slrm(spatstat)
slrm()所属R语言包：spatstat
Spatial Logistic Regression
空间Logistic回归
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a spatial logistic regression model to a spatial point pattern.
适合空间Logistic回归模型一个空间点格局。
用法----------Usage----------
slrm(formula, ..., data = NULL, offset = TRUE, link = "logit",
dataAtPoints=NULL, splitby=NULL)
参数----------Arguments----------
参数：formula
The model formula. See Details.
模型公式。查看详细信息。
参数：...
Optional arguments passed to pixellate determining the pixel resolution for the discretisation of the point pattern.
可选参数传递给pixellate确定像素的分辨率，离散的点模式。
参数：data
Optional. A list containing data required in the formula. The names of entries in the list should correspond to variable names in the formula. The entries should be point patterns, pixel images or windows.
可选。公式中的一个列表，其中包含所需的数据。在列表中的条目的名称应该对应于在公式中的变量名。参赛作品必须是点模式，像素的图像或窗口。
参数：offset
Logical flag indicating whether the model formula should be augmented by an offset equal to the logarithm of the pixel area.
逻辑标志指示是否应该增加的模型公式，由偏移的像素区域的对数相等。
参数：link
The link function for the regression model. A character string, specifying a link function for binary regression.
链接功能的回归模型。一个字符串，指定链接功能，二元回归。
参数：dataAtPoints
Optional. Exact values of the covariates at the data points. A data frame, with column names corresponding to variables in the formula, with one row for each point in the point pattern dataset.
可选。精确值的协变量的数据点。一个数据框，对应的变量在formula，有一排点模式数据集的每个点的列名。
参数：splitby
Optional. Character string identifying a window. The window will be used to split pixels into sub-pixels.
可选。字符串识别的一个窗口。窗口将被用来分割成的子像素的像素。
Details
详细信息----------Details----------
This function fits a Spatial Logistic Regression model (Tukey, 1972; Agterberg, 1974) to a spatial point pattern dataset. The logistic function may be replaced by another link function.
此功能适用空间Logistic回归模型（杜克，1972年Agterberg，1974年），一个空间点格局数据集。MF功能可能被替换为另一个链接功能。
The formula specifies the form of the model to be fitted, and the data to which it should be fitted. The formula must be an R formula with a left and right hand side.
formula指定以嵌合的模型的形式，并且它应嵌合的数据。 formula必须是用左手和右手侧的R公式。
The left hand side of the formula is the name of the point pattern dataset, an object of class "ppp".
的左手侧的formula是点图案数据集的名称，一个对象的类"ppp"。
The right hand side of the formula is an expression, in the usual R formula syntax, representing the functional form of the linear predictor for the model.
右手边的formula是一个表达式，在平时的ŕ公式语法，代表的线性预测模型的函数形式。
Each variable name that appears in the formula may be
出现在公式中每个变量的名称可能是
one of the reserved names x and y, referring to the Cartesian coordinates;
一个保留名称x和y，指的是在直角坐标系;
the name of an entry in the list data, if this argument is given;
的名称列表中的条目data，如果这种说法是;
the name of an object in the parent environment, that is, in the environment where the call to slrm was issued.
父环境中的对象的名称，即，在环境中的呼叫到slrm发出。
Each object appearing on the right hand side of the formula may be
下式的右手侧上出现的每一个对象可能是
a pixel image (object of class "im") containing the values of a covariate;
包含协变量的值像素（对象的类"im"），;
a window (object of class "owin"), which will be interpreted as a logical covariate which is TRUE inside the window and FALSE outside it;
一个窗口（对象的类"owin"），将被解释为一个逻辑的协变量，这是TRUE内的窗口和FALSE外面;
a function in the R language, with arguments x,y, which can be evaluated at any location to obtain the values of a covariate.
functionR语言中，带参数的x,y，它可以在任何位置，得到协变量的值进行评估。
See the Examples below.
