# models_linear: Linear model functions. In randomchars42/bioset: Convert a Matrix of Raw Values into Nice and Tidy Data

## Description

Use these functions to calculate a linear model from data, 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_linear`) and interpolate concentrations from the raw values (`interpolate_linear`). Use `plot_linear` to visually inspect goodness of fit.

• `fit_linear`: Calculate a linear model from x and y.

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

• `interpolate_linear`: Inverse `fit_linear` using `model` and calculate x values from y values.

## Usage

 ```1 2 3 4 5``` ```fit_linear(x, y) plot_linear(x, y) interpolate_linear(y, model) ```

## Arguments

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

## Value

• `fit_linear`: The line model.

• `plot_linear`: The plot.

• `interpolate_linear`: The calculated x values.

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# generate data x <- c(1, 3, 4, 7) y_known <- c(3.5, 6.5, 8, 12.5) # x is known for these values y_unknown <- c(5, 9.5, 11) # we will calculate x for those model <- fit_linear(x = x, y = y_known) model plot_linear(x = x, y = y_known) interpolate_linear(y = y_unknown, model) rm(x, y_known, y_unknown, model) ```