Subgrouping with Chain Graphical VAR Models

References

• Abegaz, F., & Wit, E. (2013). Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics, 14(3), 586–599. https://doi.org/10.1093/biostatistics/kxt005
   PubMed Web of Science ®Google Scholar
• Beck, E. D., & Jackson, J. J. (2020). Consistency and change in idiographic personality: A longitudinal ESM network study. Journal of Personality and Social Psychology, 118(5), 1080–1100. https://doi.org/10.1037/pspp0000249
   PubMed Web of Science ®Google Scholar
• Bogner, K., Pappenberger, F., & Cloke, H. L. (2012). The normal quantile transformation and its application in a flood forecasting system. Hydrology and Earth System Sciences, 16(4), 1085–1094. https://doi.org/10.5194/hess-16-1085-2012
   Web of Science ®Google Scholar
• Bolin, J. H., Edwards, J. M., Finch, W. H., & Cassady, J. C. (2014). Applications of cluster analysis to the creation of perfectionism profiles: A comparison of two clustering approaches. Frontiers in Psychology, 5, 343. https://doi.org/10.3389/fpsyg.2014.00343
   PubMed Web of Science ®Google Scholar
• Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
   PubMed Web of Science ®Google Scholar
• Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS One, 8(4), e60188. https://doi.org/10.1371/journal.pone.0060188
   PubMed Web of Science ®Google Scholar
• Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, 1540. https://doi.org/10.3389/fpsyg.2016.01540
   PubMed Web of Science ®Google Scholar
• Ceulemans, E., & Kiers, H. A. (2006). Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method. The British Journal of Mathematical and Statistical Psychology, 59(Pt 1), 133–150. https://doi.org/10.1348/000711005X64817
   PubMedGoogle Scholar
• Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759–771. https://doi.org/10.1093/biomet/asn034
   Web of Science ®Google Scholar
• Chow, S.-M., Losardo, D., Park, J. J., & Molenaar, P. C. (2021). Continuous-time dynamic models: Connections to structural equation models and other discrete-time models. Guilford.
   Google Scholar
• Demeshko, M., Washio, T., Kawahara, Y., & Pepyolyshev, Y. (2015). A novel continuous and structural var modeling approach and its application to reactor noise analysis. ACM Transactions on Intelligent Systems and Technology, 7(2), 1–22. https://doi.org/10.1145/2710025
   Web of Science ®Google Scholar
• De Vos, S., Wardenaar, K. J., Bos, E. H., Wit, E. C., Bouwmans, M. E., & De Jonge, P. (2017). An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks. PLoS One, 12(6), e0178586. https://doi.org/10.1371/journal.pone.0178586
   PubMed Web of Science ®Google Scholar
• Drakopoulos, G., Kanavos, A., Makris, C., & Megalooikonomou, V. (2016). On converting community detection algorithms for fuzzy graphs in neo4j. arXiv preprint arXiv:1608.02235.
   Google Scholar
• Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. https://doi.org/10.1037/met0000167
   PubMed Web of Science ®Google Scholar
• Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A.-M., Riese, H., & Cramer, A. O. (2018). Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections. Clinical Psychological Science: A Journal of the Association for Psychological Science, 6(3), 416–427. https://doi.org/10.1177/2167702617744325
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/10.1080/00273171.2018.1454823
   PubMed Web of Science ®Google Scholar
• Fisher, A. J. (2015). Toward a dynamic model of psychological assessment: Implications for personalized care. Journal of Consulting and Clinical Psychology, 83(4), 825–836. https://doi.org/10.1037/ccp0000026
   PubMed Web of Science ®Google Scholar
• Fisher, Z. F., Kim, Y., Fredrickson, B., & Pipiras, V. (2022). Penalized estimation and forecasting of multiple subject intensive longitudinal data. psychometrika, 87(2), 1–29.
   Google Scholar
• Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for gaussian graphical models. Advances in neural information processing systems, 23.
