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https://github.com/philmikejones/rakeR

Tools for easy spatial microsimulation (raking) in R
https://github.com/philmikejones/rakeR

cran ipf raker raking spatial-microsimulation

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Tools for easy spatial microsimulation (raking) in R

Lists

README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```



# rakeR



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Create a spatial microsimulated data set in R using iterative proportional

fitting ('raking').



## Install



Install the latest stable version from CRAN:



```{r install-cran, eval=FALSE, include=TRUE}

install.packages("rakeR")

```



Or install the development version with `devtools`:



```{r install-rakeR, eval=FALSE, include=TRUE}

# Obtain devtools if you don't already have it installed

# install.packages("devtools")



# Install rakeR development version from GitHub

devtools::install_github("philmikejones/rakeR")

```



Load the package with:



```{r load-rakeR}

library("rakeR")

```



## Overview



`rakeR` has three main functions.

The first stage is always to use `rk_weight()` to produce a matrix of fractional weights.

This matrix stores weights for each individual in each zone.



From this weight matrix, `rakeR` has functions to create fractional weights (`rk_extract()`) or integerised cases (`rk_integerise()`), depending on your needs and use cases.

Fractional (`rk_extract()`ed) weights are generally more accurate, while integer cases are probably the most intuitive to use and are useful as inputs

for further modeling.



To create fractional weights use `rk_weight()` then `rk_extract()`, and to produce integerised weights use `rk_weight()` then `rk_integerise()`.



## Inputs



To perform the weighting you should supply two data frames.

One data frame should contain the constraint information (`cons`) with counts per category for each zone (e.g. census counts).

The other data frame should contain the individual--level data (`inds`), i.e. one row per individual.



In addition, it is necessary to supply a character vector with the names of the constraint variables in `inds` (`vars`).

This is so that `rakeR` knows which are the contraint variables and which variables it should be simulating as an output.



Below are examples of `cons`, `inds`, and `vars`.



```{r data}

cons <- data.frame(

  "zone"      = letters[1:3],

  "age_0_49"  = c(8, 2, 7),

  "age_gt_50" = c(4, 8, 4),

  "sex_f"     = c(6, 6, 8),

  "sex_m"     = c(6, 4, 3),

  stringsAsFactors = FALSE

)



inds <- data.frame(

  "id"     = LETTERS[1:5],

  "age"    = c("age_gt_50", "age_gt_50", "age_0_49", "age_gt_50", "age_0_49"),

  "sex"    = c("sex_m", "sex_m", "sex_m", "sex_f", "sex_f"),

  "income" = c(2868, 2474, 2231, 3152, 2473)

)



vars <- c("age", "sex")

```



It is _essential_ that the unique levels in the constraint variables in the

`inds` data set match the variables names in the `cons` data set.

For example, `age_0_49` and `age_gt_50` are variable names in `cons` and the unique levels of the `age` variable in `inds` precisely match these:



```{r labels-test}

all.equal(

  levels(inds$age), colnames(cons[, 2:3])  # cons[, 1] is the id column

)

```



Without this, the functions do not know how to match the `inds` and `cons` data

and will fail so as not to return spurious results.



## `rk_weight()`



(Re-)weighting is done with `rk_weight()` which returns a data frame of raw weights.



```{r weight}

weights <- rk_weight(cons = cons, inds = inds, vars = vars)

weights

```



The raw weights tell you how frequently each individual (`A`-`E`) should appear

in each zone (`a`-`c`).

The raw weights are useful when validating and checking performance of the

model, so it can be necessary to save these separately.

They aren't very useful for analysis however, so we can `rk_extract()` or `rk_integerise()` them into a useable form.



## `rk_extract()`



`rk_extract()` produces aggregated totals of the simulated data for each category in each zone.

`rk_extract()`ed data is generally more accurate than `rk_integerise()`d data, although the user should be careful this isn't spurious precision based on context and knowledge of the domain.

