ipu {ipfr}

Iterative Proportional Updating

Description

A general case of iterative proportional fitting. It can satisfy two, disparate sets of marginals that do not agree on a single total. A common example is balancing population data using household- and person-level marginal controls. This could be for survey expansion or synthetic population creation. The second set of marginal/seed data is optional, meaning it can also be used for more basic IPF tasks.

Usage

ipu(
  primary_seed,
  primary_targets,
  secondary_seed = NULL,
  secondary_targets = NULL,
  primary_id = "id",
  secondary_importance = 1,
  relative_gap = 0.01,
  max_iterations = 100,
  absolute_diff = 10,
  weight_floor = 1e-05,
  verbose = FALSE,
  max_ratio = 10000,
  min_ratio = 1e-04
)

Arguments

primary_seed

In population synthesis or household survey expansion, this would be the household seed table (each record would represent a household). It could also be a trip table, where each row represents an origin-destination pair.

primary_targets

A named list of data frames. Each name in the list defines a marginal dimension and must match a column from the primary_seed table. The data frame associated with each named list element can contain a geography field (starting with "geo_"). If so, each row in the target table defines a new geography (these could be TAZs, tracts, clusters, etc.). The other column names define the marginal categories that targets are provided for. The vignette provides more detail.

secondary_seed

Most commonly, if the primary_seed describes households, the secondary seed table would describe the persons in each household. Must contain the same primary_id column that links each person to their respective household in primary_seed.

secondary_targets

Same format as primary_targets, but they constrain the secondary_seed table.

primary_id

The field used to join the primary and secondary seed tables. Only necessary if secondary_seed is provided.

secondary_importance

A real between 0 and 1 signifying the importance of the secondary targets. At an importance of 1, the function will try to match the secondary targets exactly. At 0, only the percentage distributions are used (see the vignette section "Target Agreement".)

relative_gap

After each iteration, the weights are compared to the previous weights and the the relative_gap threshold, then the process terminates.

max_iterations

maximum number of iterations to perform, even if relative_gap is not reached.

absolute_diff

Upon completion, the ipu() function will report the worst-performing marginal category and geography based on the percent difference from the target. absolute_diff is a threshold below which percent differences don't matter.

For example, if if a target value was 2, and the expanded weights equaled 1, that's a 100 is only 1.

Defaults to 10.

weight_floor

Minimum weight to allow in any cell to prevent zero weights. Set to .0001 by default. Should be arbitrarily small compared to your seed table weights.

verbose

Print iteration details and worst marginal stats upon completion? Default FALSE.

max_ratio

real number. The average weight per seed record is calculated by dividing the total of the targets by the number of records. The max_scale caps the maximum weight at a multiple of that average. Defaults to 10000 (basically turned off).

min_ratio

real number. The average weight per seed record is calculated by dividing the total of the targets by the number of records. The min_scale caps the minimum weight at a multiple of that average. Defaults to 0.0001 (basically turned off).

Value

a named list with the primary_seed with weight, a histogram of the weight distribution, and two comparison tables to aid in reporting.

References

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf

Examples

hh_seed <- dplyr::tibble(
  id = c(1, 2, 3, 4),
  siz = c(1, 2, 2, 1),
  weight = c(1, 1, 1, 1),
  geo_cluster = c(1, 1, 2, 2)
)

hh_targets <- list()
hh_targets$siz <- dplyr::tibble(
  geo_cluster = c(1, 2),
  `1` = c(75, 100),
  `2` = c(25, 150)
)

result <- ipu(hh_seed, hh_targets, max_iterations = 5)