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MLDS (version 0.1-2)

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

278

Version

0.1-2

License

GPL

Maintainer

Ken Knoblauch

Last Published

August 20th, 2023

Functions in MLDS (0.1-2)

predict.mlds

Predict method for MLDS Fits
SwapOrder

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

Plot a mlds Object
make.ix.mat

Create data.frame for Fitting Difference Scale by glm
print.mlds

Difference Scale default print statement
summary.mlds

Summary for a mlds fit
rbind.mlds.df

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

Fitted Responses for a Difference Scale
pmc

Proportion of Misclassifications According to an Estimated MLDS Fit
ix.mat2df

Transform data.frame back to Raw Difference Scale Format
mlds

Fit Difference Scale by Maximum Likelihood
AIC.mlds

Extract AIC from Object of Class 'mlds'
AutumnLab

Difference Scale Judgement Data Set
simu.6pt

Perform Bootstrap Test on 6-point Likelihood for MLDS FIT
runSampleExperiment

Start and run a Difference Scale Experiment
boot.mlds

Resampling of an Estimated Difference Scale
DisplayOneTrial

Helper Functions for Perception of Correlation Difference Scale Experiment
kk

Difference Scale Judgment Data Sets
lik6pt

Compute Log Likelihood for 6-point Test
logLik.mlds

Compute Log-Likelihood for an mlds object
binom.diagnostics

Diagnostics for Binary GLM
Get6pts

Find All 6-point Conditions in data.frame
MLDS-package

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