pompom

README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} pompom R package to perform time-series analysis and guage the temporal influence from one variable to another. We created an R package named “pompom” (pompom is the initials of person-oriented modeling and perturbation on the model), and we will use the functions in “pompom” to compute iRAM (impulse response analysis metric) in this pacakge. iRAM is built upon a hybrid method that combines intraindividual variability methods and network analysis methods in order to model individuals as high-dimensional dynamic systems. This hybrid method is designed and tested to quantify the extent of interaction in a high-dimensional multivariate system, and applicable on experience sampling data.

Help for package pompom const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {pompom} Contents bootstrap_iRAM_2node bootstrap_iRAM_3node iRAM iRAM_equilibrium model_summary parse_beta plot_iRAM_dist plot_integrated_time_profile plot_network_graph plot_time_profile simts_2node simts_3node true_beta_2node true_beta_3node uSEM usemmodelfit Type: Package Title: Person-Oriented Method and Perturbation on the Model Version: 0.2.1 Maintainer: Xiao Yang <vwendy@gmail.com> Description: 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-oriented method used in this package refers to uSEM (unified structural equation modeling, 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). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) < doi:10.1002/hbm.20259 >. Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) < doi:10.1016/j.neuroimage.2009.12.117 >. Gates, K. M., & Molenaar, P. C. M. (2012) < doi:10.1016/j.neuroimage.2012.06.026 >. Lutkepohl, H. (2007, ISBN:3540262393). License: GPL-2 Encoding: UTF-8 LazyData: true RoxygenNote: 7.1.1 Depends: R (≥ 3.0.0) Imports: lavaan (≥ 0.5-23.1097), ggplot2 (≥ 2.2.1), reshape2 (≥ 1.4.2), qgraph, utils Suggests: knitr, rmarkdown, testthat VignetteBuilder: knitr NeedsCompilation: no Packaged: 2021-01-22 15:46:39 UTC; Xiao Yang Author: Xiao Yang [cre, aut], Nilam Ram [aut], Peter Molenaar [aut] Repository: CRAN Date/Publication: 2021-02-15 00:40:02 UTC Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the bivariate time-series (simts2node) Description Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the bivariate time-series (simts2node) Usage bootstrap_iRAM_2node Format An object of class list of length 5. Details Data bootstrapped from the estimated three-node network structure with 200 replications. Examples bootstrap_iRAM_2node$mean # mean of bootstrapped iRAM bootstrap_iRAM_2node$upper # Upper bound of confidence interval of bootstrapped iRAM bootstrap_iRAM_2node$lower # lower bound of confidence interval of bootstrapped iRAM bootstrap_iRAM_2node$time.profile.data # time profiles generated from the bootstrapped beta matrices bootstrap_iRAM_2node$recovery.time.reps # iRAMs generated from the bootstrapped beta matrices Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the 3-variate time-series (simts) Description Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the 3-variate time-series (simts) Usage bootstrap_iRAM_3node Format An object of class list of length 5. Details Data bootstrapped from the estimated three-node network structure with 200 replications. Examples bootstrap_iRAM_3node$mean # mean of bootstrapped iRAM bootstrap_iRAM_3node$upper # Upper bound of confidence interval of bootstrapped iRAM bootstrap_iRAM_3node$lower # lower bound of confidence interval of bootstrapped iRAM bootstrap_iRAM_3node$time.profile.data # time profiles generated from the bootstrapped beta matrices bootstrap_iRAM_3node$recovery.time.reps # iRAMs generated from the bootstrapped beta matrices Generate iRAM (impulse response anlaysis metric) from model fit. Description Generate iRAM (impulse response anlaysis metric) from model fit. Usage iRAM( model.fit, beta, var.number, lag.order = 1, threshold = 0.01, boot = FALSE, replication = 200, steps = 100 ) Arguments model.fit model fit object generated by lavaan beta beta matrix for a point estimate var.number number of variables in the time series lag.order lag order of the model to be fit threshold threshold of calculation of recovery time (duration of perturbation), default value is 0.01 boot to bootstrap, default value is FALSE replication number of replication of bootstrap, default value is 200 steps number of steps of impulse response analysis, default value is 100 Value iRAM matrix. Rows represent where the orthognal impulse was given, and columns represent the response. Dimension is var.number by var.number. References Lütkepohl, H. (2007). New introduction to multiple time-series analysis. Berlin: Springer. Examples boot.iRAM <- iRAM(model.fit = usemmodelfit, beta = NULL, var.number = 3, lag.order = 1, threshold = 0.01, boot = TRUE, replication = 200, steps = 100 ) boot.iRAM$mean Generate iRAM (impulse response anlaysis metric) in the equilibrium form. Description Generate iRAM (impulse response anlaysis metric) in the equilibrium form. Usage iRAM_equilibrium(beta.matrix, var.number, lag.order) Arguments beta.matrix beta matrix for a point estimate var.number number of variables in the time series lag.order lag order of the model to be fit Value a list of equilibria. First numeric number in the variable name indicate where the impulse was given, and the second numeric number indicate the response, e.g., e12 indicates equilibrium of node 2 when node 1 is given an impulse. Examples iRAM_evalue <- iRAM_equilibrium(beta.matrix = true_beta_3node, var.number = 3, lag.order = 1 ) iRAM_evalue Provide model summary. Description Provide model summary. Usage model_summary(model.fit, var.number, lag.order) Arguments model.fit model fit object generated by lavaan var.number number of variables in the time-series lag.order lag order of model Details Model fit criteria: 3 out of 4 rule, meaning 3 out of 4 critea should be satisfied, including CFI and TLI should be greater than 0.95, RMSEA and SRMR should be less than 0.08. Value beta matrix estimates matrix of standard error of beta matrix of psi estimates fit statistics CFI fit statistics TLI fit statistics RMSEA fit statistics SRMR Examples mdl <- model_summary(model.fit = usemmodelfit, var.number = 3, lag.order = 1) mdl$beta mdl$beta.se mdl$psi mdl$cfi mdl$tli mdl$rmsea mdl$srmr Parse the beta from model fit object Description Parse the beta from model fit object Usage parse_beta(var.number, model.fit, lag.order, matrix = F) Arguments var.number number of variables in the time series model.fit model fit object generated by lavaan lag.order lag order of the model to be fit matrix output beta in matrix format or estimates format, default value is FALSE (as estimates) Value beta Examples data(usemmodelfit) beta.matrix <- parse_beta(var.number = 3, model.fit = usemmodelfit, lag.order = 1, matrix = TRUE) beta.matrix Plot distribution of recovery time based on bootstrapped version of iRAM Description Plot distribution of recovery time based on bootstrapped version of iRAM Usage plot_iRAM_dist(recovery.time.reps) Arguments recovery.time.reps bootstrapped version of recovery time Examples plot_iRAM_dist(bootstrap_iRAM_3node$recovery.time.reps) Plot the time profiles in the integrated form Description Plot the time profiles in the integrated form Usage plot_integrated_time_profile(beta.matrix, var.number, lag.order = 1) Arguments beta.matrix matrix of temporal relations, cotaining both lag-1 and contemporaneous var.number number of variables in the time series lag.order lag order of the model to be fit Examples plot_integrated_time_profile(beta.matrix = true_beta_3node, var.number = 3, lag.order = 1) Plot the network graph Description Plot the network graph Usage plot_network_graph(beta, var.number) Arguments beta matrix of temporal relations, cotaining both lag-1 and contemporaneous var.number number of variables in the time series Examples plot_network_graph(beta = true_beta_3node, var.number = 3) Plot time profiles given a time-ser