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Plot `CosinorM` and `CosinorM.KDE` Fit with `ggplot2`

Source: R/ggCosinorM.R
ggCosinorM.Rd

Create visualization of a CosinorM or CosinorM.KDE fit using ggplot2. The plot shows the parametric cosinor fit over a fine time grid, optional pointwise confidence bands, observed data points, MESOR line, acrophase verticals, amplitude annotation segments, and labelled parameter values when requested.

Usage

ggCosinorM(
  object,
  labels = TRUE,
  ci = TRUE,
  ci_level = 0.95,
  n = 400,
  point_size = 0.5,
  title_extra = NULL,
  legend.position = "right",
  ...
)

Arguments

object

A fitted model of class CosinorM or CosinorM.KDE.

labels

Logical; Default: TRUE, which places repelled labels on the plot with MESOR, amplitude(s), and acrophase(s).

ci

Logical; Default: TRUE, which computes and draws pointwise parametric confidence bands for the fitted cosinor curve using the model covariance.

ci_level

Numeric scaler. The range of the confidence interval, expressed in numeric value between 0 and 1. Default: 0.95

n

Integer scaler. Number of points on the fine prediction grid used to draw the fitted cosinor and confidence ribbon. Default: 400

point_size

Numeric scaler. Plotting size for observed points. Default: 0.5

title_extra

Optional character string appended to the plot title for extra context.

legend.position

Position of the legend on the plot; default is "right". Other options include "top", "bottom", "left", or a numeric vector of length two specifying x and y coordinates.

...

Additional arguments (currently ignored) kept for future update.

Value

A `ggplot` object representing the cosinor model fit visualization.

See also

ggplot, geom_label_repel, predict

Examples

if (FALSE) { # \dontrun{
# Import data
data (FlyEast)

BdfList <-
  BriefSum (
    df = FlyEast,
    SR = 1 / 60,
    Start = "2017-10-24 13:45:00"
  )

# Let's extract actigraphy data from a single day
df <- BdfList$df
df <- subset (df, df$Date == "2017-10-28")

fit <- CosinorM (
  time = df$Time,
  activity = df$Activity,
  tau = 24,
  method = "OLS"
)


p <- ggCosinorM (
  object = fit,
  labels = TRUE,
  ci = TRUE,
  ci_level = 0.95,
  title_extra = "2017-10-24"
)
print (p)
} # }