iterake: Iterative raking

In ttrodrigz/iterake: Tools for iterative raking

Iterative raking

Description

This function utilizes an iterative process known as Raking or RIM (Random Iterative Method) weighting, which allows the user to adjust multiple characteristics simultaneously without knowing the relationship between those characteristics. This iterative fitting algorithm is rooted in the mathematical model developed by Deming & Stephan (1940).

Usage

iterake(universe, permute = FALSE, control = control_iterake())

Arguments

universe	Output object created with the universe() function.
permute	Whether to test all possible orders of categories in universe and keep the most efficient (TRUE) or to test categories in the order listed in universe only (default, FALSE). Setting this to TRUE will increase the run time by a factor of the factorial of the number of weighting categories.
control	Controls for the raking algorithm created with control_iterake().

Details

The algorithm begins by assigning a temporary weight of 1 for each case. It then calculates the weighting factor of the first group supplied in universe() by taking the ratio of the target proportions of that weighting category to the weighted proportions of that variable in the data. (While it is taking a weighed proportion, it is effectively unweighted at this time since the temporary weights are currently all set to 1.)

After the weighting factors are calculated and assigned to each respondent, the new weights are created by multiplying the existing weights by the weighting factor. This process is repeated for each of the categories passed to universe().

At this point, the sum of the absolute values of the difference between the target and actual proportions are calculated. If this value is less than the threshold set in control_iterake(), then the algorithm has converged and stops. Otherwise, it continues to cycle through the weighting categories until either (a) the algorithm converges, (b) it reaches the maximum number of iterations (set with max_iter), or (c) the algorithm gets stuck where the sum of the absolute values of the differences oscillates between getting smaller and larger (set with max_stuck).

There are times when the usage of this weighting approach is not advisable. If there is a known strong relationship between targets in universe(), this approach will not capture that relationship. If there are either too large a number of targets or targets are too discrepant from the actual sample, convergence may not be possible - though how convergence is defined can be modified in control_iterake(), which can make the process of converging easier or more difficult by changing the number of iterations or the max/min weight factor allowed.

There is also a permute argument that can be supplied to iterake(), and when set to TRUE it will assess every order of targets in universe() possible, and select as the winner the one that converges or has the highest effective N.

Value

A list that currently includes 12 objects:

• universe - This is a copy of the universe object originally passed to iterake

• control - This is a copy of the control object originally passed to iterake

• status - Character stating the outcome of the run - one of success, ⁠max iter⁠, or ⁠max stuck⁠

• delta_log - Numeric vector listing the sum of absolute differences between the target and actual proportions for each iteration

• counter - The number of iterations that were ran

• stuck_counter - The number of times the sum of absolute differences oscillated between decreasing and increasing

• stuck_delta - The sum of absolute differences between the target and actual proportions once max_stuck is reached

• cat_order - The order of targets used to generate weights

• delta - The sum of absolute differences between the target and actual proportions for the last iteration ran

• permute - This is a copy of the permute parameter originally passed to iterake

• results - Numeric vector of the final generated weights

• stats - A tibble of summary statistics of the resulting weights, containing the following information:

  • unweighted, weighted, and effective N

  • loss and efficiency of weights

  • mean, median, min, and max of weights

Examples

iterake(
    universe = universe(
        data = mtcars,
        category(
            name = "cyl",
            groups = c(4, 6, 8),
            targets = c(0.3, 0.3, 0.4)
        ),
        category(
            name = "vs",
            groups = c(0, 1),
            targets = c(1/2, 1/2)
        )
    ),
    permute = FALSE,
    control = control_iterake()
)

ttrodrigz/iterake documentation built on March 27, 2024, 12:48 a.m.