This is a wrapper for the newer alsos function
which allows optimal scaling of both dependent and independent
variables. I retain the old operationalization of alsosDV
for backward compatability purposes.
alsosDV(form, data, maxit = 30, level = 2, process = 1, starts = NULL, ...)A formula for a linear model where the dependent variable will be optimally scaled relative to the model.
A data frame.
Maximum number of iterations of the optimal scaling algorithm.
Measurement level of the dependent variable 1=Nominal, 2=Ordinal
Nature of the measurement process: 1=discrete, 2=continuous. Basically identifies whether tied observations will continue to be tied in the optimally scaled variale (1) or whether the algorithm can untie the points (2) subject to the overall measurement constraints in the model.
Optional starting values for the optimal scaling algorithm.
Other arguments to be passed down to lm.
A list with the following elements:
The result of the optimal scaling process
The original data frame with additional columns adding the optimally scaled DV
The iteration history of the algorithm
Original formula
Jacoby, William G. 1999. ‘Levels of Measurement and Political Research: An Optimistic View’ American Journal of Political Science 43(1): 271-301.
Young, Forrest. 1981. ‘Quantitative Analysis of Qualitative Data’ Psychometrika, 46: 357-388.
Young, Forrest, Jan de Leeuw and Yoshio Takane. 1976. ‘Regression with Qualitative and Quantitative Variables: An Alternating Least Squares Method with Optimal Scaling Features’ Psychometrika, 41:502-529.