Package 'metaDyn'

Title: Multivariate Meta-Analysis of Dynamic Model Estimates
Description: Fits fixed-, random-, or mixed-effects multivariate meta-analysis models using dynamic model estimates from each individual building on and extending Lee and Gates (2023) <doi:10.1080/00273171.2023.2229310>.
Authors: Ivan Jacob Agaloos Pesigan [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-4818-8420>)
Maintainer: Ivan Jacob Agaloos Pesigan <[email protected]>
License: MIT + file LICENSE
Version: 1.0.2
Built: 2026-06-05 14:47:40 UTC
Source: https://github.com/jeksterslab/metaDyn

Help Index

Estimated Parameter Method for an Object of Class metadynmeta

Description

Estimated Parameter Method for an Object of Class metadynmeta

Usage

## S3 method for class 'metadynmeta'
coef(object, ...)

Arguments

object

an object of class metadynmeta.
...

further arguments.

Value

Returns a vector of estimated parameters.

Author(s)

Ivan Jacob Agaloos Pesigan

Confidence Intervals for the Parameter Estimates

Description

Confidence Intervals for the Parameter Estimates

Usage

## S3 method for class 'metadynmeta'
confint(
  object,
  parm = NULL,
  level = 0.95,
  ci_type = "wald",
  robust = NULL,
  nrep = 1000L,
  seed = NULL,
  ncores = NULL,
  ...
)

Arguments

object

an object of class metadynmeta.
parm

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
level

the confidence level required.
ci_type

Character string. Valid values are "wald" and "mc".
robust

Logical. If TRUE, use robust (sandwich) sampling variance-covariance matrix. If FALSE, use normal theory sampling variance-covariance matrix. If NULL, the function will check object if robust standard errors are available.
nrep

Positive integer. Number of replications for ci_type = "mc".
seed

Random seed for ci_type = "mc".
ncores

Positive integer. Number of cores to use for ci_type = "mc".
...

further arguments.

Value

Returns a matrix of confidence intervals.

Author(s)

Ivan Jacob Agaloos Pesigan

Extract Generic Function

Description

A generic function for extracting elements from objects.

Usage

extract(object, what)

Arguments

object

An object.
what

Character string.

Value

A value determined by the specific method for the object's class.

Extract Method for an Object of Class metadynmeta

Description

Extract Method for an Object of Class metadynmeta

Usage

## S3 method for class 'metadynmeta'
extract(object, what = NULL)

Arguments

object

an object of class metadynmeta.
what

Character string. What specific matrix to extract. If what = NULL, extract all available matrices.

Value

Returns a list of estimates.

Author(s)

Ivan Jacob Agaloos Pesigan

Fit Multivariate Meta-Analysis

Description

This function estimates fixed-, random-, or mixed-effects meta-analytic parameters using per-individual coefficient estimates and their sampling variance-covariance matrices. Optionally, it fits distal-outcome models in which between-person outcomes are regressed on between-person covariates and the meta-analyzed parameters/effect sizes.

Usage

Meta(
  y,
  v,
  x = NULL,
  z = NULL,
  random = TRUE,
  fixed_x = TRUE,
  alpha_free = NULL,
  alpha_values = NULL,
  alpha_lbound = NULL,
  alpha_ubound = NULL,
  tau_sqr_diag = FALSE,
  tau_sqr_d_free = NULL,
  tau_sqr_d_values = NULL,
  tau_sqr_d_lbound = NULL,
  tau_sqr_d_ubound = NULL,
  tau_sqr_l_free = NULL,
  tau_sqr_l_values = NULL,
  tau_sqr_l_lbound = NULL,
  tau_sqr_l_ubound = NULL,
  mu_x_free = NULL,
  mu_x_values = NULL,
  mu_x_lbound = NULL,
  mu_x_ubound = NULL,
  sigma_x_d_free = NULL,
  sigma_x_d_values = NULL,
  sigma_x_d_lbound = NULL,
  sigma_x_d_ubound = NULL,
  sigma_x_l_free = NULL,
  sigma_x_l_values = NULL,
  sigma_x_l_lbound = NULL,
  sigma_x_l_ubound = NULL,
  gamma_free = NULL,
  gamma_values = NULL,
  gamma_lbound = NULL,
  gamma_ubound = NULL,
  kappa_free = NULL,
  kappa_values = NULL,
  kappa_lbound = NULL,
  kappa_ubound = NULL,
  phi_free = NULL,
  phi_values = NULL,
  phi_lbound = NULL,
  phi_ubound = NULL,
  omega_free = NULL,
  omega_values = NULL,
  omega_lbound = NULL,
  omega_ubound = NULL,
  psi_diag = FALSE,
  psi_d_free = NULL,
  psi_d_values = NULL,
  psi_d_lbound = NULL,
  psi_d_ubound = NULL,
  psi_l_free = NULL,
  psi_l_values = NULL,
  psi_l_lbound = NULL,
  psi_l_ubound = NULL,
  check_estimates = TRUE,
  robust = FALSE,
  alpha = 0.05,
  seed = NULL,
  tries_explore = 100,
  tries_local = 100,
  max_attempts = 10,
  silent = FALSE,
  ncores = NULL
)

