compute_margins: Compute margins for a weighting of a multi-level fitting...
In mlfit: Iterative Proportional Fitting Algorithms for Nested Structures

Description Usage Arguments Details Value See Also Examples

View source: R/margins.R

These functions allows checking a fit in terms of the original input data.

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compute_margins(ml_problem, weights, verbose = FALSE)

margin_to_df(controls, count = NULL, verbose = FALSE)

`ml_problem`	A fitting problem created by `ml_problem()` or returned by `flatten_ml_fit_problem()`.
`weights`	A vector with one entry per row of the original reference sample
`verbose`	If `TRUE`, print diagnostic output.
`controls`	Margins as returned by `compute_margins` or as passed to the `controls` parameter of `ml_problem()`.
`count`	Name of control total column, autodetected by default.

compute_margins() computes margins in the format used for the input controls (i.e., as expected by the controls parameter of the ml_problem() function), based on a reference sample and a weight vector.

margins_to_df() converts margins to a data frame for easier comparison.

compute_margins() returns a named list with two components, individual and group. Each contains a list of margins as data.frames.

margins_to_df() returns a data frame with the following columns:

..control.type..: Type of the control total: either individual or group.
..control.name..: Name of the control total, if exists.
..id..: Name of the control category.
..count..: Count of the control category.

ml_fit()

path <- toy_example("Tiny")
problem <- readRDS(path)
fit <- ml_fit(ml_problem = problem, algorithm = "entropy_o")
margins <- compute_margins(problem, fit$weights)
margins

margin_to_df(problem$controls)
margin_to_df(margins)