   Google Scholar
• Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432–441. https://doi.org/10.1093/biostatistics/kxm045
   PubMed Web of Science ®Google Scholar
• Gates, K. M., Henry, T., Steinley, D., & Fair, D. A. (2016). A Monte Carlo evaluation of weighted community detection algorithms. Frontiers in Neuroinformatics, 10, 45. https://doi.org/10.3389/fninf.2016.00045
   PubMed Web of Science ®Google Scholar
• Gates, K. M., Lane, S. T., Varangis, E., Giovanello, K., & Guskiewicz, K. (2017). Unsupervised classification during time-series model building. Multivariate Behavioral Research, 52(2), 129–148. https://doi.org/10.1080/00273171.2016.1256187
   PubMed Web of Science ®Google Scholar
• Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), 310–319. https://doi.org/10.1016/j.neuroimage.2012.06.026
   PubMed Web of Science ®Google Scholar
• Gates, K. M., Molenaar, P. C., Hillary, F. G., Ram, N., & Rovine, M. J. (2010). Automatic search for fMRI connectivity mapping: An alternative to granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. NeuroImage, 50(3), 1118–1125. https://doi.org/10.1016/j.neuroimage.2009.12.117
   PubMed Web of Science ®Google Scholar
• Gates, K. M., Molenaar, P. C., Iyer, S. P., Nigg, J. T., & Fair, D. A. (2014). Organizing heterogeneous samples using community detection of gimme-derived resting state functional networks. PLoS One, 9(3), e91322. https://doi.org/10.1371/journal.pone.0091322
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L. (2004). Time series analysis and the individual as the unit of psychological research Universiteit van Amsterdam.
   Google Scholar
• Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. (2005). Statistical modeling of the individual: Rationale and application of multivariate stationary time series analysis. Multivariate Behavioral Research, 40(2), 207–233. https://doi.org/10.1207/s15327906mbr4002_3
   PubMed Web of Science ®Google Scholar
• Haslbeck, J., & Waldorp, L. J. (2015). mgm: Estimating time-varying mixed graphical models in high-dimensional data. arXiv preprint arXiv:1510.06871.
   Google Scholar
• Hecht, M., & Zitzmann, S. (2021). Sample size recommendations for continuous-time models: Compensating shorter time series with larger numbers of persons and vice versa. Structural Equation Modeling: A Multidisciplinary Journal, 28(2), 229–236. https://doi.org/10.1080/10705511.2020.1779069
   Web of Science ®Google Scholar
• Henry, T. R., Feczko, E., Cordova, M., Earl, E., Williams, S., Nigg, J. T., Fair, D. A., & Gates, K. M. (2019). Comparing directed functional connectivity between groups with confirmatory subgrouping gimme. NeuroImage, 188, 642–653. https://doi.org/10.1016/j.neuroimage.2018.12.040
   PubMed Web of Science ®Google Scholar
• Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218. https://doi.org/10.1007/BF01908075
   Web of Science ®Google Scholar
• Kailath, T. (1967). The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Communications, 15(1), 52–60. https://doi.org/10.1109/TCOM.1967.1089532
   Web of Science ®Google Scholar
• Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28(2), 85–93. https://doi.org/10.1002/hbm.20259
   PubMed Web of Science ®Google Scholar
• Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., Brown, T. A., Carpenter, W. T., Caspi, A., Clark, L. A., Eaton, N. R., Forbes, M. K., Forbush, K. T., Goldberg, D., Hasin, D., Hyman, S. E., Ivanova, M. Y., Lynam, D. R., Markon, K., … Zimmerman, M. (2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454–477. https://doi.org/10.1037/abn0000258
   PubMed Web of Science ®Google Scholar
• Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21(7), 984–991. https://doi.org/10.1177/0956797610372634
   PubMed Web of Science ®Google Scholar
• Lane, S. T., Gates, K. M., Pike, H. K., Beltz, A. M., & Wright, A. G. (2019). Uncovering general, shared, and unique temporal patterns in ambulatory assessment data. Psychological Methods, 24(1), 54–69. https://doi.org/10.1037/met0000192
   PubMed Web of Science ®Google Scholar
• Liu, S., Ou, L., & Ferrer, E. (2020). Dynamic mixture modeling with dynr. Multivariate Behavioral Research, 56(6), 941–955. https://doi.org/10.1080/00273171.2020.1794775
   PubMed Web of Science ®Google Scholar
• Lundh, L.-G. (2015). The person as a focus for research-the contributions of windelband, stern, allport, lamiell, and magnusson. Journal for Person-Oriented Research, 1(1-2), 15–33. https://doi.org/10.17505/jpor.2015.03
   Google Scholar
• Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media.
