R中节点属性与网络连边的联合多重插补
Joint Multiple Imputation of Node Attributes and Network Ties in R
AI总结：
本文介绍R包netimpute，其通过链式方程例程netmice()联合插补社会网络中缺失的节点属性与网络连边，支持多种网络类型及相关高级功能。
AI中文摘要：
社会网络研究中的缺失数据通常同时影响节点级属性和网络连边本身。标准多重插补软件如mice（van Buuren和Groothuis-Oudshoorn，2011）能很好地处理前者，但不具备网络结构的概念；而专用的网络插补程序通常将连边插补和属性插补视为独立问题。本文介绍netimpute，一个R包，它：（a）在一个或多个网络上计算大量节点级结构和属性同质性度量；（b）可将这些度量简化为一组易于处理的主成分预测因子，同时可选择保留本身属于实质性假设的特定原始度量；（c）提供遵循MR-QAP（Krackhardt，1988）思路的网络连边的二元（单元格级）回归；（d）将两者结合为netmice()，一种链式方程例程，通过在节点属性和网络连边之间循环，使用网络衍生的预测因子预测属性、属性衍生（及其他网络衍生）的预测因子预测连边，来联合插补缺失的节点属性和缺失的网络连边。网络连边默认按连边更新：序贯吉布斯步骤每次重绘一个缺失连边，条件是所有先前插补的连边，每次绘制后通过变化统计量更新内生统计量（互惠性、共同接触）。该包还支持结构零、网络间的逻辑约束、连边模型中的社会关系模型随机截距、包含任何内部创建项之间交互作用的自定义插补模型，以及面向目标的网络感知预测因子选择（netquickpred()）。我们描述该包的设计选择、其为二元和非负加权网络计算的度量，并通过一个可复现示例说明其使用方法。
英文摘要：
Missing data in social network studies routinely affects both node-level attributes and the network ties themselves. Standard multiple imputation software such as mice (van Buuren and Groothuis-Oudshoorn, 2011) handles the former well but has no notion of network structure, while dedicated network-imputation procedures typically treat tie imputation and attribute imputation as separate problems. This paper introduces netimpute, an R package that (a) computes a broad battery of node-level structural and attribute homophily measures across one or more networks, (b) can reduce these to a manageable set of principal component predictors while optionally preserving specific raw measures that are themselves part of substantive hypotheses, (c) provides a dyadic (cell-level) regression for network ties in the spirit of MR-QAP (Krackhardt, 1988), and (d) combines both into netmice(), a chained-equations routine that jointly imputes missing node attributes and missing network ties by cycling between them, using network-derived predictors for attributes and attribute-derived (and other-network-derived) predictors for ties. Network ties are updated tie-wise by default: a sequential Gibbs step redraws each missing tie one at a time, conditional on all previously imputed ties, refreshing the endogenous statistics (reciprocity, shared contacts) after every single draw via change statistics. The package additionally supports structural zeros, logical constraints between networks, social-relations-model random intercepts in the tie model, custom imputation models with interactions among any internally created terms, and network-aware per-target predictor selection (netquickpred()). We describe the package's design choices, the measures it computes for binary and non-negative weighted networks, and illustrate its use with a reproducible example
