# meta_varcov: Variance-covariance and GGM meta analysis In psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis

 meta_varcov R Documentation

## 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,
bootstrap = FALSE, boot_sub, boot_resample)

meta_ggm(...)
``````

### Arguments

 `cors` A list of correlation matrices. Must contain rows and columns with `NA`s 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? `bootstrap` Should the data be bootstrapped? If `TRUE` the data are resampled and a bootstrap sample is created. These must be aggregated using `aggregate_bootstraps`! Can be `TRUE` or `FALSE`. Can also be `"nonparametric"` (which sets `boot_sub = 1` and `boot_resample = TRUE`) or `"case"` (which sets `boot_sub = 0.75` and `boot_resample = FALSE`). `boot_sub` Proportion of cases to be subsampled (`round(boot_sub * N)`). `boot_resample` Logical, should the bootstrap be with replacement (`TRUE`) or without replacement (`FALSE`) `...` Arguments sent to `meta_varcov`

### Value

An object of the class psychonetrics (psychonetrics-class)

### Author(s)

Sacha Epskamp <mail@sachaepskamp.com>

### References

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

psychonetrics documentation built on June 22, 2024, 10:29 a.m.