Arguments

y

A list. Each element of the list is a numeric vector of estimated coefficients.
v

A list. Each element of the list is a sampling variance-covariance matrix of y.
x

An optional list. Each element of the list is a numeric vector of covariates.
z

An optional list. Each element of the list is a numeric vector of distal outcomes.
random

Logical. If random = TRUE, estimates random effects. If random = FALSE, tau_sqr is a null matrix.
fixed_x

Logical. If fixed_x = TRUE covariates are treated as fixed. If fixed_x = FALSE covariates are treated as stochastic.
alpha_free

Logical vector. Optional vector of free (TRUE) parameters for alpha.
alpha_values

Numeric vector. Optional vector of starting values for alpha.
alpha_lbound

Numeric vector. Optional vector of lower bound values for alpha.
alpha_ubound

Numeric vector. Optional vector of upper bound values for alpha.
tau_sqr_diag

Logical. If tau_sqr_diag = TRUE, tau_sqr is a diagonal matrix. If tau_sqr_diag = FALSE, tau_sqr is a symmetric matrix.
tau_sqr_d_free

Logical vector indicating free/fixed status of the elements of tau_sqr_d. If NULL, all element of tau_sqr_d are free.
tau_sqr_d_values

Numeric vector with starting values for tau_sqr_d. If NULL, defaults to a vector of ones.
tau_sqr_d_lbound

Numeric vector with lower bounds for tau_sqr_d. If NULL, no lower bounds are set.
tau_sqr_d_ubound

Numeric vector with upper bounds for tau_sqr_d. If NULL, no upper bounds are set.
tau_sqr_l_free

Logical matrix indicating which strictly-lower-triangular elements of tau_sqr_l are free. Ignored if tau_sqr_diag = TRUE.
tau_sqr_l_values

Numeric matrix of starting values for the strictly-lower-triangular elements of tau_sqr_l. If NULL, defaults to a null matrix.
tau_sqr_l_lbound

Numeric matrix with lower bounds for tau_sqr_l. If NULL, no lower bounds are set.
tau_sqr_l_ubound

Numeric matrix with upper bounds for tau_sqr_l. If NULL, no upper bounds are set.
mu_x_free

Logical vector. Optional vector of free (TRUE) parameters for mu_x.
mu_x_values

Numeric vector. Optional vector of starting values for mu_x.
mu_x_lbound

Numeric vector. Optional vector of lower bound values for mu_x.
mu_x_ubound

Numeric vector. Optional vector of upper bound values for mu_x.
sigma_x_d_free

Logical vector indicating free/fixed status of the elements of sigma_x_d. If NULL, all element of sigma_x_d are free.
sigma_x_d_values

Numeric vector with starting values for sigma_x_d. If NULL, defaults to a vector of ones.
sigma_x_d_lbound

Numeric vector with lower bounds for sigma_x_d. If NULL, no lower bounds are set.
sigma_x_d_ubound

Numeric vector with upper bounds for sigma_x_d. If NULL, no upper bounds are set.
sigma_x_l_free

Logical matrix indicating which strictly-lower-triangular elements of sigma_x_l are free. Ignored if sigma_x_diag = TRUE.
sigma_x_l_values

Numeric matrix of starting values for the strictly-lower-triangular elements of sigma_x_l. If NULL, defaults to a null matrix.
sigma_x_l_lbound

Numeric matrix with lower bounds for sigma_x_l. If NULL, no lower bounds are set.
sigma_x_l_ubound

