alsosDV: Alternating Least Squares Optimal Scaling

In davidaarmstrong/damisc_nodep: Dave Armstrong's Miscellaneous Functions

Description

Estimates the Alternating Least Squares Optimal Scaling (ALSOS) solution for qualitative dependent variables.

Usage

	alsosDV(form, data, maxit=30, level=2, process=1, starts=NULL,...)

Arguments

formula	A formula with a dependent variable that will be optimally scaled
data	A data frame.
maxit	Maximum number of iterations of the optimal scaling algorithm.
level	Measurement level of the dependent variable 1=Nominal, 2-Ordinal
process	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.
...	Other arguments to be passed down to lm.

Details

alsosDV estimates the Alternating Least Squares Optimal Scaling solution on the dependent variable.

Value

A list with the following elements:

result	The result of the optimal scaling process
data	The original data frame with additional columns adding the optimally scaled DV
iterations	The iteration history of the algorithm
form	Original formula

Author(s)

Dave Armstrong

References

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.

davidaarmstrong/damisc_nodep documentation built on May 15, 2019, 6:25 p.m.