# ind.cal.ES

0th

Percentile

##### Calculate the effect sizes

The function can be used to calculate various effect sizes(and the corresponding sampling variances) that are commonly used in meta-analyses.

##### Usage
ind.cal.ES(x, paired, nperm = NULL,miss.tol=0.3)
##### Arguments
x
a list of data sets and their labels. The first list is a list of datasets, the second list is a list of their labels
paired
A vector of logical values to specify the design patterns of studies. see 'Details'.
nperm
an integer to specify the number of permutations.
miss.tol
The maximum percent missing data allowed in any gene (default 30 percent).
##### Details

This functions is used to calculate the effect size, standardized mean difference, often used in meta-analysis.

The argument paired is a vector of logical values to specify whether the corresponding study is paired design or not. If the study is pair-designed, the effect sizes (corresponding variances) are calcualted using the formula in morris's paper, otherwise calculated using the formulas in choi et al.

##### Value

ES
The observed effect sizes.
Var
The observed variances corresponding to ES
perm.ES
The effect sizes calculated from permutations, perm.ES is NULL if the argument nperm is set as NULL.
perm.Var
The corresponding variances calculated from permutations. perm.Var is NULL if the argument nperm is set as NULL.

##### References

Morris SB: Distribution of the standardized mean change effect size for meta-analysis on repeated measures. Br J Math Stat Psychol 2000, 53 ( Pt 1):17-29. Choi et al, Combining multiple microarray studies and modeling interstudy variation. Bioinformatics,2003, i84-i90.

MetaDE.ES

• ind.cal.ES
##### Examples
#---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)