Numeric matrix with upper bounds for sigma_x_l. If NULL, no upper bounds are set.
gamma_free

Logical matrix. Optional matrix of free (TRUE) parameters for gamma.
gamma_values

Numeric matrix. Optional matrix of starting values for gamma.
gamma_lbound

Numeric matrix. Optional matrix of lower bound values for gamma.
gamma_ubound

Numeric matrix. Optional matrix of upper bound values for gamma.
kappa_free

Logical vector. Optional vector of free (TRUE) parameters for kappa.
kappa_values

Numeric vector. Optional vector of starting values for kappa.
kappa_lbound

Numeric vector. Optional vector of lower bound values for kappa.
kappa_ubound

Numeric vector. Optional vector of upper bound values for kappa.
phi_free

Logical matrix. Optional matrix of free (TRUE) parameters for phi.
phi_values

Numeric matrix. Optional matrix of starting values for phi.
phi_lbound

Numeric matrix. Optional matrix of lower bound values for phi.
phi_ubound

Numeric matrix. Optional matrix of upper bound values for phi.
omega_free

Logical matrix. Optional matrix of free (TRUE) parameters for omega.
omega_values

Numeric matrix. Optional matrix of starting values for omega.
omega_lbound

Numeric matrix. Optional matrix of lower bound values for omega.
omega_ubound

Numeric matrix. Optional matrix of upper bound values for omega.
psi_diag

Logical. If psi_diag = TRUE, psi is a diagonal matrix. If psi_diag = FALSE, psi is a symmetric matrix.
psi_d_free

Logical vector indicating free/fixed status of the elements of psi_d. If NULL, all element of psi_d are free.
psi_d_values

Numeric vector with starting values for psi_d. If NULL, defaults to a vector of ones.
psi_d_lbound

Numeric vector with lower bounds for psi_d. If NULL, no lower bounds are set.
psi_d_ubound

Numeric vector with upper bounds for psi_d. If NULL, no upper bounds are set.
psi_l_free

Logical matrix indicating which strictly-lower-triangular elements of psi_l are free. Ignored if psi_diag = TRUE.
psi_l_values

Numeric matrix of starting values for the strictly-lower-triangular elements of psi_l. If NULL, defaults to a null matrix.
psi_l_lbound

Numeric matrix with lower bounds for psi_l. If NULL, no lower bounds are set.
psi_l_ubound

Numeric matrix with upper bounds for psi_l. If NULL, no upper bounds are set.
check_estimates

Logical. Check elements of v for positive definiteness. If the test fails, the function generates a near positive definite matrix to replace the original using Matrix::nearPD().
robust

Logical. If TRUE, calculate robust (sandwich) sampling variance-covariance matrix in stage 2.
alpha

NUmeric. Alpha for test of significance and confidence intervals.
seed

Random seed for reproducibility.
tries_explore

Integer. Number of extra tries for the wide exploration phase.
tries_local

Integer. Number of extra tries for local polishing.
max_attempts

Integer. Maximum number of remediation attempts after the first Hessian computation fails the criteria.
silent

Logical. If TRUE, suppresses messages during the model fitting stage.
ncores

Positive integer. Number of cores to use.

Value

Returns an object of class metadynmeta which is a list with the following elements:

call

Function call.

args

List of function arguments.

fun

Function used ("Meta").

output

A fitted OpenMx model.

robust

Output from OpenMx::imxRobustSE() with argument details = TRUE if robust = TRUE.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Cheung, M. W.-L. (2015). Meta-analysis: A structural equation modeling approach. Wiley. doi:10.1002/9781118957813

Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H. H., & Boker, S. M. (2015). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81(2), 535–549. doi:10.1007/s11336-014-9435-8

See Also

Other Meta-Analysis of VAR Functions: MetaVARMx()

Examples

if (requireNamespace("simStateSpace")) {
  # Generate data using the simStateSpace package-------------------------
  library(simStateSpace)
  set.seed(42)
  n <- 5
  time <- 100
  p <- 2
  alpha <- rep(x = 0, times = p)
  beta <- 0.50 * diag(p)
  psi <- 0.001 * diag(p)
  psi_l <- t(chol(psi))
  mu0 <- SSMMeanEta(
    beta = beta,
    alpha = alpha
  )
  sigma0 <- SSMCovEta(
    beta = beta,
    psi = psi
  )
  sigma0_l <- t(chol(sigma0))
  sim <- SimSSMVARFixed(
    n = n,
    time = time,
    mu0 = mu0,
    sigma0_l = sigma0_l,
    alpha = alpha,
    beta = beta,
    psi_l = psi_l
  )
  data <- as.data.frame(sim)

