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\nMaintainer Xiao Yang \u003C[vwendy@gmail.com](vwendy@gmail.com)>  \nDescription An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM) . The person  \noriented method used in this package refers to uSEM (unified structural equation model  \ning, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details) . Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007) .  \nKim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) \u003Cdoi:10.1002/hbm.20259> .  \nGates, K. M., Mole  \nnaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) \u003Cdoi:10.1016/j.neuroimage.2009.12.117> . Gates, K. M., & Molenaar, P. C. M. (2012) \u003Cdoi:10.1016/j.neuroimage.2012.06.026> .  \nLutkepohl, H. (2007, ISBN:3540262393) .  \nLicense GPL-2 Encoding UTF-8 LazyData true RoxygenNote 7.1.1 Depends R (>= 3.0.0)  \nImports lavaan (>= 0.5-23.1097), ggplot2 (>= 2.2.1), reshape2 (>= 1.4.2), qgraph, utils  \nSuggests knitr, rmarkdown, testthat VignetteBuilder knitr NeedsCompilation no  \nAuthor Xiao Yang [cre, aut],  \nNilam Ram [aut],  \nPeter Molenaar [aut] Repository CRAN  \nDate/Publication 2021-02-15 00:40:02 UTC  \nContents  \nbootstrap_iRAM_ 2node .................................. 2  \nbootstrap_iRAM_ 3node .................................. 3  \niRAM ............................................ 4  \niRAM_equilibrium ..................................... 5  \nmodel_summary ...................................... 6  \nparse_beta .......................................... 7  \nplot_integrated_time_profile ................................ 8  \nplot_iRAM_dist ....................................... 8  \nplot_network_graph ..................................... 9  \nplot_time_profile ...................................... 9  \nsimts_ 2node ......................................... 10  \nsimts_ 3node ......................................... 11  \ntrue_beta_ 2node ....................................... 11  \ntrue_beta_ 3node ....................................... 12  \nuSEM ............................................ 12  \nusemmodelfit ........................................ 14  \nIndex 15  \n\n| bootstrap_iRAM_ 2node | Bootstrapped iRAM (including replications of iRAM and correspond ing time profiles) for the bivariate time-series (simts2node) |\n| --- | --- |\n\nDescription  \nBootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the bivariate time-series (simts2node)  \nUsage  \nbootstrap_iRAM_ 2node  \nFormat  \nAn object of class list of length 5 .  \nDetails  \nData bootstrapped from the estimated three-node network structure with 200 replications.  \nbootstrap_iRAM_ 3node 3  \nExamples  \nbootstrap_iRAM_ 2node$mean \\# mean of bootstrapped iRAM  \nbootstrap_iRAM_ 2node$upper \\# Upper bound of confidence interval of bootstrapped iRAM  \nbootstrap_iRAM_ 2node$lower \\# lower bound of confidence interval of bootstrapped iRAM [bootstrap_iRAM_2node$time.profile.data \\# time](bootstrap_iRAM_2node$time.profile.data # time) profiles generated from the bootstrapped beta matrices bootstrap_iRAM_ 2node$recovery .time. reps \\# iRAMs generated from the bootstrapped beta matrices  \n\n| bootstrap_iRAM_ 3node | Bootstrapped iRAM (including replications of iRAM and correspond ing time profiles) for the 3-variate time-series (simts) |\n| --- | --- |\n\nDescription  \nBootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the 3-variate time-series (simts)  \nUsage  \nbootstrap_iRAM_ 3node  \nFormat  \nAn object of class list of length 5 .  \nDetails  \nData bootstrapped from the estimated three-node network structure with 200 replications.  \nExamples  \nbootstrap_iRAM_ 3node$mean \\# mean of bootstrapped iRAM","cbCaiv0R8PVJCO9c","https://ap.wps.com/l/cbCaiv0R8PVJCO9c","pdf",108531,15,"English","# Package overview\n## iRAM\n## iRAM_equilibrium\n## bootstrap_iRAM\n## model and simulation utilities","[{\"question\":\"pompom 这个 R 包的核心用途是什么？\",\"answer\":\"pompom 用于实现人导向方法 uSEM 与模型扰动的混合流程，并生成多变量个体内变异度指标 iRAM。\"},{\"question\":\"如何从模型拟合结果计算 iRAM？\",\"answer\":\"通过 iRAM() 函数，使用模型拟合对象、beta 矩阵（可为点估计）以及时间序列变量数、滞后阶数、扰动阈值等参数进行计算。\"},{\"question\":\"bootstrap_iRAM_2node 和 bootstrap_iRAM_3node 有什么区别？\",\"answer\":\"bootstrap_iRAM_2node 针对二变量时间序列，bootstrap_iRAM_3node 针对三变量时间序列；两者都基于估计的网络结构进行 200 次重抽样，并给出 iRAM 的均值、置信区间上下界以及对应时间剖面数据。\"}]","Person-Oriented Method and Perturbation on the Model - Version 0.2.1 - R包说明 | PDF",38]