Learn R Programming

MLDS (version 0.5.1)

Maximum Likelihood Difference Scaling

Description

Difference scaling is a method for scaling perceived supra-threshold differences. The package contains functions that allow the user to design and run a difference scaling experiment, to fit the resulting data by maximum likelihood and test the internal validity of the estimated scale.

Copy Link

Version

Install

install.packages('MLDS')

Monthly Downloads

570

Version

0.5.1

License

GPL (>= 2)

Maintainer

Ken Knoblauch

Last Published

August 20th, 2023

Functions in MLDS (0.5.1)

Transform data.frame back to Raw Difference Scale Format

Create data.frame for Fitting Difference Scale by glm

runQuadExperiment

Start and run a Difference Scale Experiment

summary.mlds.bt

Method to Extract Bootstrap Values for MLDS Scale Values

Summary for a mlds fit

Perform Bootstrap Test on 6-point Likelihood for MLDS FIT

Difference Scale default print statement

Concatenate Objects of Class 'mlbs.df' or 'mlds.df' by Row

binom.diagnostics

Diagnostics for Binary GLM

Helper Functions for Perception of Correlation Difference Scale Experiment

Difference Scale Judgement Data Set

~~ MLDS ~~ Maximum Likelihood Differerence Scaling

Order Stimuli and Adjust Responses from Difference Scaling data.frame

Coerces a data.frame to mlds.df

Resampling of an Estimated Difference Scale

Find All 6-point Conditions in data.frame

Difference Scaling of Transparency

Simulate Output of MLDS Experiment

Fitted Responses for a Difference Scale

Compute Log-Likelihood for an mlds object

Proportion of Misclassifications According to an Estimated MLDS Fit

Predict method for MLDS Fits

Compute Log Likelihood for 6-point Test

Difference Scale Judgment Data Sets

Fit Difference Scale by Maximum Likelihood

Plot a mlds Object