MBASED (version 1.6.0)

MBASEDMetaAnalysisGetMeansAndSEs: Helper function to obtain estimate of underlying mean and the standard error of the estimate in meta analysis framework.

Description

Helper function to obtain estimate of underlying mean and the standard error of the estimate in meta analysis framework.

Usage

MBASEDMetaAnalysisGetMeansAndSEs(zValuesMat, zVariancesMat, checkArgs = FALSE)

Arguments

zValuesMat

matrix of z-values, on standard normal scale. Each row represents a specific genomic locus, while each column represents a set of observed values across loci (in practice, multiple columns represent different outcomes of simulations).

zVariancesMat

matrix of (estimated) variances of each z-value in zValuesMat. The interpretation of rows and columns is the same as for zValuesMat.

checkArgs

single boolean specifying whether arguments should be checked for adherence to specifications. DEFAULT: FALSE

Value

a list with 4 elements:

weightsMat

a matrix of same dimension as zValuesMat, giving the assigned weight for each observation

totalWeights

a vector of length equal to number of rows in zValuesMat, giving the column sum of assigned weights

hetQ

a vector of length equal to number of rows in zValuesMat, giving the estimated standard error for the corresponding entries in meanValues

meanValues

a vector of length equal to number of rows in zValuesMat, giving for each column the estimated average value.

hetQ

a vector of length equal to number of rows in zValuesMat, giving the estimated standard error for the corresponding entries in meanValues

Details

MBASEDMetaAnalysisGetMeansAndSEs is a helper function employed by MBASEDMetaAnalysis(). For each column of input matrices, it calculates the inverse-variance weighted column average and provides an estimate of the standard error of this mean estimator. Input matrices zValuesMat and zVariancesMat have one column for each set of loci ('independent studies') to be combined, with each row corresponding to an individual locus.

Examples

set.seed(127000)
zVals1=rnorm(5, mean=rep(2,5), sd=sqrt(1:5))
zVals2=rnorm(5, mean=0, sd=1)+c(0,0,5,0,0) ## one outlier
MBASED:::MBASEDMetaAnalysisGetMeansAndSEs(zValuesMat=matrix(c(zVals1, zVals2), ncol=2), zVariancesMat=matrix(c(1:5, rep(1,5)), ncol=2))