# models_lnln: Model functions for data requiring ln-ln-transformation to... In randomchars42/bioset: Convert a Matrix of Raw Values into Nice and Tidy Data

## Description

Use these functions to transform x and y using the natural logarithm and calculate a linear model, plot the model and use it to calculate x-values from the model data and y-values (inverse function).

Those function are intended to be used in set_calc_concentrations / sets_read to be applied to the calibrators (`fit_lnln`) and interpolate concentrations from the raw values (`interpolate_lnln`). Use `plot_lnln` to visually inspect goodness of fit.

• `fit_lnln`: Apply ln to x and y and calculate a linear model from x and y.

• `plot_lnln`: Draw the plot for the model that can be calculated with `fit_lnln`. Uses ggplot2::ggplot if available.

• `interpolate_lnln`: Inverse `fit_lnln` using `model` and calculate x values from y values.

## Usage

 ```1 2 3 4 5``` ```fit_lnln(x, y) plot_lnln(x, y) interpolate_lnln(y, model) ```

## Arguments

 `x` The x coordinates of the points. `y` The y coordinates of the points. `model` The line model.

## Value

• `fit_lnln`: The model.

• `plot_lnln`: The plot.

• `interpolate_lnln`: The calculated x values.

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# generate data x <- c(2.718282, 20.085537, 54.598150, 1096.633158) # x is known for these values y_known <- c(33.11545, 665.14163, 2980.95799, 268337.28652) # we will calculate x for those: y_unknown <- c(148.4132, 13359.7268, 59874.1417) model <- fit_lnln(x = x, y = y_known) model plot_lnln(x = x, y = y_known) interpolate_lnln(y = y_unknown, model) rm(x, y_known, y_unknown, model) ```