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.
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
)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.
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.
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.
Same format as primary_targets, but they constrain
the secondary_seed table.
The field used to join the primary and secondary seed
tables. Only necessary if secondary_seed is provided.
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".)
After each iteration, the weights are compared to the
previous weights and the
the relative_gap threshold, then the process terminates.
maximum number of iterations to perform, even if
relative_gap is not reached.
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.
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.
Print iteration details and worst marginal stats upon
completion? Default FALSE.
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).
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).
a named list with the primary_seed with weight, a
histogram of the weight distribution, and two comparison tables to aid in
reporting.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf
# NOT RUN {
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)
# }
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