Iterative proportional fitting to scale seed values to multiple target subtotals.
ip_fit_sl(datatable, targets, assumptions, datatable.value.name = "value", target.value.names = "value", assumption.value.names = c("value", "value_min", "value_max"), assumption.drop.names = c("Notes"), max.error = 0.01, max.iterations = 25, minmax.smash.param = 1/3, save.tars = FALSE, show.messages = TRUE)
datatable | A data frame of values to be scaled to targets. |
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targets | A list of data frames containing subtotal targets for the |
assumptions | A single data frame containing data belonging to |
target.value.names | The names of the series in |
assumption.value.names | The names of the series in |
assumption.drop.names | The names of the series in |
max.error | The maximum total absolute difference allowed between final scaled values and targets.
Iterative scaling will complete once the error is below this threshold or |
max.iterations | The maximum number of iterations of scaling. Iterative scaling with end once this value is reached, even if the error is above |
minmax.smash.param | Numeric value of 0 < x < 1. Following an out-of-bounds occurence for |
datatable.value.names | The name of the series in |
A dataframe with the same dimensionality as datatable
, with all values scaled to the subtotals specified in each data frame in targets
and meeting criteria supplied in assumptions
.