# ind.cal.ES

##### 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.

##### See Also

##### 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)
MetaDE.ES(ind.res,meta.method='REM')
```

*Documentation reproduced from package MetaDE, version 1.0.5, License: GPL-2*