Plot a cosinor model — autoplot.cglmm • GLMMcosinor

Given a cglmm model fit, generate a plot of the data with the fitted values. Optionally allows for plotting by covariates

Usage

# S3 method for cglmm
autoplot(
  object,
  ci_level = 0.95,
  x_str,
  type = "response",
  xlims,
  pred.length.out,
  points_per_min_cycle_length = 20,
  superimpose.data = FALSE,
  data_opacity = 0.3,
  predict.ribbon = TRUE,
  ranef_plot = NULL,
  cov_list = NULL,
  quietly = TRUE,
  ...
)

Arguments

object: An cglmm object.
ci_level: The level for calculated confidence intervals. Defaults to 0.95.
x_str: A character vector naming variable(s) to be plotted. Default has no value and plots all groups.
type: A character that will be passed as an argument to predict.cglmm(), specifying the type of prediction (e.g, "response", or "link"). See ?glmmTMB::predict.glmmTMB for full list of possible inputs.
xlims: A vector of length two containing the limits for the x-axis.
pred.length.out: An integer value that specifies the number of predicted data points. The larger the value, the more smooth the fitted line will appear. If missing, uses points_per_min_cycle_length to generate a sensible default value.
points_per_min_cycle_length: Used to determine the number of samples to create plot if pred.length.out is missing.
superimpose.data: A logical. If TRUE, data from the original data used to fit the model (object) will be superimposed over the predicted fit.
data_opacity: A number between 0 and 1 inclusive that controls the opacity of the superimposed data. (Used as the alpha when calling ggplot2::geom_point()).
predict.ribbon: A logical. If TRUE, a prediction interval is plotted.
ranef_plot: Specify the random effects variables that you wish to plot. If not specified, only the fixed effects will be visualised.
cov_list: Specify the levels of the covariates that you wish to plot as a list. For example, if you have two covariants: var1, and var 2, you could fix the level to be plotted as such cov_list = list(var1 = 'a', var2 = 1), where 'a' is a level in 'var1', and 1 is a level in 'var2'. See the examples for a demonstration. If not specified, the reference level of the covariate(s) will be used. points_per_min_cycle_length is the number of points plotted per the minimum cycle length (period) of all cosinor components in the model.
quietly: A logical. If TRUE, shows warning messages when wrangling data and fitting model. Defaults to TRUE.
...: Additional, ignored arguments.

Value

Returns a ggplot object.

Examples

#A simple model
model <- cglmm(
  vit_d ~ X + amp_acro(time, group = "X", period = 12),
  data = vitamind
)
autoplot(model, x_str = "X")



#Plotting a model with various covariates
test_data <- vitamind[vitamind$X == 1,]
test_data$var1 <- sample(c("a", "b", "c"), size = nrow(test_data), replace = TRUE)
test_data$var2 <- rnorm(n = nrow(test_data))

object <- cglmm(
  vit_d ~ amp_acro(time, period = 12) + var1 + var2,
  data = test_data
)
autoplot(object,
         cov_list = list(var1 = 'a',
                         var2 = 1),
         superimpose.data = TRUE)