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glmmcosinor

An R package for flexible cosinor modelling using the glmmTMB framework

https://github.com/ropensci/glmmcosinor

Science Score: 39.0%

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    Found 2 DOI reference(s) in README
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Last synced: 10 months ago · JSON representation

Repository

An R package for flexible cosinor modelling using the glmmTMB framework

Basic Info
Statistics
  • Stars: 3
  • Watchers: 3
  • Forks: 0
  • Open Issues: 4
  • Releases: 2
Created over 3 years ago · Last pushed 11 months ago
Metadata Files
Readme Changelog License Codemeta

README.Rmd 

---
output: 
  github_document
bibliography: vignettes/REFERENCES.bib
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

```{r srr, eval = FALSE, echo = FALSE}
#' @srrstats {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.*
#' @srrstats {G1.1} *Statistical Software should document whether the algorithm(s) it implements are:* - *The first implementation of a novel algorithm*; or - *The first implementation within **R** of an algorithm which has previously been implemented in other languages or contexts*; or - *An improvement on other implementations of similar algorithms in **R***.
#' @srrstats {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).*
#' @srrstats {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.*
```


# GLMMcosinor 


[![cranlogs](https://www.r-pkg.org/badges/version/GLMMcosinor)](https://CRAN.R-project.org/package=GLMMcosinor)
[![R-CMD-check](https://github.com/ropensci/GLMMcosinor/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ropensci/GLMMcosinor/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/ropensci/GLMMcosinor/graph/badge.svg)](https://app.codecov.io/gh/ropensci/GLMMcosinor)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/603_status.svg)](https://github.com/ropensci/software-review/issues/603)



The goal of `{GLMMcosinor}` is to fit a cosinor model to rhythmic data with all
the flexibility and functionality of a generalized linear (mixed-) model (GLM) 
framework using `{glmmTMB}`. 

The package is also accessible via a [web app](https://aushsi.shinyapps.io/GLMMcosinor-shinyapp) 
developed using shiny.

For an introduction to the cosinor model, see the [getting started vignette](https://docs.ropensci.org/GLMMcosinor/articles/GLMMcosinor.html).

Existing statistical software for circadian data analyses (including `cosinor` 
[@sachs2023cosinor] or `circacompare` [@parsons2020circacompare]) allows users 
to fit regression models to rhythmic data, but many are limited due to their 
inability to specify a link function, multiple components, or a hierarchical 
structure. `GLMMcosinor` aims to be comprehensive and flexible and is an 
improvement on other implementations of cosinor model fitting in R or Python.
See table below for features available within currently available methods.

`GLMMcosinor` makes use of the `glmmTMB` package framework for estimation of the
cosinor model. If the model has no random effects, `glmmTMB` uses 
maximum likelihood estimation to estimate the linear coefficients of the model. 
For models with random effects, a Laplace approximation is used to integrate 
over the random effects. This approximation is handled by the
[`TMB`](https://cran.r-project.org/package=TMB)
package which uses automatic differentiation of the joint likelihood function to 
efficiently compute parameter estimates. A detailed explanation of this process 
is described [here](https://doi.org/10.18637/jss.v070.i05) [@Kristensen2016TMB].


```{r, include=FALSE}
source("vignettes/fixtures/methods-table.R")
# flextable::save_as_image(
#   dat,
#   file.path("man", "figures", "methods-table.png")
# )
```

flextable formats 

## Installation

You can install GLMMcosinor from 
[GitHub](https://github.com/) with:

``` r
# dev version
# install.packages("pak")
pak::pkg_install("ropensci/GLMMcosinor")

# or, equivalently
install.packages("GLMMcosinor", repos = "https://ropensci.r-universe.dev")

# latest release version
pak::pkg_install("ropensci/GLMMcosinor@*release") 
```


## Example

This is a basic example which shows you how to solve a common problem:

```{r example}
library(GLMMcosinor)
```


```{r}
model <- cglmm(
  vit_d ~ X + amp_acro(time, group = "X", period = 12),
  data = vitamind
)
summary(model)
```

```{r, eval=FALSE}
autoplot(model, superimpose.data = TRUE)
polar_plot(model)
```

```{r, echo=FALSE}
p1 <- autoplot(model, superimpose.data = TRUE) +
  ggplot2::theme(legend.position = "none")
p2 <- polar_plot(model)
cowplot::plot_grid(p1, p2, nrow = 1, rel_widths = c(0.4, 0.6))
```

## Citation

```{r}
citation("GLMMcosinor")
```

## References

Owner

  • Name: rOpenSci
  • Login: ropensci
  • Kind: organization
  • Email: info@ropensci.org
  • Location: Berkeley, CA

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Dependencies

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DESCRIPTION cran
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  • glmmTMB * imports
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