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 ' ' 1autoplot(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, 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,
#> https://docs.ropensci.org/GLMMcosinor/},
#> url = {https://github.com/ropensci/GLMMcosinor},
#> }