calibPop: Calibration of 0/1 weights by Simulated Annealing

Description Usage Arguments Details Value Author(s) References Examples

View source: R/calibPop.R

Description

A Simulated Annealing Algorithm for calibration of synthetic population data available in a simPopObj-object. The aims is to find, given a population, a combination of different households which optimally satisfy, in the sense of an acceptable error, a given table of specific known marginals. The known marginals are also already available in slot 'table' of the input object 'inp'.

Usage

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calibPop(inp, split, temp = 1, eps.factor = 0.05, maxiter = 200,
  temp.cooldown = 0.9, factor.cooldown = 0.85, min.temp = 10^-3,
  nr_cpus = NULL, sizefactor = 2, memory = FALSE, verbose = FALSE)

Arguments

inp

an object of class simPopObj with slot 'table' being non-null! (see addKnownMargins.

split

given strata in which the problem will be split. Has to correspond to a column population data (slot 'pop' of input argument 'inp') . For example split = c("region"), problem will be split for different regions. Parallel computing is performed automatically, if possible.

temp

starting temperatur for simulated annealing algorithm

eps.factor

a factor (between 0 and 1) specifying the acceptance error. For example eps.factor = 0.05 results in an acceptance error for the objective function of 0.05*sum(totals)

maxiter

maximum iterations during a temperature step.

temp.cooldown

a factor (between 0 and 1) specifying the rate at which temperature will be reduced in each step.

factor.cooldown

a factor (between 0 and 1) specifying the rate at which the number of permutations of housholds, in each iteration, will be reduced in each step.

min.temp

minimal temperature at which the algorithm will stop.

nr_cpus

if specified, an integer number defining the number of cpus that should be used for parallel processing.

sizefactor

the factor for inflating the population before applying 0/1 weights

memory

if TRUE simulated annealing is applied in slower but less memory intensive way. Is especially usefull if factor or population is large.

verbose

boolean variable; if TRUE some additional verbose output is provided, however only if split is NULL. Otherwise the computation is performed in parallel and no useful output can be provided.

Details

Calibrates data using simulated annealing. The algorithm searches for a (near) optimal combination of different households, by swaping housholds at random in each iteration of each temperature level. During the algorithm as well as for the output the optimal (or so far best) combination will be indicated by a logical vector containg only 0s (not inculded) and 1s (included in optimal selection). The objective function for simulated annealing is defined by the sum of absolute differences between target marginals and synthetic marginals (=marginals of synthetic dataset). The sum of target marginals can at most be as large as the sum of target marginals. For every factor-level in “split”, data must at least contain as many entries of this kind as target marginals.

Possible donors are automatically generated within the procedure.

The number of cpus are selected automatically in the following manner. The number of cpus is equal the number of strata. However, if the number of cpus is less than the number of strata, the number of cpus - 1 is used by default. This should be the best strategy, but the user can also overwrite this decision.

Value

Returns an object of class simPopObj with an updated population listed in slot 'pop'.

Author(s)

Bernhard Meindl, Johannes Gussenbauer and Matthias Templ

References

M. Templ, B. Meindl, A. Kowarik, A. Alfons, O. Dupriez (2017) Simulation of Synthetic Populations for Survey Data Considering Auxiliary Information. Journal of Statistical Survey, 79 (10), 1–38. doi: 10.18637/jss.v079.i10

Examples

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data(eusilcS) # load sample data
data(eusilcP) # population data
## Not run: 
## approx. 20 seconds computation time
inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090")
simPop <- simStructure(data=inp, method="direct", basicHHvars=c("age", "rb090"))
simPop <- simCategorical(simPop, additional=c("pl030", "pb220a"), method="multinom", nr_cpus=1)

# add margins
margins <- as.data.frame(
  xtabs(rep(1, nrow(eusilcP)) ~ eusilcP$region + eusilcP$gender + eusilcP$citizenship))
colnames(margins) <- c("db040", "rb090", "pb220a", "freq")
simPop <- addKnownMargins(simPop, margins)

## End(Not run)

# apply simulated annealing
## Not run: 
## long computation time
simPop_adj <- calibPop(simPop, split="db040", temp=1, eps.factor=0.1)

## End(Not run)

statistikat/simPop documentation built on Feb. 21, 2018, 7:25 a.m.