meta_varcov: Variance-covariance and GGM meta analysis

In SachaEpskamp/psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

Variance-covariance and GGM meta analysis

Description

Meta analysis of correlation matrices to fit a homogenous correlation matrix or Gaussian graphical model. Based on meta-analytic SEM (Jak and Cheung, 2019).

Usage

meta_varcov(cors, nobs, Vmats, Vmethod = c("individual", "pooled",
                    "metaSEM_individual", "metaSEM_weighted"), Vestimation
                    = c("averaged", "per_study"), type = c("cor", "ggm"),
                    sigma_y = "full", kappa_y = "full", omega_y = "full",
                    lowertri_y = "full", delta_y = "full", rho_y = "full",
                    SD_y = "full", randomEffects = c("chol", "cov",
                    "prec", "ggm", "cor"), sigma_randomEffects = "full",
                    kappa_randomEffects = "full", omega_randomEffects =
                    "full", lowertri_randomEffects = "full",
                    delta_randomEffects = "full", rho_randomEffects =
                    "full", SD_randomEffects = "full", vars,
                    baseline_saturated = TRUE, optimizer, estimator =
                    c("FIML", "ML"), sampleStats, verbose = FALSE)
  
meta_ggm(...)

Arguments

cors	A list of correlation matrices. Must contain rows and columns with NAs for variables not included in a study.
nobs	A vector with the number of observations per study.
Vmats	Optional list with 'V' matrices (sampling error variance approximations).
Vmethod	Which method should be used to apprixomate the sampling error variance?
Vestimation	How should the sampling error estimates be evaluated?
type	What to model? Currently only "cor" and "ggm" are supported.
sigma_y	Only used when type = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
kappa_y	Only used when type = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
omega_y	Only used when type = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
lowertri_y	Only used when type = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
delta_y	Only used when type = "ggm". Either "diag" or "zero" (not recommended), or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
rho_y	Only used when type = "cor". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
SD_y	Only used when type = "cor". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
randomEffects	What to model for the random effects?
sigma_randomEffects	Only used when type = "cov". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
kappa_randomEffects	Only used when randomEffects = "prec". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
omega_randomEffects	Only used when randomEffects = "ggm". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
lowertri_randomEffects	Only used when randomEffects = "chol". Either "full" to estimate every element freely, "diag" to only include diagonal elements, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
delta_randomEffects	Only used when randomEffects = "ggm". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
rho_randomEffects	Only used when randomEffects = "cor". Either "full" to estimate every element freely, "zero" to set all elements to zero, or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
SD_randomEffects	Only used when randomEffects = "cor". Either "diag" or "zero", or a matrix of the dimensions node x node with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.
vars	Variables to be included.
baseline_saturated	A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.
optimizer	The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), and C++ based optimizers "cpp_L-BFGS-B", "cpp_BFGS", "cpp_CG", "cpp_SANN", and "cpp_Nelder-Mead". The C++ optimizers are faster but slightly less stable. Defaults to "nlminb".
estimator	The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation or "FIML" for full-information maximum likelihood estimation.
sampleStats	An optional sample statistics object. Mostly used internally.
verbose	Logical, should progress be printed to the console?
...	Arguments sent to meta_varcov

Value

An object of the class psychonetrics (psychonetrics-class)

Author(s)

Sacha Epskamp <[e-mail]>

References

Jak, S., and Cheung, M. W. L. (2019). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological methods.

SachaEpskamp/psychonetrics documentation built on Sept. 1, 2023, 3:40 a.m.