Package 'pompom'

Title: Person-Oriented Method and Perturbation on the Model
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).
Authors: Xiao Yang [cre, aut], Nilam Ram [aut], Peter Molenaar [aut]
Maintainer: Xiao Yang <[email protected]>
License: GPL-2
Version: 0.2.1
Built: 2024-11-16 04:20:36 UTC
Source: https://github.com/cran/pompom

Help Index

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 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 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 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-series generated by impulse response analysis

Description

Plot time profiles given a time-series generated by impulse response analysis

Usage

plot_time_profile(time.series.data, var.number, threshold = 0.01, xupper = 20)

Arguments

time.series.data

data of impulse response in long format
var.number

number of variables in the time-series
threshold

threshold of asymptote of equilibrium
xupper

upper limit of x-axis

Examples

plot_time_profile(time.series.data = bootstrap_iRAM_2node$time.profile.data,
                 var.number = 2,
                 threshold= .01,
                 xupper = 20)

Simulated bivariate time-series data

Description

Simulated bivariate time-series data

Usage

simts_2node

Format

An object of class data.frame with 200 rows and 2 columns.

Details

Data simulated from a given three-node network structure with 200 measurements. Network structure is shown in the dataset true.beta. Process noise has mean of 0 and SD .1.

Examples

data(simts_2node)

Simulated 3-variate time-series data

Description

Simulated 3-variate time-series data

Usage

simts_3node

Format

An object of class data.frame with 100 rows and 3 columns.

Details

Data simulated from a given three-node network structure with 200 measurements. Network structure is shown in the dataset true.beta. Process noise has mean of 0 and SD .1.

Examples

data(simts_3node)

The true beta matrix (4 by 4) used in simulation.

Description

The true beta matrix (4 by 4) used in simulation.

Usage

true_beta_2node

Format

An object of class matrix (inherits from array) with 4 rows and 4 columns.

Details

true_beta_2node <- matrix(c(0,0,0,0, 0,0,0,0, 0.2,-.4,0,-0.25, 0,0.3,-0.2,0), nrow = 4, ncol = 4, byrow = TRUE)

Examples

true_beta_2node

The true beta matrix (6 by 6) used in simulation.

Description

The true beta matrix (6 by 6) used in simulation.

Usage

true_beta_3node

Format

An object of class matrix (inherits from array) with 6 rows and 6 columns.

Details

true_beta_3node <- matrix(c(0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0.2,0,0.25,0,0,0.6, 0,0.3,0,-0.2,0,-0.6, 0,-0.2,0.3,0,0,0), nrow = 6, ncol = 6, byrow = TRUE)

Examples

true_beta_3node

Fit a multivariate time series with uSEM (unified Structural Equation Model).

Description

Fit a multivariate time series with uSEM (unified Structural Equation Model).

Usage

uSEM(var.number,
     data,
     lag.order = 1,
     verbose = FALSE,
     trim = FALSE)

Arguments

var.number

number of variables in the time series
data

time series data, must be in long format
lag.order

lag order of the model to be fit, default value is 1. Note: Higher order (greater than 1) might not run.
verbose

print intermediate model fit (iterations), default value is FALSE
trim

to trim the insignificant betas (just one step, not iterative), default value is FALSE

Details

The purpose of uSEM is to quantify the temporal relations (both contemporaneous and lag-1) between variables. Model specification and estimation can be found in the references.

Value

model fit object generated by lavaan

References

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, 93, 85–93. doi:10.1002/hbm.20259

Gates, K. M., & Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage 63(1), 310-319. doi: 10.1016/j.neuroimage.2012.06.026

Gates, K. M., Molenaar, P. C. M., 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. doi: 10.1016/j.neuroimage.2009.12.117

Examples

model.fit <- uSEM(var.number = 3,
                 data = simts_3node,
                 lag.order = 1,
                 verbose = FALSE,
                 trim = FALSE)
model.fit

Model fitbased on similated time-series by uSEM.

Description

Model fitbased on similated time-series by uSEM.

Usage

usemmodelfit

Format

An object of class lavaan of length 1.

Examples

data(usemmodelfit)