make.wide and make.wide.full generate a $n$ x $q - 1$ matrix from an $n$ x $2$ column subset of a data frame storing the results of a conjoint measurement experiment, where $n$ is the number of trials and $q$ is the number of levels per dimension in the stimulus set tested. Currently, make.wide.full is limited to data sets with only 2 stimulus dimensions. The columns code covariates for all but the first stimulus level, which is constrained to be 0, along each dimension. These columns take the value 0 unless one of the stimuli in the trial corresponded to a level along that dimension, in which case it takes a 1 or a -1, depending on which of the two stimuli represented that level. If both stimuli represent the same level for a dimension, then they cancel out and the column contains a 0. This function is used for each dimension along which the stimuli vary to create a design matrix for each dimension. The final design matrix is constructed inside the mlcm method by putting together the design matrices from each dimension.make.wide(d)make.wide.full(d)
mlcm, and not typically exploited by the casual user.