boot.alsos: Bootstrapping function for the ALSOS algorithm

In DAMisc: Dave Armstrong's Miscellaneous Functions

Description

Executes a non-parametric bootstrap for the alsos algorithm to get uncertainty estimates for the optimally scaled values of the variables.

Usage

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"),
  ...
)

Arguments

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.
R	Number of bootstrap samples to be calculated
conf.level	Level of confidence for the confidence intervals.
return	Whether the aggregated result with percentile confidence intervals, the bootstrap object or both should be returned.
...	Other arguments to be passed down to lm.

Value

A list with either data and/or boot.obj entries.

DAMisc documentation built on Jan. 12, 2022, 1:07 a.m.