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Plot Conditional Accuracy Functions (CAFs)

Source: R/plotting.R
plot.cafs.Rd

This function generates a plot of Conditional Accuracy Functions (CAFs). It can display observed and predicted values, making it useful for assessing model fit or exploring observed data.

Usage

# S3 method for class 'cafs'
plot(
  x,
  ...,
  conds = NULL,
  col = NULL,
  xlim = NULL,
  ylim = c(0, 1),
  xlab = "Bins",
  ylab = NULL,
  pch = 21,
  lty = 1,
  type = "l",
  legend = NULL,
  legend_pos = "bottomright"
)

Arguments

x

a data.frame, containing CAFs, typically resulting from a call to calc_stats.

...

additional arguments passed to the plot, graphics::points, and graphics::legend functions. Oftentimes, this will (unfortunately) lead to an error due to a clash of arguments.

conds

character vector, specifying the conditions to plot. Defaults to all unique conditions.

col

Character vector, specifying colors for each condition. If a single color is provided, it will be repeated for each condition.

xlim, ylim

numeric vectors of length 2, specifying the x and y axis limits.

xlab, ylab

character, labels for the x and y axes.

pch

integer, specifying the plotting symbol for observed data points.

lty

integer, line type for the predicted CAFs.

type

character, type of plot for the predicted CAFs.

legend

character vector, specifying legend labels corresponding to the conditions in the CAFs. Defaults to the condition names.

legend_pos

character, specifying the position of the legend on the plot.

Value

Nothing (NULL; invisibly)

Details

The plot.cafs function allows for a quick investigation of CAFs, including options for color, symbols, and line types for different data sources (observed vs. predicted). When the supplied data.frame includes multiple IDs, CAFs are aggregated across IDs before plotting.

Examples

# Example 1: Only model predictions ---------------------------------------
# get a cafs data.frame for demonstration purpose
a_model <- dmc_dm(t_max = 1.5, dt = .0025, dx = .0025)
cafs <- calc_stats(a_model, type = "cafs")

# call the plot function with default values
plot(cafs)


# make the plot a little bit more pretty
plot(cafs,
  col = c("green", "red"),
  ylim = c(0.5, 1)
)


# Example 2: Model predictions and observed data --------------------------
obs_data(a_model) <- dmc_synth_data
cafs <- calc_stats(a_model, type = "cafs")
plot(cafs)

# Note: The model was not fitted to the data set, thus observed data and
# model predictions don't match


# Example 3: Only observed data -------------------------------------------
cafs <- calc_stats(dmc_synth_data, type = "cafs")
plot(cafs)