# CVCL: Confidence limits of a CV for log-normal data In PowerTOST: Power and Sample Size for (Bio)Equivalence Studies

## Description

The function calculates the 1–α confidence limits (either 1-sided or 2-sided) via the χ2 distribution of the error variance the CV is based on.

## Usage

 `1` ```CVCL(CV, df, side = c("upper", "lower", "2-sided"), alpha = 0.05) ```

## Arguments

 `CV` Coefficient of variation as ratio (not percent) `df` degrees of freedom of the CV (error variance) `side` Side(s) to calculate the confidence limits for, defaults to `upper` `alpha` Type I error probability, aka significance level

## Value

Numeric vector of the confidence limits named as `lower CL` and `upper CL`.
In case of the one-sided upper confidence limit the `lower CL` is = 0.
In case of the one-sided lower confidence limit the `upper CL` is = Inf.

D. Labes

## Examples

 ```1 2 3 4 5 6 7``` ```# upper one-sided 95% CL of a CV=0.3 # from a study with df=22 (f.i. a 2x2 crossover with n=24) # default side="upper" since not explicitly given CVCL(0.3, df = 22) # should give: # lower CL upper CL # 0.0000000 0.4075525 ```

### Example output

``` lower CL  upper CL
0.0000000 0.4075525
```

PowerTOST documentation built on Jan. 18, 2021, 5:07 p.m.