A common challenge in data analysis is uncovering relationships between predictors and responses in problems involving large numbers of both. When the number of predictors and responses is limited, visual approaches are particularly effective. We present an R package, pandemonium, designed to explore such problems by combining cluster analysis with linked visualisations. Clustering is performed in one set of variables to identify regions with similar patterns in that space. The resulting clusters are simultaneously visualised in both spaces using linked views based on non-linear dimension reduction and animated tours. We introduce the package through two examples that illustrate different types of linked spaces. In the first example, we consider how a set of input variables is mapped to latent activations in a neural network regression model, to identify input combinations that result in similar activation patterns. In the second example, we analyse a complex multivariable mathematical model arising in physics to investigate how structure in the predictor space relates to the responses.

翻译：数据分析中的一个常见挑战是，在涉及大量预测变量和响应变量的问题中揭示二者之间的关系。当预测变量和响应变量的数量有限时，可视化方法尤为有效。我们介绍一个R语言包pandemonium，旨在通过结合聚类分析与链接可视化来探索此类问题。聚类在变量集中的某一组上进行，以识别该空间中具有相似模式的区域。随后，基于非线性降维和动画漫游的链接视图，将所得聚类同时在两个空间中可视化。我们通过两个例子介绍该包，这两个例子展示了不同类型的链接空间。在第一个例子中，我们考虑一组输入变量如何映射到神经网络回归模型中的潜在激活值，以识别产生相似激活模式的输入组合。在第二个例子中，我们分析一个源于物理学的复杂多变量数学模型，以探究预测变量空间中的结构如何与响应变量相关联。