ABSTRACT
A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user-friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R. Such an approach can easily require time-consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious, and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this approach permits simple retrieval of standard errors at the same time. We illustrate the performance of our routines using different data sets: first, two smaller samples from a mobile application for tinnitus patients and a well-known data set of fetal lamb movements with 87 and 240 data points, respectively. Second, we rely on larger data sets of simulated data of sizes 2000 and 5000 for further analysis. This tutorial is accompanied by a collection of scripts, which are all available in the Supporting Information. These scripts allow any user with moderate programming experience to benefit quickly from the computational advantages of TMB.
Subject(s)
Algorithms , Software , Animals , Confidence Intervals , Likelihood Functions , Markov Chains , SheepABSTRACT
Tinnitus, the perception of sound in the absence of a corresponding sound, and the distress caused by it, is rarely a static phenomenon. It rather fluctuates over time depending on endogenous and exogenous factors. The COVID-19 pandemic is a potential environmental stressor that might influence the individually perceived tinnitus distress. Since not all people are affected by the pandemic in the same way, the situation allows one to identify environmental factors and personality traits that impact tinnitus distress differently. In our study, 122 tinnitus patients were included at two time points: in the year 2018 and during the German lockdown in April 2020. We assessed tinnitus-related distress, depressive symptoms, personality characteristics and the individual perception of the pandemic situation. On average, there was only a small increase of tinnitus distress with heterogeneous changes during the lockdown. People perceiving the situation as generally stressful with increased grief, frustration, stress and nervousness reported the worsening of tinnitus distress. People with high values in neuroticism also reported the worsening of tinnitus distress, while the personality traits extraversion, conscientiousness and openness seemed to be a protection factor. The study identifies factors that influence tinnitus distress change during a pandemic and spots those patients that need specific help in the pandemic situation.
