metahdep (version 1.30.0)

metahdep.FEMA: metahdep.FEMA

Description

Performs a fixed effects linear model meta-analysis. It returns a list containing the results.

Usage

metahdep.FEMA(theta, V, X, meta.name = "meta-analysis",  center.X = FALSE)

Arguments

theta

A vector of effect size estimates from multiple studies.

V

The variance/covariance matrix for theta. Typically, this will be block diagonal (to represent any sampling dependence).

X

A matrix of covariates for theta. At the very least, this must consist of an intercept term. Other covariates can be included, but there must be more rows than columns in this covariate matrix.

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 X. If TRUE, then the mean from each column will be subtracted from every element in that column (but not for the intercept). This changes the interpretation of the intercept coefficient estimate from the model fit.

Value

beta.hats

A vector of model estimates for the covariates given by X (it may be a scalar, i.e., a vector of length 1 )

cov.matrix

The variance/covariance matrix for the beta.hats estimate(s)

beta.hat.p.values

The [two-sided] p-value(s) for the beta.hats estimate(s)

Q

The statistic used to test for model homogeneity / model mis-specification

Q.p.value

The p-value for Q

name

An optional name field

Details

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.

References

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.

Examples

###
### 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)