batchGenerateCI: Generates multiple classification images by participant or...

In rdotsch/rcicr: Reverse-Correlation Image-Classification Toolbox

Generates multiple classification images by participant or condition

Description

Generate classification image for any reverse correlation task that displays independently generated alternatives.

Usage

batchGenerateCI(
  data,
  by,
  stimuli,
  responses,
  baseimage,
  rdata,
  save_as_png = TRUE,
  targetpath = "./cis",
  label = "",
  antiCI = FALSE,
  scaling = "autoscale",
  constant = 0.1
)

Arguments

data	Data frame
by	String specifying column name that specifies the smallest unit (participant, condition) to subset the data on and calculate CIs for.
stimuli	String specifying column name in data frame that contains the stimulus numbers of the presented stimuli.
responses	String specifying column name in data frame that contains the responses coded 1 for original stimulus selected and -1 for inverted stimulus selected.
baseimage	String specifying which base image was used. Not the file name, but the key used in the list of base images at time of generating the stimuli.
rdata	String pointing to .RData file that was created when stimuli were generated. This file contains the contrast parameters of all generated stimuli.
save_as_png	Boolean stating whether to additionally save the CI as PNG image.
targetpath	Optional string specifying path to save PNGs to (default: ./cis).
label	Optional string to insert in file names of PNGs to make them easier to identify.
antiCI	Optional boolean specifying whether antiCI instead of CI should be computed.
scaling	Optional string specifying scaling method: none, constant, independent or autoscale (default).
constant	Optional number specifying the value used as constant scaling factor for the noise (only works for scaling='constant').

Details

This function saves the classification images by participant or condition as PNG to a folder and returns the CIs.

Value

List of classification image data structures (which are themselves lists of pixel matrix of classification noise only, scaled classification noise only, base image only and combined).

rdotsch/rcicr documentation built on Feb. 5, 2023, 10:15 p.m.