alsos {DAMisc} | R Documentation |
Estimates the Alternating Least Squares Optimal Scaling (ALSOS) solution for qualitative variables.
alsos(
os_form,
raw_form = ~1,
data,
scale_dv = FALSE,
maxit = 30,
level = 2,
process = 1,
starts = NULL,
...
)
os_form |
A two-sided formula including the independent variables to be scaled on the left-hand side. Optionally, the dependent variable can also be scaled. |
raw_form |
A right-sided formula with covariates that will not be scaled. |
data |
A data frame. |
scale_dv |
Logical indicating whether the dependent variable should be optimally scaled. |
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. |
starts |
Optional starting values for the optimal scaling algorithm. |
... |
Other arguments to be passed down to |
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 |
Dave Armstrong and Bill Jacoby
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.