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 developed using shiny.
For an introduction to the cosinor model, see the getting started vignette.
Existing statistical software for circadian data analyses (including
cosinor
(Sachs 2023) or circacompare
(Parsons et al. 2020)) 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
package which uses
automatic differentiation of the joint likelihood function to
efficiently compute parameter estimates. A detailed explanation of this
process is described here
(Kristensen et al. 2016).
You can install the development version of GLMMcosinor from GitHub with:
# install.packages("remotes")
remotes::install_github("ropensci/GLMMcosinor")
# or, equivalently
install.packages("GLMMcosinor", repos = "https://ropensci.r-universe.dev")
This is a basic example which shows you how to solve a common problem:
library(GLMMcosinor)
model <- cglmm(
vit_d ~ X + amp_acro(time, group = "X", period = 12),
data = vitamind
)
summary(model)
#>
#> Conditional Model
#> Raw model coefficients:
#> estimate standard.error lower.CI upper.CI p.value
#> (Intercept) 29.6897986 0.4583696 28.7914106 30.58819 < 2.22e-16 ***
#> X1 1.9018605 0.7919688 0.3496302 3.45409 0.016331 *
#> X0:main_rrr1 0.9307837 0.6260656 -0.2962822 2.15785 0.137089
#> X1:main_rrr1 6.5102912 0.9303406 4.6868572 8.33373 2.6010e-12 ***
#> X0:main_sss1 6.2009927 0.6701952 4.8874342 7.51455 < 2.22e-16 ***
#> X1:main_sss1 4.8184563 0.8963299 3.0616821 6.57523 7.6259e-08 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Transformed coefficients:
#> estimate standard.error lower.CI upper.CI p.value
#> (Intercept) 29.68979858 0.45836964 28.79141059 30.58819 < 2.22e-16 ***
#> [X=1] 1.90186054 0.79196879 0.34963023 3.45409 0.016331 *
#> [X=0]:amp1 6.27046001 0.66965643 4.95795753 7.58296 < 2.22e-16 ***
#> [X=1]:amp1 8.09946996 0.89570579 6.34391887 9.85502 < 2.22e-16 ***
#> [X=0]:acr1 1.42180626 0.09993555 1.22593618 1.61768 < 2.22e-16 ***
#> [X=1]:acr1 0.63715378 0.11493856 0.41187833 0.86243 2.966e-08 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
autoplot(model, superimpose.data = TRUE)
polar_plot(model)
citation("GLMMcosinor")
#> To cite package 'GLMMcosinor' in publications use:
#>
#> Parsons R, Jayasinghe O, White N, Rawashdeh O (2024). _GLMMcosinor:
#> Fit a Cosinor Model Using a Generalized Mixed Modeling Framework_. R
#> package version 0.2.1.9000, https://docs.ropensci.org/GLMMcosinor/,
#> <https://github.com/ropensci/GLMMcosinor>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {GLMMcosinor: Fit a Cosinor Model Using a Generalized Mixed Modeling Framework},
#> author = {Rex Parsons and Oliver Jayasinghe and Nicole White and Oliver Rawashdeh},
#> year = {2024},
#> note = {R package version 0.2.1.9000,
#> https://docs.ropensci.org/GLMMcosinor/},
#> url = {https://github.com/ropensci/GLMMcosinor},
#> }
Kristensen, Kasper, Anders Nielsen, Casper W. Berg, Hans Skaug, and Bradley M. Bell. 2016. “TMB: Automatic Differentiation and Laplace Approximation.” Journal of Statistical Software 70 (5): 1–21. https://doi.org/10.18637/jss.v070.i05.
Parsons, Rex, Richard Parsons, Nicholas Garner, Henrik Oster, and Oliver Rawashdeh. 2020. “CircaCompare: A Method to Estimate and Statistically Support Differences in Mesor, Amplitude and Phase, Between Circadian Rhythms.” Bioinformatics 36 (4): 1208–12.
Sachs, Michael. 2023. Cosinor: Tools for Estimating and Predicting the Cosinor Model. https://CRAN.R-project.org/package=cosinor.