meta_varcov {psychonetrics}

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 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?

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 <[e-mail]>

References

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