| Title: | Easy Spatial Microsimulation (Raking) in R |
| Version: | 0.2.1 |
| Date: | 2017-10-10 |
| Description: | Functions for performing spatial microsimulation ('raking') in R. |
| Depends: | R (≥ 3.4.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| Imports: | ipfp, wrswoR |
| Suggests: | testthat, readr |
| URL: | https://philmikejones.github.io/rakeR/ |
| BugReports: | https://github.com/philmikejones/rakeR/issues |
| NeedsCompilation: | no |
| Packaged: | 2017-10-10 13:39:10 UTC; lw1pj |
| Author: | 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 <philmikejones@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2017-10-10 13:44:46 UTC |
check_constraint
Description
Checks a constraint table for common errors.
Usage
check_constraint(constraint_var, num_zones)
Arguments
constraint_var |
The constraint table to check, usually a data frame |
num_zones |
The number of zones that should be present in the table |
Details
Checks a constraint table for the following common errors:
Ensures all zone codes are unique
Ensures there are the expected number of zones
Ensures all but the zone column are numeric (integer or double)
Value
If no errors are detected the function returns silently. Any errors will stop the function or script to be investigated.
Examples
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)
)
check_constraint(cons, 3) # no errors
check_ind
Description
Checks an individual (survey) variable for common errors.
Usage
check_ind(ind_var)
Arguments
ind_var |
the individual (survey) variable you want to check |
Details
Checks an individual (survey) variable for the following common errors:
That each row sums to 1 (i.e. correctly converted to a dummy variable)
Value
If no errors are detected the function returns silently. Any errors will stop the function or script to be investigated.
Examples
## check_ind(ind_var)
extract
Description
Extract aggregate weights from individual weight table
Usage
extract(weights, inds, id)
Arguments
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 |
Details
Extract aggregate weights from individual weight table, typically produced by rakeR::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, use integerise() instead.
Value
A data frame with zones and aggregated simulated values for each variable
Examples
## Not run
## Use weights object from weights()
## ext_weights <- extract(weights = weights, inds = inds, id = "id")
extract_weights
Description
Deprecated: use rakeR::extract()
Usage
extract_weights(weights, inds, id)
Arguments
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 |
Value
A data frame with zones and aggregated simulated values for each variable
Examples
## Not run
## extract_weights() is deprecated, use extract() instead
integerise
Description
Generate integer cases from numeric weights matrix.
Usage
integerise(weights, inds, method = "trs", seed = 42)
Arguments
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. |
Details
Extracted weights (using rakeR::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::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.
Value
A data frame of integerised cases
Examples
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 <- weight(cons = cons, inds = inds, vars = vars)
weights_int <- integerise(weights, inds = inds)
rake
Description
A convenience function wrapping weight() and extract() or
weight() and integerise()
Usage
rake(cons, inds, vars, output = "fraction", iterations = 10, ...)
Arguments
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" (extract()) or "integer" (integerise()) |
iterations |
The number of iterations to perform. Defaults to 10. |
... |
Additional arguments to pass to depending on desired output:
|
Value
A data frame with extracted weights (if output == "fraction", the default) or integerised cases (if output == "integer")
Examples
## not run
## frac_weights <- rake(cons, inds, vars, output = "fraction",
## id = "id")
## int_weight <- rake(cons, inds, vars, output = "integer",
## method = "trs", seed = "42")
simulate
Description
Deprecated: integerise() %>% simulate() has been replaced by simply integerise() to be consistent with extract().
Usage
simulate(...)
Arguments
... |
arguments previously passed to simulate() |
Value
Returns an error if used. Just use integerise()
weight
Description
Produces fractional weights using the iterative proportional fitting algorithm.
Usage
weight(cons, inds, vars = NULL, iterations = 10)
Arguments
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 |
Details
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
consor individual IDinds).constraints
indsor constraint categorycons.-
indscan 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 inds variables.
The columns in cons must be in alphabetical order because these are
created alphabetically when they are 'spread' in the individual–level data.
Value
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.
Examples
# 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 <- weight(cons = cons, inds = inds, vars = vars)
print(weights)