  # Stage 1---------------------------------------------------------------
  library(fitVARMxID)
  stage1 <- FitVARMxID(
    data = data,
    observed = paste0("y", seq_len(p)),
    id = "id",
    center = TRUE
  )
  summary(stage1)
  # Stage 2---------------------------------------------------------------
  # Meta-analyze set point vector and matrix of lagged-effects
  y <- coef(
    object = stage1,
    mu = TRUE,
    beta = TRUE,
    alpha = FALSE,
    nu = FALSE,
    psi = FALSE,
    theta = FALSE
  )
  v <- vcov(
    object = stage1,
    mu = TRUE,
    beta = TRUE,
    alpha = FALSE,
    nu = FALSE,
    psi = FALSE,
    theta = FALSE
  )
  library(metaDyn)
  stage2 <- Meta(y = y, v = v, random = FALSE)
  # Methods for the output of the Meta() function
  print(stage2)
  summary(stage2)
  coef(stage2)
  vcov(stage2)
  confint(stage2)
  extract(stage2, what = "alpha")
}

Fit Multivariate Meta-Analysis (metaVAR via OpenMx)

Description

This function estimates fixed-, random-, or mixed-effects meta-analytic parameters using per-individual coefficient estimates and their sampling variance-covariance matrices. Optionally, it fits distal-outcome models in which between-person outcomes are regressed on between-person covariates and the meta-analyzed parameters/effect sizes. This function uses the estimated coefficients and sampling variance-covariance matrix from each individual fitted using the fitVARMxID::FitVARMxID() function.

Usage

MetaVARMx(
  object,
  x = NULL,
  z = NULL,
  random = TRUE,
  fixed_x = TRUE,
  alpha_free = NULL,
  alpha_values = NULL,
  alpha_lbound = NULL,
  alpha_ubound = NULL,
  tau_sqr_diag = FALSE,
  tau_sqr_d_free = NULL,
  tau_sqr_d_values = NULL,
  tau_sqr_d_lbound = NULL,
  tau_sqr_d_ubound = NULL,
  tau_sqr_l_free = NULL,
  tau_sqr_l_values = NULL,
  tau_sqr_l_lbound = NULL,
  tau_sqr_l_ubound = NULL,
  mu_x_free = NULL,
  mu_x_values = NULL,
  mu_x_lbound = NULL,
  mu_x_ubound = NULL,
  sigma_x_d_free = NULL,
  sigma_x_d_values = NULL,
  sigma_x_d_lbound = NULL,
  sigma_x_d_ubound = NULL,
  sigma_x_l_free = NULL,
  sigma_x_l_values = NULL,
  sigma_x_l_lbound = NULL,
  sigma_x_l_ubound = NULL,
  gamma_free = NULL,
  gamma_values = NULL,
  gamma_lbound = NULL,
  gamma_ubound = NULL,
  kappa_free = NULL,
  kappa_values = NULL,
  kappa_lbound = NULL,
  kappa_ubound = NULL,
  phi_free = NULL,
  phi_values = NULL,
  phi_lbound = NULL,
  phi_ubound = NULL,
  omega_free = NULL,
  omega_values = NULL,
  omega_lbound = NULL,
  omega_ubound = NULL,
  psi_diag = FALSE,
  psi_d_free = NULL,
  psi_d_values = NULL,
  psi_d_lbound = NULL,
  psi_d_ubound = NULL,
  psi_l_free = NULL,
  psi_l_values = NULL,
  psi_l_lbound = NULL,
  psi_l_ubound = NULL,
  check_estimates = TRUE,
  effects = TRUE,
  set_point = TRUE,
  int_meas = TRUE,
  int_dyn = TRUE,
  cov_meas = TRUE,
  cov_dyn = TRUE,
  robust_v = FALSE,
  robust = FALSE,
  alpha = 0.05,
  seed = NULL,
  tries_explore = 100,
  tries_local = 100,
  max_attempts = 10,
  silent = FALSE,
  ncores = NULL
)

