simCategorical {simPop}

Simulate categorical variables of population data

Description

Simulate categorical variables of population data. The household structure of the population data needs to be simulated beforehand.

Usage

simCategorical(
  simPopObj,
  additional,
  method = c("multinom", "distribution", "ctree", "cforest", "ranger", "xgboost"),
  limit = NULL,
  censor = NULL,
  maxit = 500,
  MaxNWts = 1500,
  eps = NULL,
  nr_cpus = NULL,
  regModel = NULL,
  seed = 1,
  verbose = FALSE,
  by = "strata",
  model_params = NULL
)

Arguments

simPopObj

a simPopObj containing population and household survey data as well as optionally margins in standardized format.

additional

a character vector specifying additional categorical variables available in the sample object of simPopObj that should be simulated for the population data.

method

a character string specifying the method to be used for simulating the additional categorical variables. Accepted values are "multinom" (estimation of the conditional probabilities using multinomial log-linear models and random draws from the resulting distributions) or "distribution" (random draws from the observed conditional distributions of their multivariate realizations). "ctree" for using Classification trees "cforest" for using random forest (implementation in package party) "ranger" for using random forest (implementation in package ranger) "xgboost" for using xgboost (implementation in package xgboost)

limit

if method is "multinom", this can be used to account for structural zeros. If only one additional variable is requested, a named list of lists should be supplied. The names of the list components specify the predictor variables for which to limit the possible outcomes of the response. For each predictor, a list containing the possible outcomes of the response for each category of the predictor can be supplied. The probabilities of other outcomes conditional on combinations that contain the specified categories of the supplied predictors are set to 0. If more than one additional variable is requested, such a list of lists can be supplied for each variable as a component of yet another list, with the component names specifying the respective variables.

censor

if method is "multinom", this can be used to account for structural zeros. If only one additional variable is requested, a named list of lists or data.frames should be supplied. The names of the list components specify the categories that should be censored. For each of these categories, a list or data.frame containing levels of the predictor variables can be supplied. The probability of the specified categories is set to 0 for the respective predictor levels. If more than one additional variable is requested, such a list of lists or data.frames can be supplied for each variable as a component of yet another list, with the component names specifying the respective variables.

maxit, MaxNWts

control parameters to be passed to multinom and nnet. See the help file for nnet.

eps

a small positive numeric value, or NULL (the default). In the former case and if method is "multinom", estimated probabilities smaller than this are assumed to result from structural zeros and are set to exactly 0.

nr_cpus

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

regModel

allows to specify the variables or model that is used when simulating additional categorical variables. The following choices are available if different from NULL.

• 'basic'only the basic household variables (generated with simStructure) are used.

• 'available'all available variables (that are common in the sample and the synthetic population such as previously generated varaibles) excluding id-variables, strata variables and household sizes are used for the modelling. This parameter should be used with care because all factors are automatically used as factors internally.

• formula-objectUsers may also specify a specifiy formula (class 'formula') that will be used. Checks are performed that all required variables are available.

If method 'distribution' is used, it is only possible to specify a vector of length one containing one of the choices described above. If parameter 'regModel' is NULL, only basic household variables are used in any case.

seed

optional; an integer value to be used as the seed of the random number generator, or an integer vector containing the state of the random number generator to be restored.

verbose

set to TRUE if additional print output should be shown.

by

defining which variable to use as split up variable of the estimation. Defaults to the strata variable.

model_params

NULL or a named list which can contain model specific parameters which will be passed onto the function call for the respective model.

Details

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

An object of class simPopObj containing survey data as well as the simulated population data including the categorical variables specified by argument additional.

Note

The basic household structure needs to be simulated beforehand with the function simStructure.

Author(s)

Bernhard Meindl, Andreas Alfons, Stefan Kraft, Alexander Kowarik, Matthias Templ, Siro Fritzmann

References

B. Meindl, M. Templ, A. Kowarik, 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

A. Alfons, M. Templ (2011) Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods & Applications, 20 (3), 383–407. doi:10.1080/02664763.2013.859237

See Also

simStructure, simRelation, simContinuous, simComponents

Examples

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

## End(Not run)