Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/meta_analysis03282012.r
Function to fit the metaanalytic fixed and randomeffects models.The data consists of effect sizes and corresponding variances from your own method/calculations.
1 
x 
a list with components.

meta.method 
a character string specifying whether a fixed or a random/mixedeffects model should be fitted. A fixedeffects model is fitted when using meta.method="FEM". Randomeffects model is fitted by setting meta.method equal to "REM". See "Details". 
The function can be used to combine any of the usual effect size used in metaanalysis,such as standardized mean differences.Simply specify the observed effect sizes via the x\$ES and the corresponding variances vis x\$Var. If the effect sizes and corresponding varicances calculated from permutation are available,then specify them by x\$perm.ES and x\$perm.Var, respectively.
The argument paired
is a vecter of logical values to specify whethe the corresponding study is paired design or
not. If the study is pairdesigned, the effect sizes (corresponding variances) are calcualted using the formula in morris's
paper, otherwise calculated using the formulas in choi et al.
In addition, if the components of x, perm.ES and perm.Var, are not "NULL", the pvalues are calculated using permutation method, otherwise, the pvalues are calculated using parametric method by assupming the zscores following a standard normal distribution.
The object is a list containing the following components:
zval 
test statistics of the aggregated value. 
pval 
pvalues for the test statistics. 
FDR 
A matrix with one column which has the corrected pvalues using Benjamini and Hochberg method (see 
Qval 
test statistics for the test of heterogeneity. 
Qpval 
pvalues for the test of heterogeneity. 
tau2 
estimated amount of (residual) heterogeneity. 
Jia Li and Xingbin Wang
Choi et al, Combining multiple microarray studies and modeling interstudy variation. Bioinformatics,2003, i84i90.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289:300.
1 2 3 4 5 6 7 8  #example 1: Meta analysis of Differentially expressed genes between two classes#
label1<rep(0:1,each=5)
label2<rep(0:1,each=5)
exp1<cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5))
exp2<cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,1.5),20,5))
x<list(list(exp1,label1),list(exp2,label2))
ind.res<ind.cal.ES(x,paired=rep(FALSE,2),nperm=100)
MetaDE.ES(ind.res,meta.method='REM')

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