Arguments

object

Output of the fitVARMxID::FitVARMxID() function.
x

An optional list. Each element of the list is a numeric vector of covariates.
z

An optional list. Each element of the list is a numeric vector of distal outcomes.
random

Logical. If random = TRUE, estimates random effects. If random = FALSE, tau_sqr is a null matrix.
fixed_x

Logical. If fixed_x = TRUE covariates are treated as fixed. If fixed_x = FALSE covariates are treated as stochastic.
alpha_free

Logical vector. Optional vector of free (TRUE) parameters for alpha.
alpha_values

Numeric vector. Optional vector of starting values for alpha.
alpha_lbound

Numeric vector. Optional vector of lower bound values for alpha.
alpha_ubound

Numeric vector. Optional vector of upper bound values for alpha.
tau_sqr_diag

Logical. If tau_sqr_diag = TRUE, tau_sqr is a diagonal matrix. If tau_sqr_diag = FALSE, tau_sqr is a symmetric matrix.
tau_sqr_d_free

Logical vector indicating free/fixed status of the elements of tau_sqr_d. If NULL, all element of tau_sqr_d are free.
tau_sqr_d_values

Numeric vector with starting values for tau_sqr_d. If NULL, defaults to a vector of ones.
tau_sqr_d_lbound

Numeric vector with lower bounds for tau_sqr_d. If NULL, no lower bounds are set.
tau_sqr_d_ubound

Numeric vector with upper bounds for tau_sqr_d. If NULL, no upper bounds are set.
tau_sqr_l_free

Logical matrix indicating which strictly-lower-triangular elements of tau_sqr_l are free. Ignored if tau_sqr_diag = TRUE.
tau_sqr_l_values

Numeric matrix of starting values for the strictly-lower-triangular elements of tau_sqr_l. If NULL, defaults to a null matrix.
tau_sqr_l_lbound

Numeric matrix with lower bounds for tau_sqr_l. If NULL, no lower bounds are set.
tau_sqr_l_ubound

Numeric matrix with upper bounds for tau_sqr_l. If NULL, no upper bounds are set.
mu_x_free

Logical vector. Optional vector of free (TRUE) parameters for mu_x.
mu_x_values

Numeric vector. Optional vector of starting values for mu_x.
mu_x_lbound

Numeric vector. Optional vector of lower bound values for mu_x.
mu_x_ubound

Numeric vector. Optional vector of upper bound values for mu_x.
sigma_x_d_free

Logical vector indicating free/fixed status of the elements of sigma_x_d. If NULL, all element of sigma_x_d are free.
sigma_x_d_values

Numeric vector with starting values for sigma_x_d. If NULL, defaults to a vector of ones.
sigma_x_d_lbound

Numeric vector with lower bounds for sigma_x_d. If NULL, no lower bounds are set.
sigma_x_d_ubound

Numeric vector with upper bounds for sigma_x_d. If NULL, no upper bounds are set.
sigma_x_l_free

Logical matrix indicating which strictly-lower-triangular elements of sigma_x_l are free. Ignored if sigma_x_diag = TRUE.
sigma_x_l_values

Numeric matrix of starting values for the strictly-lower-triangular elements of sigma_x_l. If NULL, defaults to a null matrix.
sigma_x_l_lbound

Numeric matrix with lower bounds for sigma_x_l. If NULL, no lower bounds are set.
sigma_x_l_ubound

Numeric matrix with upper bounds for sigma_x_l. If NULL, no upper bounds are set.
gamma_free

Logical matrix. Optional matrix of free (TRUE) parameters for gamma.
gamma_values

Numeric matrix. Optional matrix of starting values for gamma.
gamma_lbound

Numeric matrix. Optional matrix of lower bound values for gamma.
gamma_ubound

Numeric matrix. Optional matrix of upper bound values for gamma.
kappa_free

Logical vector. Optional vector of free (TRUE) parameters for kappa.
kappa_values

Numeric vector. Optional vector of starting values for kappa.
kappa_lbound

Numeric vector. Optional vector of lower bound values for kappa.
kappa_ubound

Numeric vector. Optional vector of upper bound values for kappa.
phi_free

Logical matrix. Optional matrix of free (TRUE) parameters for phi.
phi_values

Numeric matrix. Optional matrix of starting values for phi.
phi_lbound

Numeric matrix. Optional matrix of lower bound values for phi.
phi_ubound

Numeric matrix. Optional matrix of upper bound values for phi.
omega_free

Logical matrix. Optional matrix of free (TRUE) parameters for omega.
omega_values

