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README.Rmd
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---
title: "Statistical Models for 'SciViews::R' <a href='https://www.sciviews.org/modelit'><img src='man/figures/logo.png' align='right' height='139'/></a>"
output: github_document
---
<!-- Do not edit the README.md file directly. It is generated from README.Rmd. -->
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[](https://github.com/SciViews/modelit/actions/workflows/R-CMD-check.yaml) [](https://codecov.io/gh/SciViews/modelit?branch=main) [](https://cran.r-project.org/package=modelit) [](https://sciviews.r-universe.dev/modelit) [](https://opensource.org/licenses/MIT) [](https://lifecycle.r-lib.org/articles/stages.html#experimental)
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```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>",
fig.path = "man/figures/README-", out.width = "80%")
library(modelit)
library(tabularise)
library(chart)
```
The {modelit} package adds the statistical models to the SciViews::R dialect. It uses both the `fun$type(data = ...., formula)` approach and the enhanced formula allowing to specify arguments with `%arg=%` directly inside the formula. It also takes the variable labels into account in the outputs.
The {modelit} package provides methods (**lm**, **glm**; **nls** objects) for the `chart()` function in the {chart} package and the `tabularise()` function in the {tabularise} package.
## Installation
{modelit} is not available from CRAN yet. You should install it from the [SciViews R-Universe](https://sciviews.r-universe.dev). {chart} is an alternate formula interface to {ggplot2}. {tabularise} produces publication-ready (rich-formatted) tabular output. The {equatags} and {equatiomatic} packages are optional, but they are useful to display equations, both inline in R Markdown/Quarto documents and in {tabularise} tables. {data.io} is useful too because it manages labels and units that {chart} uses. To install these six packages and their dependencies, run the following command in R:
```{r, eval=FALSE}
install.packages(c('modelit', 'chart', 'tabularise', 'equatags', 'equatiomatic', 'data.io'),
repos = c('https://sciviews.r-universe.dev', 'https://cloud.r-project.org'))
```
You can also install the latest development version of {modelit}. Make sure you have the {remotes} R package installed:
```{r, eval=FALSE}
# install.packages("remotes")
remotes::install_github("SciViews/modelit")
```
## Short example
```{r}
library(modelit)
library(tabularise)
library(chart)
```
The trees datasets present diameter, height and volume for Black Cherry Trees (*Prunus serotina*, Ehrh., 1788)
```{r}
data('trees', package = 'datasets')
```
We perform a polynomial regression of the volume of timber as a function of the girth of the trees.
```{r}
trees_lm <- lm(Volume ~ Girth + I(Girth^2), data = trees)
summary(trees_lm)
```
Here is the summary of the model, using `tabularise()`:
```{r, eval=FALSE}
summary(trees_lm) |> tabularise()
```
<img src="man/figures/README-tabularise-chunk.png" width="80%"/>
The model can be viewed using `chart()`:
```{r}
chart(trees_lm)
```
Residual analysis plots can also be carried out with `chart()`.
```{r}
chart$residuals(trees_lm)
```
For further examples, please, refer to the help pages at <https://www.sciviews.org/modelit/>.
## Code of Conduct
Please note that the {modelit} package is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.