compute_margins {mlfit} | R Documentation |
Compute margins for a weighting of a multi-level fitting problem
Description
These functions allows checking a fit in terms of the original input data.
Usage
compute_margins(ml_problem, weights, verbose = FALSE)
margin_to_df(controls, count = NULL, verbose = FALSE)
Arguments
ml_problem |
A fitting problem created by
|
weights |
A vector with one entry per row of the original reference sample |
verbose |
If |
controls |
Margins as returned by |
count |
Name of control total column, autodetected by default. |
Details
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.
Value
compute_margins()
returns a named list with two components,
individual
and group
. Each contains a list of margins as data.frame
s.
margins_to_df()
returns a data frame with the following columns:
..control.type..
Type of the control total: either
individual
orgroup
...control.name..
Name of the control total, if exists.
..id..
Name of the control category.
..count..
Count of the control category.
See Also
Examples
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)