强度调整，以类似的方式，在给函数Linhom。
All the arguments are passed to Kcross.inhom, which estimates the inhomogeneous multitype K function Kij(r) for the point pattern. The resulting values are then transformed by taking L(r) = sqrt(K(r)/pi).
所有的参数被传递给Kcross.inhom，估计的非均匀多类型的K函数Kij(r)点模式。生成的值，然后转化，通过采取L(r) = sqrt(K(r)/pi)。
值----------Value----------
An object of class "fv" (see fv.object).
类的一个对象"fv"（见fv.object）。
Essentially a data frame containing numeric columns
本质上是一个数据框包含数字的列
参数：r
the values of the argument r at which the function Lij(r) has been estimated
的参数的值的r在哪些函数Lij(r)已估计
参数：theo
the theoretical value of Lij(r) for a marked Poisson process, identically equal to r
的理论值Lij(r)显着的泊松过程，恒等于r
together with a column or columns named "border", "bord.modif", "iso" and/or "trans", according to the selected edge corrections. These columns contain estimates of the function Lij(r) obtained by the edge corrections named.
连同一列或多列名为"border"，"bord.modif"，"iso"和/或"trans"，根据选定的边修正。这些列包含的功能Lij(r)命名的边缘修正的估计。
警告----------Warnings----------
The arguments i and j are always interpreted as levels of the factor X$marks. They are converted to character strings if they are not already character strings. The value i=1 does not refer to the first level of the factor.
的参数i和j总是被解释为水平的因素X$marks。它们被转换为字符串，如果他们不已经字符串。值i=1不是指第一级的因素。
（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>
参考文献----------References----------
Statistical Inference and Simulation for Spatial Point Processes Chapman and Hall/CRC Boca Raton, 2003.
参见----------See Also----------
Lcross, Linhom, Kcross.inhom
Lcross，Linhom，Kcross.inhom
实例----------Examples----------
# Lansing Woods data[蓝星伍兹数据]
data(lansing)
lansing <- lansing[seq(1,lansing$n, by=10)]
ma <- split(lansing)$maple
wh <- split(lansing)$whiteoak
# method (1): estimate intensities by nonparametric smoothing[方法（1）：平滑非参数估计强度]
lambdaM <- density.ppp(ma, sigma=0.15, at="points")
lambdaW <- density.ppp(wh, sigma=0.15, at="points")
L <- Lcross.inhom(lansing, "whiteoak", "maple", lambdaW, lambdaM)
# method (2): fit parametric intensity model[方法（2）：合适的参数强度模型]
fit <- ppm(lansing, ~marks * polynom(x,y,2))
# evaluate fitted intensities at data points[评估合身的强度，数据点]
# (these are the intensities of the sub-processes of each type)[（这些每种类型的子流程的强度）]
inten <- fitted(fit, dataonly=TRUE)
# split according to types of points[分裂根据类型的点]
lambda <- split(inten, lansing$marks)
L <- Lcross.inhom(lansing, "whiteoak", "maple",
lambda$whiteoak, lambda$maple)
# synthetic example: type A points have intensity 50,[合成例如：键入一个点的强度为50，]
# type B points have intensity 100 * x[B型点的强度为100 * X]
lamB <- as.im(function(x,y){50 + 100 * x}, owin())
X <- superimpose(A=runifpoispp(50), B=rpoispp(lamB))
L <- Lcross.inhom(X, "A", "B",
lambdaI=as.im(50, X$window), lambdaJ=lamB)
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 13:41:13
