plot_model_fit {mixtur}

Plot model fit against human error data (target errors)

Description

Plot model fit against human error data (target errors)

Usage

plot_model_fit(
  participant_data,
  model_fit,
  model,
  unit = "degrees",
  id_var = "id",
  response_var = "response",
  target_var = "target",
  set_size_var = NULL,
  condition_var = NULL,
  n_bins = 18,
  n_col = 2,
  palette = "Dark2"
)

Arguments

participant_data

A data frame of the participant data, with columns containing: participant identifier ('id_var'); the participants' response per trial ('response_var'); the target value ('target_var'); and, if applicable, the set size of each response ('set_size_var'), and the condition of each response ('condition_var').

model_fit

The model fit object to be plotted against participant data.

model

A string indicating the model that was fit to the data. Currently the options are "2_component", "3_component", "slots", and "slots_averaging".

unit

The unit of measurement in the data frame: "degrees" (measurement is in degrees, from 0 to 360); "degrees_180 (measurement is in degrees, but limited to 0 to 180); or "radians" (measurement is in radians, from pi to 2 * pi, but could also be already in -pi to pi).

id_var

The column name coding for participant id. If the data is from a single participant (i.e., there is no id column) set to "NULL".

response_var

The column name coding for the participants' responses

target_var

The column name coding for the target value

set_size_var

The column name (if applicable) coding for the set size of each response

condition_var

The column name (if applicable) coding for the condition of each response

n_bins

An integer controlling the number of cells / bins used in the plot of the behavioural data.

n_col

An integer controlling the number of columns in the resulting plot.

palette

A character stating the preferred colour palette to use. To see all available palettes, type ?scale_colour_brewer into the console.

Value

The function returns a ggplot2 object visualising the mean observed response error density distribution across participants (if applicable) per set-size (if applicable) and condition (if applicable) together with the model predictions superimposed.