This function performs sample size computation for testing Pearson's correlation, using uses Fisher's classic z-transformation to normalize the distribution of Pearson's correlation coefficient.

## Usage

```
size_corr(
r1,
r0,
alpha = 0.05,
power = 0.8,
alternative = c("two.sided", "less", "greater")
)
```

## Arguments

- r1
(

`numeric`

)

expected correlation coefficient of the evaluated assay.- r0
(

`numeric`

)

acceptable correlation coefficient of the evaluated assay.- alpha
(

`numeric`

)

type-I-risk, \(\alpha\).- power
(

`numeric`

)

Power of test, equal to 1 minus type-II-risk (\(\beta\)).- alternative
(

`string`

)

string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

## Examples

```
size_corr(r1 = 0.95, r0 = 0.9, alpha = 0.025, power = 0.8, alternative = "greater")
#>
#> Sample size determination for testing Pearson's Correlation
#>
#> Call: size_corr(r1 = 0.95, r0 = 0.9, alpha = 0.025, power = 0.8, alternative = "greater")
#>
#> optimal sample size: n = 64
#>
#> r1:0.95 r0:0.9 alpha:0.025 power:0.8 alternative:greater
```