fdars (version 0.3.3)

autoplot.fdata: Create a ggplot for fdata objects

Description

For 1D functional data, plots curves as lines with optional coloring by external variables. For 2D functional data, plots surfaces as heatmaps with contour lines.

Usage

# S3 method for fdata
autoplot(
  object,
  color = NULL,
  alpha = NULL,
  show.mean = FALSE,
  show.ci = FALSE,
  ci.level = 0.9,
  palette = NULL,
  ...
)

Value

A ggplot object.

Arguments

object

An object of class 'fdata'.

color

Optional vector for coloring curves. Can be:

• Numeric vector: curves colored by continuous scale (viridis)

• Factor/character: curves colored by discrete groups

Must have length equal to number of curves.

alpha

Transparency of individual curve lines. Default is 0.7 for basic plots, but automatically reduced to 0.3 when show.mean = TRUE or show.ci = TRUE to reduce visual clutter and allow mean curves to stand out. Can be explicitly set to override the default.

show.mean

Logical. If TRUE and color is categorical, overlay group mean curves with thicker lines (default FALSE).

show.ci

Logical. If TRUE and color is categorical, show pointwise confidence interval ribbons per group (default FALSE).

ci.level

Confidence level for CI ribbons (default 0.90 for 90 percent).

palette

Optional named vector of colors for categorical coloring, e.g., c("A" = "blue", "B" = "red").

...

Additional arguments (currently ignored).

Details

Use autoplot() to get the ggplot object without displaying it. Use plot() to display the plot (returns invisibly).

Examples

library(ggplot2)
# Get ggplot object without displaying
fd <- fdata(matrix(rnorm(200), 20, 10))
p <- autoplot(fd)

# Customize the plot
p + theme_minimal()

# Color by numeric variable
y <- rnorm(20)
autoplot(fd, color = y)

# Color by category with mean and CI
groups <- factor(rep(c("A", "B"), each = 10))
autoplot(fd, color = groups, show.mean = TRUE, show.ci = TRUE)