Numeric matrix. Optional matrix of starting values for omega.
omega_lbound

Numeric matrix. Optional matrix of lower bound values for omega.
omega_ubound

Numeric matrix. Optional matrix of upper bound values for omega.
psi_diag

Logical. If psi_diag = TRUE, psi is a diagonal matrix. If psi_diag = FALSE, psi is a symmetric matrix.
psi_d_free

Logical vector indicating free/fixed status of the elements of psi_d. If NULL, all element of psi_d are free.
psi_d_values

Numeric vector with starting values for psi_d. If NULL, defaults to a vector of ones.
psi_d_lbound

Numeric vector with lower bounds for psi_d. If NULL, no lower bounds are set.
psi_d_ubound

Numeric vector with upper bounds for psi_d. If NULL, no upper bounds are set.
psi_l_free

Logical matrix indicating which strictly-lower-triangular elements of psi_l are free. Ignored if psi_diag = TRUE.
psi_l_values

Numeric matrix of starting values for the strictly-lower-triangular elements of psi_l. If NULL, defaults to a null matrix.
psi_l_lbound

Numeric matrix with lower bounds for psi_l. If NULL, no lower bounds are set.
psi_l_ubound

Numeric matrix with upper bounds for psi_l. If NULL, no upper bounds are set.
check_estimates

Logical. Check elements of v for positive definiteness. If the test fails, the function generates a near positive definite matrix to replace the original using Matrix::nearPD().
effects

Logical. If effects = TRUE, include estimates of the dynamic effects matrix, if available. If effects = FALSE, exclude estimates of the dynamic effects matrix.
set_point

Logical. If set_point = TRUE, include estimates of the set-point vector, if available. If set_point = FALSE, exclude estimates of the set-point vector.
int_meas

Logical. If int_meas = TRUE, include estimates of the measurement intercept vector, if available. If int_meas = FALSE, exclude estimates of the measurement intercept vector.
int_dyn

Logical. If int_dyn = TRUE, include estimates of the dynamic process intercept vector, if available. If int_dyn = FALSE, exclude estimates of the dynamic process intercept vector.
cov_meas

Logical. If cov_meas = TRUE, include estimates of the measurement error covariance matrix, if available. If cov_meas = FALSE, exclude estimates of the measurement error covariance matrix.
cov_dyn

Logical. If cov_dyn = TRUE, include estimates of the process noise covariance matrix, if available. If cov_dyn = FALSE, exclude estimates of the process noise covariance matrix.
robust_v

Logical. If TRUE, use robust (sandwich) sampling variance-covariance matrix in stage 1. If FALSE, use normal theory sampling variance-covariance matrix in stage 1.
robust

Logical. If TRUE, calculate robust (sandwich) sampling variance-covariance matrix in stage 2.
alpha

NUmeric. Alpha for test of significance and confidence intervals.
seed

Random seed for reproducibility.
tries_explore

Integer. Number of extra tries for the wide exploration phase.
tries_local

Integer. Number of extra tries for local polishing.
max_attempts

Integer. Maximum number of remediation attempts after the first Hessian computation fails the criteria.
silent

Logical. If TRUE, suppresses messages during the model fitting stage.
ncores

Positive integer. Number of cores to use.

Value

Returns an object of class metadynmeta which is a list with the following elements:

call

Function call.

args

List of function arguments.

fun

Function used ("Meta").

output

A fitted OpenMx model.

robust

Output from OpenMx::imxRobustSE() with argument details = TRUE if robust = TRUE.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Cheung, M. W.-L. (2015). Meta-analysis: A structural equation modeling approach. Wiley. doi:10.1002/9781118957813

Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H. H., & Boker, S. M. (2015). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81(2), 535–549. doi:10.1007/s11336-014-9435-8

See Also

Other Meta-Analysis of VAR Functions: Meta()

