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Plot Statistics on Data

Source: R/plot_data.R
plot_stat.Rd

Plots panels that contain a set of densities for each level of the specified factor The densities represent the predicted data across the posterior, the vertical lines represent the real data.

Usage

plot_stat(
  input,
  post_predict = NULL,
  prior_predict = NULL,
  stat_fun,
  stat_name = NULL,
  subject = NULL,
  factors = NULL,
  n_cores = 1,
  n_post = 50,
  quants = c(0.025, 0.5, 0.975),
  functions = NULL,
  layout = NA,
  to_plot = c("data", "posterior", "prior")[1:2],
  use_lim = c("data", "posterior", "prior")[1:2],
  legendpos = c("topleft", "top"),
  posterior_args = list(),
  prior_args = list(),
  ...
)

Arguments

input

Either an emc object or a data frame, or a list of such objects.

post_predict

Optional posterior predictive data (matching columns) or list thereof.

prior_predict

Optional prior predictive data (matching columns) or list thereof.

stat_fun

A function that can be applied to the data and returns a single value or a vector of values.

stat_name

The name of the calculated quantity

subject

Subset the data to a single subject (by index or name).

factors

Character vector of factor names to aggregate over; defaults to plotting full data set ungrouped by factors if NULL.

n_cores

Number of CPU cores to use if generating predictives from an emc object.

n_post

Number of posterior draws to simulate if needed for predictives.

quants

Numeric vector of credible interval bounds (e.g. c(0.025, 0.975)).

functions

A function (or list of functions) that create new columns in the datasets or predictives

layout

Numeric vector used in par(mfrow=...); use NA for auto-layout.

to_plot

Character vector: any of "data", "posterior", "prior".

use_lim

Character vector controlling which source(s) define xlim.

legendpos

Character vector controlling the positions of the legends

posterior_args

Optional list of graphical parameters for posterior lines/ribbons.

prior_args

Optional list of graphical parameters for prior lines/ribbons.

...

Other graphical parameters for the real data lines.

Value

an invisible data frame with the stat applied to the real data, posterior predictives and/or prior predictives

Examples

# For example plot the observed and predicted response accuracy
# Can also apply more sophisticated statistics
drt <- function(data) diff(tapply(data$rt,data[,c("E")],mean))
plot_stat(samples_LNR, stat_fun = drt, n_post = 10, stat_name = "RT diff Speed - A/N")