,
Robin Lovelace [aut] (Many functions are based on code by Robin
Lovelace and Morgane Dumont),
Morgane Dumont [aut] (Many functions are based on code by Robin
Lovelace and Morgane Dumont),
Andrew Smith [ctb]| Title: | Easy Spatial Microsimulation (Raking) in R |
|---|---|
| Description: | Functions for performing spatial microsimulation ('raking') in R. |
| Authors: | Phil Mike Jones [aut, cre] ,
Robin Lovelace [aut] (Many functions are based on code by Robin
Lovelace and Morgane Dumont),
Morgane Dumont [aut] (Many functions are based on code by Robin
Lovelace and Morgane Dumont),
Andrew Smith [ctb] |
| Maintainer: | Phil Mike Jones <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.1.9000 |
| Built: | 2025-02-19 03:59:00 UTC |
| Source: | https://github.com/philmikejones/raker |
Deprecated. Use rk_extract() instead.
extract(weights, inds, id)extract(weights, inds, id)
weights |
A weight table, typically produced by rakeR::weight() |
inds |
The individual level data |
id |
The unique id variable in the individual level data (inds), usually the first column |
A data frame with zones and aggregated simulated values for each variable
## Not run: Deprecated. Use rk_extract() ## End(Not run)## Not run: Deprecated. Use rk_extract() ## End(Not run)
Deprecated: use rakeR::rk_extract()
extract_weights(weights, inds, id)extract_weights(weights, inds, id)
weights |
A weight table, typically produced using rakeR::rk_weight() |
inds |
The individual level data |
id |
The unique id variable in the individual level data (inds), usually the first column |
A data frame with zones and aggregated simulated values for each variable
## Not run: extract_weights() is deprecated, use rk_extract() instead ## End(Not run)## Not run: extract_weights() is deprecated, use rk_extract() instead ## End(Not run)
Deprecated. Use rk_integerise()
integerise(weights, inds, method = "trs", seed = 42)integerise(weights, inds, method = "trs", seed = 42)
weights |
A matrix or data frame of fractional weights, typically
provided by |
inds |
The individual–level data (i.e. one row per individual) |
method |
The integerisation method specified as a character string.
Defaults to |
seed |
The seed to use, defaults to 42. |
A data frame of integerised cases
## Not run: Deprecated. Use rk_integerise() ## End(Not run)## Not run: Deprecated. Use rk_integerise() ## End(Not run)
Deprecated. Use rk_rake()
rake(cons, inds, vars, output = "fraction", iterations = 10, ...)rake(cons, inds, vars, output = "fraction", iterations = 10, ...)
cons |
A data frame of constraint variables |
inds |
A data frame of individual–level (survey) data |
vars |
A character string of variables to iterate over |
output |
A string specifying the desired output, either "fraction" (rk_extract()) or "integer" (rk_integerise()) |
iterations |
The number of iterations to perform. Defaults to 10. |
... |
Additional arguments to pass to depending on desired output:
|
A data frame with extracted weights (if output == "fraction", the default) or integerised cases (if output == "integer")
## Not run: Deprecated. Use rk_rake() ## End(Not run)## Not run: Deprecated. Use rk_rake() ## End(Not run)
Extract aggregate weights from individual weight table
rk_extract(weights, inds, id)rk_extract(weights, inds, id)
weights |
A weight table, typically produced by rakeR::weight() |
inds |
The individual level data |
id |
The unique id variable in the individual level data (inds), usually the first column |
Extract aggregate weights from individual weight table, typically produced by rakeR::rk_weight()
Extract cannot operate with numeric variables because it creates a new variable for each unique factor of each variable If you want numeric information, like income, you need to cut() the numeric values, or use integerise() instead.
A data frame with zones and aggregated simulated values for each variable
## Not run ## Use weights object from rk_weight() ## ext_weights <- rk_extract(weights = weights, inds = inds, id = "id")## Not run ## Use weights object from rk_weight() ## ext_weights <- rk_extract(weights = weights, inds = inds, id = "id")
Generate integer cases from numeric weights matrix.
rk_integerise(weights, inds, method = "trs", seed = 42)rk_integerise(weights, inds, method = "trs", seed = 42)
weights |
A matrix or data frame of fractional weights, typically
provided by |
inds |
The individual–level data (i.e. one row per individual) |
method |
The integerisation method specified as a character string.
Defaults to |
seed |
The seed to use, defaults to 42. |
Extracted weights (using rakeR::rk_extract()) are more 'precise' than integerised weights (although the user should be careful this is not spurious precision based on context) as they return fractions. Nevertheless, integerised weights are useful in cases when:
Numeric information (such as income) is required, as this needs to be cut() to work with rakeR::rk_extract()
Simulated 'individuals' are required for case studies of key areas.
