Impulse response analysis is especially useful when studying emotion-regulation, e.g., when a threat occurs, a person’s physiological system and subjective experience might respond in a concordant manner, so to produce a substantial increase of physiological arousal or recognition of stress to prepare the person to fight or flight from the threat. This is achieved by a technique called impulse response analysis (Lutkepohl, 2007). One way to understand the network is to examine how far an external perturbation can derail the system. The interconnectedness leads to the depiction of these dynamical systems as networks. The dynamical system methods (e.g., vector autoregression models) are applied to estimate these temporal relations between variables. When using dynamical system methods to analyze intensive longitudinal data (e.g., ecological momentary assessment data, physiological data), we often conceptulize the multivariate system as an interconnected organism, where each variable at time t might affect itself or other variables at t+1. iRAM is built upon a sequential method that used dynamical system methods and impulse response analysis to model individuals differences in the system dynamics. In this tutorial, we introduced a novel network-based metric, impulse response analysis matrix (iRAM), to extract information from the network. Network analysis has been increasingly adopted to analyze intensive longitudinal data.
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