ippm(spatstat)
ippm()所属R语言包：spatstat
Optimise Irregular Trend Parameters in Point Process Model
最佳化的不规则走势图中的参数点过程模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Experimental extension to ppm. Find optimal values of the irregular trend parameters in a point process model using Fisher scoring algorithm.
实验扩展到ppm。在一个点过程模型，采用Fisher得分算法的不规则趋势参数的最佳值。
用法----------Usage----------
ippm(..., iScore=NULL,
start=list(),
covfunargs=start,
maxiter=20, tol=1e-4, progress=TRUE, stepfactor=1,
dbug=FALSE)
参数----------Arguments----------
参数：...
Arguments passed to ppm to fit the point process model.
参数传递给ppm，以适应点过程模型。
参数：iScore
A named list of R functions that compute the partial derivatives of logf with respect to each irregular parameter. See Details.
命名列表，计算每个不规则参数的偏导数的logfR功能。查看详细信息。
参数：start
Named list containing initial values of the irregular parameters over which to optimise.
命名列表，其中包含的不规则参数，以优化的初始值。
参数：covfunargs
Argument passed to ppm. A named list containing values for all irregular parameters required by the covariates in the model. Must include all the parameters named in start.
参数传递到ppm。命名的列表，其中包含的协变量在模型中所要求的所有不规则的参数值。必须包括命名start的所有参数。
参数：maxiter
Integer. Maximum number of iterations of Fisher scoring algorithm.
整数。 Fisher评分算法的迭代的最大数量。
参数：tol
Numeric value or vector. The algorithm stops when the difference between two successive estimates of the irregular parameter is less than tol.
数值或向量。该算法停止当不规则的参数之间的差，两个连续的估计是小于tol时。
参数：progress
Logical. Whether to print progress reports.
逻辑。无论是打印进度报告。
参数：stepfactor
Numeric value between 0 and 1 indicating that the change in the parameter between successive iterations is only a specified fraction of the step computed by the Newton-Raphson algorithm.
在0和1之间的数值表示中的变化之间的参数连续迭代Newton-Raphson算法所计算的步骤仅仅是一个指定的分数。
参数：dbug
Logical. Whether to print debugging output.
逻辑。是否打印调试输出。
Details
详细信息----------Details----------
This function is an experimental extension to the point process model fitting command ppm. The extension allows the trend of the model to include irregular parameters, which will be maximised by a Fisher scoring method.
这是一个实验性的扩展功能点过程模型拟合命令ppm。扩展允许包括不规则的模型参数，这将是最大化的Fisher评分方法的趋势。
For the sake of explanation, consider a Poisson point process with intensity function lambda(u) at location u. Assume that
为了便于解释，考虑Poisson点过程强度功能lambda(u)在位置u。假设
where alpha,beta,gamma are parameters to be estimated, Z(u) is a spatial covariate function, and f is some known function. Then the parameters alpha,beta are called regular because they appear in a loglinear form; the parameter gamma is called irregular.
alpha,beta,gamma是待估参数，Z(u)是一个空间的协功能，并f是一些已知的功能。然后参数alpha,beta被称为定期的，因为他们在对数线性形式出现的参数gamma被称为不规则。
To fit this model using ippm, we specify the intensity using the trend formula in the same way as usual for ppm. The trend formula is a representation of the log intensity. In the above example the log intensity is
要符合这个模型使用ippm，我们指定的强度，使用像往常一样以同样的方式trendppm公式。的趋势公式是表示log强度。另外，在上述的例子中的log强度是
So the model above would be encoded with the trend formula ~Z + offset(log(f)). Note that the irregular part of the model is an offset term, which means that it is included in the log trend as it is, without being multiplied by another regular parameter.
因此，上述模型进行编码的趋势方程式~Z + offset(log(f))。请注意，不规则的模型的一部分是一个偏移量的术语，这意味着它是包括在log中的趋势，因为它是不被乘以另一个正则参数。
To perform Fisher scoring we also need the derivative of log(f(u,gamma)) with respect to gamma. We call this the irregular score. The user must write an R function that computes the irregular score for any value of gamma at any location (x,y).
要执行费舍尔得分，我们还需要衍生工具的log(f(u,gamma))就gamma。我们把这种不规则的得分。用户必须写一个R函数计算不规则的得分为任何在任何位置gamma值(x,y)。
Thus, to code such a problem,
因此，进行编码这样的问题，
The argument trend should define the log intensity, with the irregular part as an offset;
参数trend定义log强度，具有不规则的偏移量;
The argument start should be a list containing initial values of each of the irregular parameters;
参数start应该是一个列表，其中包含每个不规则的参数的初始值;
The argument iScore must be a list (with one entry for each entry of start) of functions with arguments x,y,..., that evaluate the partial derivatives of log(f(u,gamma)) with respect to each irregular parameter.
参数iScore必须是一个列表（用一个条目，每个条目start）功能参数x,y,...，即相对于每个评估的偏导数log(f(u,gamma))不规则的参数。
The coded example below illustrates the model with two irregular parameters gamma,delta and irregular term
编码下面的例子说明了该模型的两个不规则的参数gamma,delta和不规则的术语
Arguments ... passed to ppm may also include interaction. In this case the model is not a Poisson point process but a more general Gibbs point process; the trend formula trend determines the first-order trend of the model (the first order component of the conditional intensity), not the intensity.
参数...传递ppm的，也可能包含了interaction。在这种情况下，模式是不是一个泊松点的过程，但更一般的吉布斯点过程;趋势公式trend决定的模型（有条件强度的第一阶分量）的第一阶的趋势，而不是强度。
值----------Value----------
A fitted point process model (object of class "ppm").
已安装点过程模型（对象类"ppm"“）。
（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----------
ppm
ppm
实例----------Examples----------
nd <- 32
gamma0 <- 3
delta0 <- 5
POW <- 3
# Terms in intensity[在强度条款]
Z <- function(x,y) { -2*y }
f <- function(x,y,gamma,delta) { 1 + exp(gamma - delta * x^POW) }
# True intensity[真正的强度]
lamb <- function(x,y,gamma,delta) { 200 * exp(Z(x,y)) * f(x,y,gamma,delta) }
# Simulate realisation[模拟实现]
lmax <- max(lamb(0,0,gamma0,delta0), lamb(1,1,gamma0,delta0))
set.seed(42)
X <- rpoispp(lamb, lmax=lmax, win=owin(), gamma=gamma0, delta=delta0)
# Partial derivatives of log f[偏导数的logf]
DlogfDgamma <- function(x,y, gamma, delta) {
topbit <- exp(gamma - delta * x^POW)
topbit/(1 + topbit)
}
DlogfDdelta <- function(x,y, gamma, delta) {
topbit <- exp(gamma - delta * x^POW)
- (x^POW) * topbit/(1 + topbit)
}
# irregular score[不规则得分]
Dlogf <- list(gamma=DlogfDgamma, delta=DlogfDdelta)
# fit model[拟合模型]
ippm(X, ~Z + offset(log(f)),
covariates=list(Z=Z, f=f),
iScore=Dlogf,
start=list(gamma=1, delta=1),
tol=0.01, nd=nd)
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 13:35:57