Examples

if (requireNamespace("simStateSpace")) {
  # Generate data using the simStateSpace package-------------------------
  library(simStateSpace)
  set.seed(42)
  n <- 5
  time <- 100
  p <- 2
  alpha <- rep(x = 0, times = p)
  beta <- 0.50 * diag(p)
  psi <- 0.001 * diag(p)
  psi_l <- t(chol(psi))
  mu0 <- SSMMeanEta(
    beta = beta,
    alpha = alpha
  )
  sigma0 <- SSMCovEta(
    beta = beta,
    psi = psi
  )
  sigma0_l <- t(chol(sigma0))
  sim <- SimSSMVARFixed(
    n = n,
    time = time,
    mu0 = mu0,
    sigma0_l = sigma0_l,
    alpha = alpha,
    beta = beta,
    psi_l = psi_l
  )
  data <- as.data.frame(sim)

  # Stage 1---------------------------------------------------------------
  library(fitVARMxID)
  stage1 <- FitVARMxID(
    data = data,
    observed = paste0("y", seq_len(p)),
    id = "id",
    center = TRUE
  )
  summary(stage1)
  # Stage 2---------------------------------------------------------------
  # Meta-analyze set point vector and matrix of lagged-effects
  library(metaDyn)
  stage2 <- MetaVARMx(
    object = stage1,
    random = FALSE,
    effects = TRUE,
    set_point = TRUE,
    int_meas = FALSE,
    int_dyn = FALSE,
    cov_meas = FALSE,
    cov_dyn = FALSE
  )
  # Methods for the output of the MetaVARMx() function
  print(stage2)
  summary(stage2)
  coef(stage2)
  vcov(stage2)
  confint(stage2)
  extract(stage2, what = "alpha")
}

Print Method for Object of Class metadynmeta

Description

Print Method for Object of Class metadynmeta

Usage

## S3 method for class 'metadynmeta'
print(
  x,
  alpha = NULL,
  ci_type = "wald",
  robust = NULL,
  nrep = 1000L,
  seed = NULL,
  digits = 4,
  ...
)

Arguments

x

an object of class metadynmeta.
alpha

Numeric vector. Significance level α\alpha. If NULL, the function will check object for alpha used in model fitting.
ci_type

Character string. Valid values are "wald" and "mc".
robust

Logical. If TRUE, use robust (sandwich) sampling variance-covariance matrix. If FALSE, use normal theory sampling variance-covariance matrix. If NULL, the function will check object if robust standard errors are available.
nrep

Positive integer. Number of replications for ci_type = "mc".
seed

Random seed for ci_type = "mc".
digits

Integer indicating the number of decimal places to display.
...

further arguments.

Value

Returns a matrix of estimates, standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.

Author(s)

Ivan Jacob Agaloos Pesigan

Summary Method for Object of Class metadynmeta

Description

Summary Method for Object of Class metadynmeta

Usage

## S3 method for class 'metadynmeta'
summary(
  object,
  alpha = NULL,
  ci_type = "wald",
  robust = NULL,
  nrep = 1000L,
  seed = NULL,
  ncores = NULL,
  digits = 4,
  ...
)

Arguments

object

an object of class metadynmeta.
alpha

Numeric vector. Significance level α\alpha. If NULL, the function will check object for alpha used in model fitting.
ci_type

Character string. Valid values are "wald" and "mc".
robust

Logical. If TRUE, use robust (sandwich) sampling variance-covariance matrix. If FALSE, use normal theory sampling variance-covariance matrix. If NULL, the function will check object if robust standard errors are available.
nrep

Positive integer. Number of replications for ci_type = "mc".
seed

Random seed for ci_type = "mc".
ncores

Positive integer. Number of cores to use for ci_type = "mc".
digits

Integer indicating the number of decimal places to display.
...

further arguments.

Value

Returns a matrix of estimates, standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.

Author(s)

Ivan Jacob Agaloos Pesigan

Variance-Covariance Matrix Method for an Object of Class metadynmeta

Description

Variance-Covariance Matrix Method for an Object of Class metadynmeta

Usage

## S3 method for class 'metadynmeta'
vcov(object, robust = NULL, ...)

Arguments

object

an object of class metadynmeta.
robust

Logical. If TRUE, use robust (sandwich) sampling variance-covariance matrix. If FALSE, use normal theory sampling variance-covariance matrix. If NULL, the function will check object if robust standard errors are available.
...

further arguments.

Value

Returns the sampling variance-covariance matrix of the estimated parameters.

Author(s)

Ivan Jacob Agaloos Pesigan