Executes a non-parametric bootstrap for the alsos algorithm to get uncertainty estimates for the optimally scaled values of the variables.
boot.alsos(
os_form,
raw_form = ~1,
data,
scale_dv = TRUE,
maxit = 30,
level = 1,
process = 1,
starts = NULL,
R = 50,
conf.level = 0.95,
return = c("data", "boot.obj", "both"),
...
)A two-sided formula including the independent variables to be scaled on the left-hand side. Optionally, the dependent variable can also be scaled.
A right-sided formula with covariates that will not be scaled.
A data frame.
Logical indicating whether the dependent variable should be optimally scaled.
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.
Number of bootstrap samples to be calculated
Level of confidence for the confidence intervals.
Whether the aggregated result with percentile confidence intervals, the bootstrap object or both should be returned.
Other arguments to be passed down to lm.
A list with either data and/or boot.obj entries.