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MLDS (version 0.2-1)

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.

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Version

Install

install.packages('MLDS')

Monthly Downloads

570

Version

0.2-1

License

GPL

Maintainer

Ken Knoblauch

Last Published

August 21st, 2009

Functions in MLDS (0.2-1)

binom.diagnostics

Diagnostics for Binary GLM
lik6pt

Compute Log Likelihood for 6-point Test
SwapOrder

Order Stimuli and Adjust Responses from Difference Scaling data.frame
pmc

Proportion of Misclassifications According to an Estimated MLDS Fit
AutumnLab

Difference Scale Judgement Data Set
predict.mlds

Predict method for MLDS Fits
logLik.mlds

Compute Log-Likelihood for an mlds object
kk

Difference Scale Judgment Data Sets
print.mlds

Difference Scale default print statement
ix.mat2df

Transform data.frame back to Raw Difference Scale Format
as.mlds.df

Coerces a data.frame to mlds.df
boot.mlds

Resampling of an Estimated Difference Scale
runSampleExperiment

Start and run a Difference Scale Experiment
Get6pts

Find All 6-point Conditions in data.frame
rbind.mlds.df

Concatenate Objects of Class 'mlds.df' by Row
MLDS-package

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

Plot a mlds Object
summary.mlds

Summary for a mlds fit
simu.6pt

Perform Bootstrap Test on 6-point Likelihood for MLDS FIT
make.ix.mat

Create data.frame for Fitting Difference Scale by glm
mlds

Fit Difference Scale by Maximum Likelihood
DisplayOneTrial

Helper Functions for Perception of Correlation Difference Scale Experiment
fitted.mlds

Fitted Responses for a Difference Scale