tidyplots (version 0.2.1)

add_curve_fit: Add curve fit

Description

Add curve fit

Usage

add_curve_fit(
  plot,
  dodge_width = NULL,
  method = "loess",
  linewidth = 0.25,
  alpha = 0.4,
  preserve = "total",
  ...
)

Value

A tidyplot object.

Arguments

plot

A tidyplot generated with the function tidyplot().

dodge_width

For adjusting the distance between grouped objects. Defaults to 0.8 for plots with at least one discrete axis and 0 for plots with two continuous axes.

method

Smoothing method (function) to use, accepts either NULL or a character vector, e.g. "lm", "glm", "gam", "loess" or a function, e.g. MASS::rlm or mgcv::gam, stats::lm, or stats::loess. "auto" is also accepted for backwards compatibility. It is equivalent to NULL.

For method = NULL the smoothing method is chosen based on the size of the largest group (across all panels). stats::loess() is used for less than 1,000 observations; otherwise mgcv::gam() is used with formula = y ~ s(x, bs = "cs") with method = "REML". Somewhat anecdotally, loess gives a better appearance, but is \(O(N^{2})\) in memory, so does not work for larger datasets.

If you have fewer than 1,000 observations but want to use the same gam() model that method = NULL would use, then set method = "gam", formula = y ~ s(x, bs = "cs").

linewidth

Thickness of the line in points (pt). Typical values range between 0.25 and 1.

alpha

A number between 0 and 1 for the opacity of an object. A value of 0 is completely transparent, 1 is completely opaque.

preserve

Should dodging preserve the "total" width of all elements at a position, or the width of a "single" element?

...

Arguments passed on to ggplot2::geom_smooth().

Examples

time_course %>%
  tidyplot(x = day, y = score, color = treatment) %>%
  add_curve_fit()

# Changing arguments
time_course %>%
  tidyplot(x = day, y = score, color = treatment) %>%
  add_curve_fit(linewidth = 1)

time_course %>%
  tidyplot(x = day, y = score, color = treatment) %>%
  add_curve_fit(alpha = 0.8)

# Remove confidence interval
time_course %>%
  tidyplot(x = day, y = score, color = treatment) %>%
  add_curve_fit(se = FALSE)