Return a flattened representation of a multi-level fitting problem instance

This function transforms a multi-level fitting problem to a representation more suitable for applying the algorithms: A matrix with one row per controlled attribute and one column per household, a weight vector with one weight per household, and a control vector.

flatten_ml_fit_problem(
  ml_problem,
  model_matrix_type = c("combined", "separate"),
  verbose = FALSE
)

as_flat_ml_fit_problem(x, model_matrix_type = c("combined", "separate"), ...)

Arguments

ml_problem

A fitting problem created by ml_problem() or returned by flatten_ml_fit_problem().

model_matrix_type

Which model matrix building strategy to use? See details.

verbose

If TRUE, print diagnostic output.

x

An object

...

Further parameters passed to the algorithm

Value

An object of classes flat_ml_fit_problem, essentially a named list.

Details

The standard way to build a model matrix (model_matrix = "combined") is to include intercepts and avoid repeating redundant attributes. A simpler model matrix specification, available via model_matrix = "separate", is suggested by Ye et al. (2009) and required for the ml_fit_ipu() implementation: Here, simply one column per target value is used, which results in a larger model matrix if more than one control is given.

See also

ml_fit()

Examples

path <- toy_example("Tiny")
flat_problem <- flatten_ml_fit_problem(ml_problem = readRDS(path))
flat_problem
#> An object of class flat_ml_fit_problem
#>   Dimensions: 5 groups, 8 target values
#>   Model matrix type: combined
#>   Original fitting problem:
#>   An object of class ml_problem
#>     Reference sample: 23 observations
#>     Control totals: 1 at individual, and 1 at group level
#>     Results for algorithms: entropy_o(1,0), entropy_o(0,1), entropy_o(1,1), entropy, ml_ipf, ipu

fit <- ml_fit_dss(flat_problem)
fit$flat_weights
#> [1]  8.937470 23.448579  2.613950 25.899223 14.347802 11.009562  2.733852
#> [8] 11.009562
fit$weights
#>  [1]  8.937470  8.937470  8.937470 23.448579 23.448579  2.613950  2.613950
#>  [8]  2.613950 25.899223 25.899223 25.899223 14.347802 14.347802 14.347802
#> [15] 11.009562 11.009562  2.733852  2.733852  2.733852  2.733852  2.733852
#> [22] 11.009562 11.009562