   Google Scholar
• Lydon-Staley, D. M., Xia, M., Mak, H. W., & Fosco, G. (2019). Adolescent emotion network dynamics in daily life and implications for depression. Journal of Abnormal Child Psychology, 47(4), 717–729. https://doi.org/10.1007/s10802-018-0474-y
   PubMed Web of Science ®Google Scholar
• McCutcheon, A. L. (1987). Latent class analysis (Number 64). Sage.
   Google Scholar
• Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research & Perspective, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1
   Google Scholar
• Muthen, B. (2001). Latent variable mixture modeling. New Developments and Techniques in Structural Equation Modeling, 2, 1–33.
   Google Scholar
• Park, J. J., Chow, S.-M., Fisher, Z. F., & Molenaar, P. C. (2021). Affect and personality: Ramifications of modeling (non-) directionality in dynamic network models. European Journal of Psychological Assessment, 36(6), 1009–1023. https://doi.org/10.1027/1015-5759/a000612
   Web of Science ®Google Scholar
• Park, J. J., Chow, S., & Molenaar, P. (2023). What the fuzz!? leveraging ambiguity in dynamic network models. https://doi.org/10.31234/osf.io/ehtkf
   Google Scholar
• Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In Computer and Information Sciences-ISCIS 2005: 20th International Symposium, Istanbul, Turkey, October 26–28, 2005. Proceedings 20 (pp. 284–293). Springer Berlin Heidelberg.
   Google Scholar
• Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M. S., Thase, M. E., & Siegle, G. J. (2017). Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biological Psychiatry, 81(4), 347–357. https://doi.org/10.1016/j.biopsych.2016.06.023
   PubMed Web of Science ®Google Scholar
• Rothman, A. J., Levina, E., & Zhu, J. (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics: A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 19(4), 947–962. https://doi.org/10.1198/jcgs.2010.09188
   PubMed Web of Science ®Google Scholar
• Runyan, W. M. (1983). Idiographic goals and methods in the study of lives. Journal of Personality, 51(3), 413–437. https://doi.org/10.1111/j.1467-6494.1983.tb00339.x
   PubMed Web of Science ®Google Scholar
• Ryan, O., Kuiper, R. M., & Hamaker, E. L. (2018). A continuous-time approach to intensive longitudinal data: What, why, and how?. In: Van montfort, K., Oud, J., Voelkle, M. (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 27–54). Springer.
   Google Scholar
• Schweren, L., van Borkulo, C. D., Fried, E., & Goodyer, I. M. (2018). Assessment of symptom network density as a prognostic marker of treatment response in adolescent depression. JAMA Psychiatry, 75(1), 98–100. https://doi.org/10.1001/jamapsychiatry.2017.3561
   PubMed Web of Science ®Google Scholar
• Sinclair, A., & Jerrum, M. (1989). Approximate counting, uniform generation and rapidly mixing Markov chains. Information and Computation, 82(1), 93–133. https://doi.org/10.1016/0890-5401(89)90067-9
   Web of Science ®Google Scholar
• Steinley, D. (2004). Properties of the Hubert-arable adjusted rand index. Psychological Methods, 9(3), 386–396. https://doi.org/10.1037/1082-989X.9.3.386
   PubMed Web of Science ®Google Scholar
• Takano, K., Stefanovic, M., Rosenkranz, T., & Ehring, T. (2021). Clustering individuals on limited features of a vector autoregressive model. Multivariate Behavioral Research, 56(5), 768–786. https://doi.org/10.1080/00273171.2020.1767532
   PubMed Web of Science ®Google Scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
   Web of Science ®Google Scholar
• Van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4(1), 5918. https://doi.org/10.1038/srep05918
   PubMedGoogle Scholar
• Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
   Web of Science ®Google Scholar
• Williams, D. R., & Rast, P. (2020). Back to the basics: Rethinking partial correlation network methodology. The British Journal of Mathematical and Statistical Psychology, 73(2), 187–212. https://doi.org/10.1111/bmsp.12173
   PubMed Web of Science ®Google Scholar
• Wright, A. G., Gates, K. M., Arizmendi, C., Lane, S. T., Woods, W. C., & Edershile, E. A. (2019). Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology. Psychological Assessment, 31(4), 502–515. https://doi.org/10.1037/pas0000617
   PubMed Web of Science ®Google Scholar