---
title: "Data Transformation in predmicror"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Data Transformation in predmicror}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Consistent Use of Natural Logarithm

In previous versions of `predmicror`, there was an inconsistency in the required data transformation for different models. Some models required the data to be in natural logarithm (`ln`) scale, while others required it to be in base-10 logarithm (`log10`) scale. This could lead to confusion and errors.

To address this issue, all models in `predmicror` have been harmonized to use the **natural logarithm (`ln`)** for the response variable `Y(t)`.

This means that users should always provide the microbial concentration in `ln` scale.

### Converting from `log10` to `ln`

If your data is in `log10` scale, you can easily convert it to `ln` scale using the following formula:

`ln(N) = log(10) * log10(N)`

Here is an example of how to convert a column in a data frame:

```{r}
# Create a sample data frame with log10 data
my_data <- data.frame(
  Time = c(0, 1, 2, 3),
  log10N = c(2, 2.5, 3, 3.5)
)

# Convert the log10N column to lnN
my_data$lnN <- log(10) * my_data$log10N

# Print the updated data frame
print(my_data)
```

By ensuring that all models use a consistent `ln` scale, `predmicror` is now more user-friendly and less prone to errors.