Input individual-level data for agent-based or dynamic models are required
The default integerisation method uses the 'truncate, replicate, sample' method developed by Robin Lovelace and Dimitris Ballas http://www.sciencedirect.com/science/article/pii/S0198971513000240
Other methods (for example proportional probabilities) may be implemented at a later date.
A data frame of integerised cases
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), stringsAsFactors = FALSE ) vars <- c("age", "sex") weights <- rk_weight(cons = cons, inds = inds, vars = vars) weights_int <- rk_integerise(weights, inds = inds)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), stringsAsFactors = FALSE ) vars <- c("age", "sex") weights <- rk_weight(cons = cons, inds = inds, vars = vars) weights_int <- rk_integerise(weights, inds = inds)
A convenience function wrapping rk_weight() and rk_extract() or
rk_weight() and rk_integerise()
rk_rake(cons, inds, vars, output = "fraction", iterations = 10, ...)rk_rake(cons, inds, vars, output = "fraction", iterations = 10, ...)
cons |
A data frame of constraint variables |
inds |
A data frame of individual–level (survey) data |
vars |
A character string of variables to iterate over |
output |
A string specifying the desired output, either "fraction" (rk_extract()) or "integer" (rk_integerise()) |
iterations |
The number of iterations to perform. Defaults to 10. |
... |
Additional arguments to pass to depending on desired output:
|
A data frame with extracted weights (if output == "fraction", the default) or integerised cases (if output == "integer")
## Not run: frac_weights <- rake(cons, inds, vars, output = "fraction", id = "id") int_weight <- rake(cons, inds, vars, output = "integer", method = "trs", seed = "42") ## End(Not run)## Not run: frac_weights <- rake(cons, inds, vars, output = "fraction", id = "id") int_weight <- rake(cons, inds, vars, output = "integer", method = "trs", seed = "42") ## End(Not run)
Produces fractional weights using the iterative proportional fitting algorithm.
rk_weight(cons, inds, vars = NULL, iterations = 10)rk_weight(cons, inds, vars = NULL, iterations = 10)
cons |
A data frame containing all the constraints. This should be in the format of one row per zone, one column per constraint category. The first column should be a zone code; all other columns must be numeric counts. |
inds |
A data frame containing individual-level (survey) data. This should be in the format of one row per individual, one column per constraint. The first column should be an individual ID. |
vars |
A character vector of variables that constrain the simulation (i.e. independent variables) |
iterations |
The number of iterations the algorithm should complete. Defaults to 10 |
rk_weight() requires three arguments:
A data frame of constraints (e.g. census tables)
A data frame of individual data (e.g. a survey)
A character vector of constraint variable names
The first column of each data frame should be an ID. The first column of
cons should contain the zone codes. The first column of inds
should contain the individual unique identifier.
Both data frames should only contain:
an ID column (zone ID cons or individual ID inds).
constraints inds or constraint category cons.
inds can optionally contain additional dependent variables
that do not influence the weighting process.
No other columns should be present (the user can merge these back in later).
It is essential that the levels in each inds constraint (i.e. column)
match exactly with the column names in cons. In the example below see
how the column names in cons ('age_0_49', 'sex_f', ...) match exactly
the levels in the appropriate inds variables.
The columns in cons must be arranged in alphabetical order because
these are created alphabetically when they are 'spread' in the
individual-level data.
A data frame of fractional weights for each individual in each zone with zone codes recorded in column names and individual id recorded in row names.
# SimpleWorld 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), stringsAsFactors = FALSE ) # Set variables to constrain over vars <- c("age", "sex") weights <- rk_weight(cons = cons, inds = inds, vars = vars) print(weights)# SimpleWorld 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), stringsAsFactors = FALSE ) # Set variables to constrain over vars <- c("age", "sex") weights <- rk_weight(cons = cons, inds = inds, vars = vars) print(weights)
Deprecated. Use rk_weight()
weight(cons, inds, vars = NULL, iterations = 10)weight(cons, inds, vars = NULL, iterations = 10)
cons |
A data frame containing all the constraints. This should be in the format of one row per zone, one column per constraint category. The first column should be a zone code; all other columns must be numeric counts. |
inds |
A data frame containing individual-level (survey) data. This should be in the format of one row per individual, one column per constraint. The first column should be an individual ID. |
vars |
A character vector of variables that constrain the simulation (i.e. independent variables) |
iterations |
The number of iterations the algorithm should complete. Defaults to 10 |
A data frame of fractional weights for each individual in each zone with zone codes recorded in column names and individual id recorded in row names.
# Deprecated. Use rk_weight()# Deprecated. Use rk_weight()