MetaHD {MetaHD} | R Documentation |
A Multivariate Meta-Analysis Model for High-Dimensional Metabolomics Data
Description
The MetaHD function performs a multivariate meta-analysis for combining summary estimates obtained from multiple metabolomic studies by using restricted maximum likelihood estimation. Assuming a meta-analysis is based on N outcomes and K studies:
Usage
MetaHD(
Y, Slist,
Psi = NULL,
method = c("reml", "fixed"),
bscov = c("unstructured", "diag"),
optim.algorithm = c("BOBYQA","hybrid","L-BFGS-B"),
initPsi = NULL,
optim.maxiter = 2000,
rigls.iter = 1,
est.wscor = FALSE,
shrinkCor = TRUE,
impute.na = FALSE,
impute.var = 10^4
)
Arguments
Y |
: treatment effect sizes of the outcomes. This should be in the form of a K x N matrix |
Slist |
: K-dimensional list of N x N matrices representing within-study variances and covariances of the treatment effects. If within-study correlations are not available, input associated variances of treatment effects in the form of a K x N matrix and set est.wscor = TRUE. |
Psi |
: N x N matrix representing between-study variances and covariances of the treatment effects. (optional, if not specified this will be estimated internally by "MetaHD" using "estimateBSvar" and "estimateCorMat" functions in "MetaHD" package). |
method |
: estimation method: "fixed" for fixed-effects models,"reml" for random-effects models fitted through restricted maximum likelihood |
bscov |
: a character vector defining the structure of the random-effects covariance matrix. Among available covariance structures, the user can select "unstructured" to obtain between-study covariance matrix with diagonal elements (variances) estimated using restricted maximul likelihood and off-diagonal elements (co-variances) reflecting the correlations estimated via shrinkage and "diag" (diagonal) for between-study variances as diagonal elements and zero co-variances |
optim.algorithm |
: specifies the algorithm used to maximize the restricted log-likelihood function for estimating between-study variances. The default algorithm is "BOBYQA", which offers derivative-free, bound-constrained optimization by iteratively constructing a quadratic approximation of the objective function. The "hybrid" option performs up to rigls.iter iterations of the RIGLS algorithm, followed by quasi-Newton (BFGS algorithm) iterations until convergence. If rigls.iter is set to zero, only the quasi-Newton method (BFGS algorithm) is used for estimation. The "L-BFGS-B" algorithm is a limited-memory version of the BFGS quasi-Newton method, which supports box constraints, allowing each variable to have specified lower and/or upper bounds. |
initPsi |
: N x N diagonal matrix representing the starting values of the between-study variances to be used in the optimization procedures. If not specified, the starting values in Psi default to a diagonal matrix with variances set to 1. |
optim.maxiter |
: maximum number of iterations in methods involving optimization procedures. |
rigls.iter |
: number of iterations of the restricted iterative generalized least square algorithm (RIGLS) when used in the initial phase of hybrid optimization procedure. Default is set to 1 |
est.wscor |
: a logical value indicating whether the within-study correlation matrix needs to be estimated or not. Default is FALSE |
shrinkCor |
: a logical value indicating whether a shrinkage estimator should be used to estimate within- or between-study correlation matrix. Default is TRUE |
impute.na |
: a logical value indicating whether missing values need to be imputed or not. Default is FALSE |
impute.var |
: multiplier for replacing the missing variances in Slist.(a large value, default is 10^4) |
Value
A list of objects containing estimate : a N-dimensional vector of the combined estimates, std.err : a N-dimensional vector of the associated standard errors, pVal : a N-dimensional vector of the p-values, I2.stat : I2 statistic