metahdep.FEMA {metahdep} | R Documentation |
Performs a fixed effects linear model meta-analysis. It returns a list containing the results.
metahdep.FEMA(theta, V, X, meta.name = "meta-analysis", center.X = FALSE)
theta |
A vector of effect size estimates from multiple studies. |
V |
The variance/covariance matrix for |
X |
A matrix of covariates for |
meta.name |
(optional) A name field for bookkeeping. This can be any character string. |
center.X |
(optional) A logical value specifying whether or not to center the columns of |
Takes a vector of effect size estimates, a variance/covariance matrix, and a covariate matrix, and fits a fixed effects linear model meta-analysis.
When a meta-analysis is to be performed for gene expression data (on a per-gene basis), the metahdep()
function calls this function for each gene separately.
A list with the following named components:
beta.hats |
A vector of model estimates for the covariates given by |
cov.matrix |
The variance/covariance matrix for the |
beta.hat.p.values |
The [two-sided] p-value(s) for the |
Q |
The statistic used to test for model homogeneity / model mis-specification |
Q.p.value |
The p-value for |
name |
An optional name field |
John R. Stevens, Gabriel Nicholas
Hedges L. V. and Olkin I (1985), Statistical methods for meta-analysis, San Diego, CA: Academic Press.
Stevens J.R. and Doerge R.W. (2005), Combining Affymetrix Microarray Results, BMC Bioinformatics 6:57.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X gene.name <- gene.data@gene # fit a regular FEMA (no hierarchical dependence) results <- metahdep.FEMA(theta, V, X, meta.name=gene.name, center.X=TRUE) results ### ### Example 2: glossing data ### - this produces part of Table 5 in the Stevens and Taylor JEBS paper. data(gloss) FEMA <- metahdep.FEMA(gloss.theta, gloss.V, gloss.X, center.X=TRUE) round(cbind( t(FEMA$beta.hats), t(FEMA$beta.hat.p.values)),4)