https://github.com/vankesteren/proportionalfitting.jl

Multidimensional iterative proportional fitting in Julia
https://github.com/vankesteren/proportionalfitting.jl

julia loglinear optimization-algorithms statistics survey-analysis

Last synced: about 1 month ago
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Multidimensional iterative proportional fitting in Julia

• Host: GitHub
• URL: https://github.com/vankesteren/proportionalfitting.jl
• Owner: vankesteren
• License: mit
• Created: 2022-07-25T09:58:59.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-06-27T11:36:42.000Z (over 1 year ago)
• Last Synced: 2024-08-22T05:17:03.944Z (4 months ago)
• Topics: julia, loglinear, optimization-algorithms, statistics, survey-analysis
• Language: Julia
• Homepage: https://vankesteren.github.io/ProportionalFitting.jl
• Size: 252 KB
• Stars: 7
• Watchers: 3
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ProportionalFitting.jl

[![CI](https://github.com/vankesteren/ProportionalFitting.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/vankesteren/ProportionalFitting.jl/actions/workflows/CI.yml) 

[![devdoc](https://img.shields.io/badge/docs-dev-blue.svg)](https://vankesteren.github.io/ProportionalFitting.jl/dev)

[![stabledoc](https://img.shields.io/badge/docs-stable-blue.svg)](https://vankesteren.github.io/ProportionalFitting.jl/stable)

Multidimensional iterative proportional fitting in Julia. 

[ProportionalFitting](https://github.com/vankesteren/ProportionalFitting.jl) implements a multidimensional version of the [factor estimation method](https://en.wikipedia.org/wiki/Iterative_proportional_fitting#Algorithm_2_(factor_estimation)) for performing iterative proportional fitting (also called RAS algorithm, raking, matrix scaling)

> Before version `0.3.0`, this package was called `ItPropFit.jl`.

## Showcase

See the full documentation and getting started [here](https://vankesteren.github.io/ProportionalFitting.jl/).

```julia

using ProportionalFitting

# matrix to be adjusted

X = [40 30 20 10; 35 50 100 75; 30 80 70 120; 20 30 40 50]

# target margins

u = [150, 300, 400, 150]

v = [200, 300, 400, 100]

# Perform iterative proportional fitting

fac = ipf(X, [u, v])

```

```

[ Info: Converged in 8 iterations.

Factors for 2D array:

  [1]: [0.9986403503185242, 0.8833622306385376, 1.1698911437112522, 0.8895042701910321]

  [2]: [1.616160156063788, 1.5431801747375655, 1.771623700829941, 0.38299396265192226]

```

```julia

# compute adjusted matrix

Z = Array(fac) .* X

```

```

4×4 Matrix{Float64}:

 64.5585   46.2325   35.3843   3.82473

 49.9679   68.1594  156.499   25.3742

 56.7219  144.428   145.082   53.7673

 28.7516   41.18     63.0347  17.0337

```

```julia

# check that the margins are indeed [u, v]

ArrayMargins(Z)

```

```

Margins of 2D array:

  [1]: [150.0000000009452, 299.99999999962523, 399.99999999949796, 149.99999999993148]

  [2]: [200.0, 299.99999999999994, 399.99999999999994, 99.99999999999997]

```