Idiographic network models are estimated on time series data of a single individual and allow researchers to investigate person-specific associations between multiple variables over time. The most common approach for fitting graphical vector autoregressive (GVAR) models uses least absolute shrinkage and selection operator (LASSO) regularization to estimate a contemporaneous and a temporal network. However, estimation of idiographic networks can be unstable in relatively small data sets typical for psychological research. This bears the risk of misinterpreting differences in estimated networks as spurious heterogeneity between individuals. As a remedy, we evaluate the performance of a Bayesian alternative for fitting GVAR models that allows for regularization of parameters while accounting for estimation uncertainty. We also develop a novel test, implemented in the tsnet package in R, which assesses whether differences between estimated networks are reliable based on matrix norms. We first compare Bayesian and LASSO approaches across a range of conditions in a simulation study. Overall, LASSO estimation performs well, while a Bayesian GVAR without edge selection may perform better when the true network is dense. In an additional simulation study, the novel test is conservative and shows good false-positive rates. Finally, we apply Bayesian estimation and testing in an empirical example using daily data on clinical symptoms for 40 individuals. We additionally provide functionality to estimate Bayesian GVAR models in Stan within tsnet. Overall, Bayesian GVAR modeling facilitates the assessment of estimation uncertainty which is important for studying interindividual differences of intraindividual dynamics. In doing so, the novel test serves as a safeguard against premature conclusions of heterogeneity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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具体网络模型的贝叶斯估计和比较。


具体网络模型是根据单个个体的时间序列数据进行估计的，使研究人员能够调查多个变量之间随时间变化的特定于人的关联。拟合图形向量自回归 (GVAR) 模型的最常见方法使用最小绝对收缩和选择算子 (LASSO) 正则化来估计同期网络和时间网络。然而，在心理学研究中典型的相对较小的数据集中，具体网络的估计可能不稳定。这存在将估计网络中的差异误解为个体之间虚假异质性的风险。作为补救措施，我们评估了拟合 GVAR 模型的贝叶斯替代方案的性能，该模型允许参数正则化，同时考虑估计不确定性。我们还开发了一种新颖的测试，在 R 的 tsnet 包中实现，它根据矩阵范数评估估计网络之间的差异是否可靠。我们首先在模拟研究中的一系列条件下比较贝叶斯方法和 LASSO 方法。总体而言，LASSO 估计表现良好，而当真实网络密集时，没有边缘选择的贝叶斯 GVAR 可能表现更好。在另一项模拟研究中，新颖的测试是保守的，并且显示出良好的假阳性率。最后，我们在一个实证示例中应用贝叶斯估计和测试，使用 40 个人的临床症状的日常数据。我们还提供了在 tsnet 中的 Stan 中估计贝叶斯 GVAR 模型的功能。总体而言，贝叶斯 GVAR 模型有助于估计不确定性的评估，这对于研究个体内动力学的个体间差异非常重要。 在此过程中，新颖的测试可以防止过早得出异质性结论。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。