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MLCM (version 0.4.1)

Maximum Likelihood Conjoint Measurement

Description

Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact.

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Version

Install

install.packages('MLCM')

Monthly Downloads

245

Version

0.4.1

License

GPL (>= 2)

Maintainer

Ken Knoblauch

Last Published

February 12th, 2014

Functions in MLCM (0.4.1)

summary.mlcm

Summary Method for mlcm objects
binom.diagnostics

Diagnostics for Binary GLM
BumpyGlossy

Conjoint Measurement Data for Bumpiness and Glossiness
mlcm

Fit Conjoint Measurement Models by Maximum Likelihood
anova.mlcm

Analysis of Deviance for Maximum Likelihood Conjoint Measurement Model Fits
as.mlcm.df

Coerce data frame to mlcm.df
make.wide

Create data frame for Fitting Conjoint Measurment Models by glm
boot.mlcm

Resampling of an Estimated Conjoint Measurement Scale
plot.mlcm

Plot an mlcm Object
predict.mlcm

Predict Method for MLCM Objects
fitted.mlcm

Fitted Responses for a Conjoint Measurement Scale
plot.mlcm.df

Create Conjoint Proportion Plot from mlcm.df Object
logLik.mlcm

Extract Log-Likelihood from mlcm Object
MLCM-package

Maximum Likelihood Conjoint Measurement