Create an instance of a fitting problem

The fitting_problem() function is the first step for fitting a reference sample to known control totals with MultiLevelIPF. All algorithms (see ml_fit()) expect an object created by this function (or optionally processed with flatten_ml_fit_problem()).

The special_field_names() function is useful for the field_names argument to fitting_problem.

fitting_problem(
  ref_sample,
  controls = list(individual = individual_controls, group = group_controls),
  field_names,
  individual_controls,
  group_controls,
  prior_weights = NULL
)

is.fitting_problem(x)

# S3 method for fitting_problem
format(x, ...)

# S3 method for fitting_problem
print(x, ...)

special_field_names(
  groupId,
  individualId,
  individualsPerGroup = NULL,
  count = NULL
)

Arguments

ref_sample	The reference sample
controls	Control totals, by default initialized from the individual_controls and group_controls arguments
field_names	Names of special fields, construct using special_field_names()
individual_controls, group_controls	Control totals at individual and group level, given as a list of data frames where each data frame defines a control
prior_weights	Prior (or design) weights at group level; by default a vector of ones will be used, which corresponds to random sampling of groups
x	An object
...	Ignored.
groupId, individualId	Name of the column that defines the ID of the group or the individual
individualsPerGroup	Obsolete.
count	Name of control total column in control tables (use first numeric column in each control by default).

Value

An object of class fitting_problem, essentially a named list with the following components:

refSample

The reference sample, a data.frame.

controls

A named list with two components, individual and group. Each contains a list of controls as data.frames.

fieldNames

A named list with the names of special fields.

Examples

# Create example from Ye et al., 2009

# Provide reference sample
ye <- tibble::tribble(
  ~HHNR, ~APER, ~HH_VAR, ~P_VAR,
  1,     3,     1,       1,
  1,     3,     1,       2,
  1,     3,     1,       3,
  2,     2,     1,       1,
  2,     2,     1,       3,
  3,     3,     1,       1,
  3,     3,     1,       1,
  3,     3,     1,       2,
  4,     3,     2,       1,
  4,     3,     2,       3,
  4,     3,     2,       3,
  5,     3,     2,       2,
  5,     3,     2,       2,
  5,     3,     2,       3,
  6,     2,     2,       1,
  6,     2,     2,       2,
  7,     5,     2,       1,
  7,     5,     2,       1,
  7,     5,     2,       2,
  7,     5,     2,       3,
  7,     5,     2,       3,
  8,     2,     2,       1,
  8,     2,     2,       2
)
ye
#> # A tibble: 23 x 4
#>     HHNR  APER HH_VAR P_VAR
#>    <dbl> <dbl>  <dbl> <dbl>
#>  1     1     3      1     1
#>  2     1     3      1     2
#>  3     1     3      1     3
#>  4     2     2      1     1
#>  5     2     2      1     3
#>  6     3     3      1     1
#>  7     3     3      1     1
#>  8     3     3      1     2
#>  9     4     3      2     1
#> 10     4     3      2     3
#> # … with 13 more rows

# Specify control at household level
ye_hh <- tibble::tribble(
  ~HH_VAR, ~N,
  1,       35,
  2,       65
)
ye_hh
#> # A tibble: 2 x 2
#>   HH_VAR     N
#>    <dbl> <dbl>
#> 1      1    35
#> 2      2    65

# Specify control at person level
ye_ind <- tibble::tribble(
  ~P_VAR, ~N,
  1,      91,
  2,      65,
  3,      104
)
ye_ind
#> # A tibble: 3 x 2
#>   P_VAR     N
#>   <dbl> <dbl>
#> 1     1    91
#> 2     2    65
#> 3     3   104

ye_problem <- fitting_problem(
  ref_sample = ye,
  field_names = special_field_names(
    groupId = "HHNR", individualId = "PNR", count = "N"
  ),
  group_controls = list(ye_hh),
  individual_controls = list(ye_ind)
)
ye_problem
#> An object of class fitting_problem
#>   Reference sample: 23 observations
#>   Control totals: 1 at individual, and 1 at group level

fit <- ml_fit_dss(ye_problem)
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