论文标题
部分可观测时空混沌系统的无模型预测
Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package
论文作者
论文摘要
内核Smoother是数据分析的重要工具，因为它们能够以简洁的图形可视化传达复杂的统计信息。它们包含在基本分布中以及R统计分析环境的许多用户基础的附加软件包中都非常适合许多从业人员。尽管对于专业数据仍然存在一些重要差距，但最值得注意的是整理和地理空间数据。提议的EKS软件包填补了这些空白。除了内核密度估计外，该软件包还可以满足更复杂的数据分析情况，例如密度衍生估计，基于密度的分类（监督学习）和平均偏移聚类（无监督学习）。我们用实验数据说明了如何获得和解释这些内核平滑方法的统计可视化。
Kernel smoothers are essential tools for data analysis due to their ability to convey complex statistical information with concise graphical visualisations. Their inclusion in the base distribution and in the many user-contributed add-on packages of the R statistical analysis environment caters well to many practitioners. Though there remain some important gaps for specialised data, most notably for tidy and geospatial data. The proposed eks package fills in these gaps. In addition to kernel density estimation, this package also caters for more complex data analysis situations, such as density derivative estimation, density-based classification (supervised learning) and mean shift clustering (unsupervised learning). We illustrate with experimental data how to obtain and to interpret the statistical visualisations for these kernel smoothing methods.