Because `rk_extract()` creates a column for each level of each variable, numerical variables (e.g. income) must be removed or `cut()` (otherwise the result would include a new column for each unique numerical value):



```{r extract}

inds$income <- cut(inds$income, breaks = 2, include.lowest = TRUE,

                   labels = c("low", "high"))



ext_weights <- rk_extract(weights, inds = inds, id = "id")

ext_weights

```



`rk_extract()` returns one row per zone, and the total of each category (for

example female and male, or high and low income) will match the known

population.



## `rk_integerise()`



The `rk_integerise()` function produces a simulated data frame populated with simulated individuals.

This is typically useful when:



* You need to include numerical variables, such as income in the example.

* You want individual cases to use as input to a dynamic or agent-based model.

* You want 'case studies' to illustrate characteristics of individuals in an

area.

* Individual-level data is more intuitive to work with.



```{r integerise}

int_weights <- rk_integerise(weights, inds = inds)

int_weights[1:6, ]

```



`rk_integerise()` returns one row per case, and the number of rows will match

the known population (taken from `cons`).



## `rk_rake()`



`rk_rake()` is a wrapper for `rk_weight() %>% rk_extract()` or

`rk_weight() %>% rk_integerise()`.

This is useful if the raw weights (from `rk_weight()`) are not required.

The desired output is specified with the `output` argument, which can be

specified with `"fraction"` (the default) or `"integer"`.

The function takes the following arguments in all cases:



* `cons`

* `inds`

* `vars`

* `output` (default `"fraction"`)

* `iterations` (default 10)



Additional arguments are required depending on the output requested.

For `output = "fraction"`:



* `id`



For `output = "integer"`:



* `method` (default `"trs"`)

* `seed` (default 42)



Details of these context-specific arguments can be found in the

respective documentation for `rk_integerise()` or `rk_extract()`.



```{r rake-int-exaple}

rake_int <- rk_rake(cons, inds, vars, output = "integer",

                 method = "trs", seed = 42)

rake_int[1:6, ]

```



```{r rake-frac-example}

rake_frac <- rk_rake(cons, inds, vars, output = "fraction", id = "id")

rake_frac

```



## Contributing



Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md).

By participating in this project you agree to abide by its terms.



## Issues and feedback



Feedback on the API,

[bug reports/issues](https://github.com/philmikejones/rakeR/issues),

and pull requests are very welcome.



## Acknowledgements



Many of the functions in this package are based on code written by

[Robin Lovelace](https://github.com/Robinlovelace) and

[Morgane Dumont](https://github.com/modumont) for their book

[*Spatial Microsimulation with R* (2016), Chapman and Hall/CRC Press](https://www.crcpress.com/Spatial-Microsimulation-with-R/Lovelace-Dumont/p/book/9781498711548).



Their book is an excellent resource for learning

about spatial microsimulation and understanding what's going on under the hood

of this package.



The book and code are licensed under the terms below:



Copyright (c) 2014 Robin Lovelace



Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.



The [rewighting (ipfp) algorithm](https://github.com/awblocker/ipfp) is

written by Andrew Blocker.



The [`wrswoR` package](http://krlmlr.github.io/wrswoR/) used for fast sampling

without replacement is written by Kirill Müller.



Thanks to [Tom Broomhead](http://mhs.group.shef.ac.uk/members/tom-broomhead/)

for his feedback on error handling and suggestions on function naming, to [Andrew Smith](https://github.com/virgesmith) for bug fixes, and Derrick Atherton for suggestions, feedback, and testing.



Data used in some of the examples and tests ('cakeMap') are anonymised data from the [Adult Dental Health Survey](https://data.gov.uk/dataset/adult_dental_health_survey), used under terms of the [Open Government Licence](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/).



## Contact



philmikejones at gmail dot com



## License



Copyright 2016-18 Phil Mike Jones.



rakeR is free software: you can redistribute it and/or modify

it under the terms of the GNU General Public License as published by

the Free Software Foundation, either version 3 of the License, or

(at your option) any later version.



rakeR is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

GNU General Public License for more details.



You should have received a copy of the GNU General Public License

along with rakeR. If not, see .