Power Law适用于PoweRlaw包的截止分配

本文介绍了Power Law适用于PoweRlaw包的截止分配的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！问题描述我目前正试图找到一种方法来计算MLE截断分布的幂律拟合。分发看起来如下： p> 正如您所看到的，我可以分别拟合整个分布（幂律拟合）和下限（exp-fit）。我无法弄清楚的是如何拟合分布的上限（fe 8 有什么办法可以做这与 poweRlaw 包或任何其他R包？我希望看到的东西是这样的（注意：它只是一个随机分布）：代码p $ lt; code>＃Power-Law library（poweRlaw） xmin1 xmin2 plf0 plf0 $ setXmin（xmin1） plf0_pars< - estimate_pars（plf0） plf0 $ setPars（plf0_pars） #Exponential exp1< - disexp $ new（deg） exp1 $ setXmin（xmin2） exp1_pars< - estimated_pars（exp1） exp1 $ setPars（exp1_pars） plot（plf0） lines（plf0，col =green） lines（exp1，col =red）解决方案以下解决方案提供了可以使用Python运行的R代码： rpy2 。它通过 Kelvin的回答所建议的那样，主要来自： a href =https://github.com/jeffalstott/powerlaw/blob/master/testing/pli-R-v0.0.3-2007-07-25/powerexp.R =nofollow noreferrer> https：/ /github.com/jeffalstott/powerlaw/blob/master/testing/pli-R-v0.0.3-2007-07-25/powerexp.R 档案。安装 Gnu科学图书馆（GSL） $ b 在Debian中： apt-get install libgl2 li>从github下载 powerlaw python库代码。解压缩并构建 exponential-integral.tgz 位于 testing / pli-R-v0.0.3-2007-07-25 / 以前下载的库的文件夹。 tar xzf exponential-integral.tgz cd ex $ p $ / code> 移动 exp_int 文件复制到一个可执行路径，从这里开始，命名为 yourexecutablepath 。 mv exp_int yourexecutablepath 修改 exp_int_function_filename powerexp.R文件，包含exp_int可执行文件的完整路径，即 yourexecutablepath / exp_int 。 eval exp.R ， pareto.R 和 powerexp.R R源代码包含了所有需要的功能。 $ b data< - list（5，3，...） powerexp.fit（unlist（data））最后一个命令的输出提供了几个输出值， code> exponent ，它给出了幂定律 alpha 值和 rate ，它给出了expon ential截止参数。 I am currently trying to find a way to calculate a power-law fit for a cut-off distribution with MLE. The distribution looks as follows:As you can see, I was able to fit the whole distribution (Power-law fit) and also the lower bound (exp-fit) separately. What I am failing to figure out, is how to fit the upper bound of the distribution (f.e. 8 < x < 100).Is there any way to do this with the poweRlaw package or any other R package? What I am hoping for is something looking like this (note: it is just a random distribution):Code (if necessary):#Power-Lawlibrary("poweRlaw")xmin1 <- 8xmin2 <- 100plf0 <- displ$new(deg)plf0$setXmin(xmin1)plf0_pars <- estimate_pars(plf0)plf0$setPars(plf0_pars)#Exponentialexp1 <- disexp$new(deg)exp1$setXmin(xmin2)exp1_pars <- estimate_pars(exp1)exp1$setPars(exp1_pars)plot(plf0)lines(plf0, col="green")lines(exp1, col="red") 解决方案 Following solution provides R code that can be run with Python using rpy2.It provides instructions based in the source code by powerlaw library, as suggested by the answer by Kelvin, mainly from: https://github.com/jeffalstott/powerlaw/blob/master/testing/pli-R-v0.0.3-2007-07-25/powerexp.R file.Install Gnu Scientific Library (GSL)In Debian: apt-get install libgl2Download powerlaw python library code from github.unpack and build exponential-integral.tgz file located at testing/pli-R-v0.0.3-2007-07-25/ folder of the previously downloaded library.tar xzf exponential-integral.tgzcd exponential-integralmakeMove exp_int file to an executable path, named yourexecutablepath from here on.mv exp_int yourexecutablepathModify exp_int_function_filename variable from powerexp.R file with the full path to exp_int executable, i.e. yourexecutablepath/exp_int.eval exp.R, pareto.R and powerexp.R R source code to include all the needed functions.Fit your data to a powerlaw with exponential cut-off evaluating the following R call:data <- list(5, 3, ...)powerexp.fit(unlist(data))The output of the last command provides several output values, among them exponent which gives the power law alpha value and rate which gives the exponential cut-off parameter. 这篇关于Power Law适用于PoweRlaw包的截止分配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！