slopeExtract: Extract slope

In nick-monto/slopeExtractR: Creates identification functions and extracts slopes from belief-updated normal distributions

Description

This function allows the user extract slopes of the internal category distributions. It first calculates the distributions of the internal categories along the designated VOT space. Then uses these distributions to create an indentification function using a simplied Bayes rule. Then takes the implicit differential along the ID function and pulls the slope closest to the provided boundary percentile.

Usage

slopeExtract(trialParameters, VOTspace, boundary)

Arguments

trialParameters	Takes the output of the selectTrials function.
VOTspace	A list of the parameters for the VOT space. Input as c(1,2,3), where 1 = minimum, 2 = maximum, and 3 = step count.
boundary	The designated percentile at with to extract the slope. This is typically done at the boundary, designated by 0.5. The function pulls the slopes closest to the designated percentile, which may not always be exact.

Value

A dataframe of the extracted slopes for each randomization at each trial. The dataframe inclues the associated VOT, percentile, trial number and randomization. Also returns three lists of dataframes that contains to the probabilities generated by the simplified Bayes rule at the trials indicated in the selectTrials function.

nick-monto/slopeExtractR documentation built on May 17, 2019, 8:21 a.m.