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MLDS (version 0.0.6)

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.

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Version

Install

install.packages('MLDS')

Monthly Downloads

590

Version

0.0.6

License

GPL

Maintainer

Ken Knoblauch

Last Published

August 20th, 2023

Functions in MLDS (0.0.6)

ix.mat2df

Transform data.frame back to Raw Difference Scale Format
Get6pts

Find All 6-point Conditions in data.frame
pmc

Proportion of Misclassifications According to an Estimated MLDS Fit
SwapOrder

Order Stimuli and Adjust Responses from Difference Scaling data.frame
AIC.mlds

Extract AIC from Object of Class 'mlds'
logLik.mlds

Compute Log-Likelihood for an mlds object
mlds

Fit Difference Scale by Maximum Likelihood
make.ix.mat

Create data.frame for Fitting Difference Scale by glm
AutumnLab

Difference Scale Judgement Data Set
predict.mlds

Predict method for MLDS Fits
print.mlds

Difference Scale default print statement
fitted.mlds

Fitted Responses for a Difference Scale
summary.mlds

Summary for a mlds fit
boot.mlds

Resampling of an Estimated Difference Scale
runSampleExperiment

Start and run a Difference Scale Experiment
MLDS-package

~~ MLDS ~~ Maximum Likelihood Differerence Scaling
plot.mlds

Plot a mlds Object
kk

Difference Scale Judgment Data Sets
Rbind

Concatenate Objects of Class 'mlds.df' by Row
lik6pt

Compute Log Likelihood for 6-point Test
DisplayOneTrial

Helper Functions for Perception of Correlation Difference Scale Experiment
simu.6pt

Perform Bootstrap Test on 6-point Likelihood for MLDS FIT