请参阅下面的例子。
The fitting algorithm discretises the point pattern onto a pixel grid. The value in each pixel is 1 if there are any points of the point pattern in the pixel, and 0 if there are no points in the pixel. The dimensions of the pixel grid will be determined as follows:
的拟合的算法discretises模式上的像素网格点。在每个像素的值是1，如果有任何的点图案中的像素点，和0，如果没有在该像素点。的像素网格的尺寸将决定如下：
The pixel grid will be determined by the extra arguments ... if they are specified (for example the argument dimyx can be used to specify the number of pixels).
像素网格，将取决于额外的参数...“”如果他们（例如参数dimyx可用于指定的像素数）。
Otherwise, if the right hand side of the formula includes the names of any pixel images containing covariate values, these images will determine the pixel grid for the discretisation. The covariate image with the finest grid (the smallest pixels) will be used.
否则，如果右手侧的formula，包括姓名，含有协变量值的任何像素的图像，这些图像将确定为离散化的像素网格。最好的网格（最小的像素）的协变量的图像将被使用。
Otherwise, the default pixel grid size is given by spatstat.options("npixel").
否则，默认的像素网格的大小是由spatstat.options("npixel")。
If link="logit" (the default), the algorithm fits a Spatial Logistic Regression model. This model states that the probability p that a given pixel contains a data point, is related to the covariates through
如果link="logit"（默认值），该算法适合空间Logistic回归模型。概率p，一个给定的像素中包含的一个数据点，相关的协变量，通过该模型状态
where eta is the linear predictor of the model (a linear combination of the covariates, whose form is specified by the formula).
eta的线性预测模型（协变量的线性组合，其形式指定的formula）。
If link="cloglog" then the algorithm fits a model stating that
如果link="cloglog"然后算法适合的模型，说明
.
。
If offset=TRUE (the default), the model formula will be augmented by adding an offset term equal to the logarithm of the pixel area. This ensures that the fitted parameters are approximately independent of pixel size. If offset=FALSE, the offset is not included, and the traditional form of Spatial Logistic Regression is fitted.
如果offset=TRUE（默认值），模型公式将增强加一个偏移量的术语的像素面积的对数相等。这确保了拟合的参数是约独立的像素尺寸。如果offset=FALSE，偏移量是不包括在内，，和空间Logistic回归传统形式的安装。
值----------Value----------
An object of class "slrm" representing the fitted model.
对象的类"slrm"拟合模型。
There are many methods for this class, including methods for print, fitted, predict, anova, coef, logLik, terms, update, formula and vcov. Automated stepwise model selection is possible using step.
这个类的方法有很多，包括方法print，fitted，predict，anova，coef，logLik，<X >，terms，update和formula。逐步模型自动选择，可以使用vcov。
（作者）----------Author(s)----------
Adrian Baddeley
<a href="mailto:adrian@maths.uwa.edu.au">adrian@maths.uwa.edu.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----------
Automatic contouring of geological maps to detect target areas for mineral exploration. Journal of the International Association for Mathematical Geology 6, 373–395.
Schuhmacher, D., Shah, R. and Turner, R. (2010) Spatial logistic regression and change-of-support for spatial Poisson point processes. Electronic Journal of Statistics 4, 1151–1201. doi: 10.1214/10-EJS581
Discussion of paper by F.P. Agterberg and S.C. Robinson. Bulletin of the International Statistical Institute 44 (1) p. 596. Proceedings, 38th Congress, International Statistical Institute.
参见----------See Also----------
anova.slrm, coef.slrm, fitted.slrm, logLik.slrm, plot.slrm, predict.slrm
anova.slrm，coef.slrm，fitted.slrm，logLik.slrm，plot.slrm，predict.slrm
实例----------Examples----------
X <- copper$SouthPoints
slrm(X ~ 1)
slrm(X ~ x+y)
slrm(X ~ x+y, link="cloglog")
# specify a grid of 1 km square pixels[指定一个1平方公里的像素网格]
slrm(X ~ 1, eps=1)
Y <- copper$SouthLines
Z <- distmap(Y)
slrm(X ~ Z)
slrm(X ~ Z, dataAtPoints=list(Z=nncross(X,Y)$dist))
dat <- list(A=X, V=Z)
slrm(A ~ V, data=dat)
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 14:14:34
