It extracts confidence and prediction bands from the `nls`

model. It is used only for data cleaning.

## Usage

```
predict_bands(
.data,
time_column = "t",
vo2_column = "VO2",
cleaning_level = 0.95,
cleaning_baseline_fit = c("linear", "exponential")
)
```

## Arguments

- .data
The nornalized data retrieved from

`normalize_transitions()`

.- time_column
The name (quoted) of the column containing the time. Depending on the language of your system, this column might not be "t". Therefore, you may specify it here. Default to "t".

- vo2_column
The name (quoted) of the column containing the absolute oxygen uptake (VO2) data. Default to 'VO2'.

- cleaning_level
A numeric scalar between 0 and 1 giving the confidence level for the intervals to be calculated.

- cleaning_baseline_fit
A character indicating what kind of fit to perform for each baseline. Either 'linear' or 'exponential'.

## Value

a tibble containing the following columns:

- x
The provided time data.

- y
The provided VO2 data.

- .fitted
The predicted response for that observation.

- .resid
The residual for a particular point.

- lwr_conf
Lower limit of the confidence band.

- upr_conf
Upper limit of the confidence band.

- lwr_pred
Lower limit of the prediction band.

- upr_pred
Upper limit of the prediction band.