Maximum Likelihood Conjoint Measurement
Analysis of Deviance for Maximum Likelihood Conjoint Measurement Model...
Coerce data frame to mlcm.df
Diagnostics for Binary GLM
Resampling of an Estimated Conjoint Measurement Scale
Fitted Responses for a Conjoint Measurement Scale
Extract Log-Likelihood from mlcm Object
Create data frame for Fitting Conjoint Measurment Models by glm
Maximum Likelihood Conjoint Measurement
Fit Conjoint Measurement Models by Maximum Likelihood
Create Conjoint Proportion Plot from mlcm.df Object
Plot an mlcm Object
Predict Method for MLCM Objects
Summary Method for mlcm objects
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. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.