Simulate categorical variables of population data taking relationships
between household members into account. The household structure of the
population data needs to be simulated beforehand using
simStructure().
simRelation(
simPopObj,
relation = "relate",
head = "head",
direct = NULL,
additional,
limit = NULL,
censor = NULL,
maxit = 500,
MaxNWts = 2000,
eps = NULL,
nr_cpus = NULL,
seed = 1,
regModel = NULL,
verbose = FALSE,
method = c("multinom", "ctree", "cforest", "ranger"),
by = "strata"
)An object of class simPopObj containing survey
data as well as the simulated population data including the categorical
variables specified by additional.
a simPopObj containing population and household
survey data as well as optionally margins in standardized format.
a character string specifying the columns of dataS
and dataP, respectively, that define the relationships between the
household members.
a character string specifying the category of the variable given
by relation that identifies the household head.
a character string specifying categories of the variable given
by relation. Simulated individuals with those categories directly
inherit the values of the additional variables from the household head. The
default is NULL such that no individuals directly inherit value from
the household head.
a character vector specifying additional categorical
variables of dataS that should be simulated for the population data.
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.
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.
control parameters to be passed to
nnet::multinom() and nnet::nnet(). See the help file
for nnet::nnet().
a small positive numeric value, or NULL (the default). In
the former case, estimated probabilities smaller than this are assumed to
result from structural zeros and are set to exactly 0.
if specified, an integer number defining the number of cpus that should be used for parallel processing.
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.
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 variables) excluding id-variables, strata variables and household sizes are used for the modeling. This parameter should be used with care because all factors are automatically used as factors internally.
formula-object: users may also specify a formula (class 'formula') that
will be used. Checks are performed that all required variables are available.
If parameter regModel is NULL, only basic household variables are used
in any case.
set to TRUE if additional print output should be shown.
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
"ctree": for using Classification trees
"cforest": for using random forest (implementation in package party)
"ranger": for using random forest (implementation in package ranger)
defining which variable to use as split up variable of the estimation. Defaults to the strata variable.
Andreas Alfons and Bernhard Meindl
The values of a new variable are simulated in three steps, where the second
step is optional. First, the values of the household heads are simulated
with multinomial log-linear models. Second, individuals directly related to
the corresponding household head (as specified by the argument
direct) inherit the value of the latter. Third, the values of the
remaining individuals are simulated with multinomial log-linear models in
which the value of the respective household head is used as an additional
predictor.
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.
simStructure(), simCategorical(),
simContinuous(), simComponents()
data(ghanaS) # load sample data
samp <- specifyInput(
data = ghanaS,
hhid = "hhid",
strata = "region",
weight = "weight"
)
ghanaP <- simStructure(
data = samp,
method = "direct",
basicHHvars = c("age", "sex", "relate")
)
class(ghanaP)
if (FALSE) {
## long computation time ...
ghanaP <- simRelation(
simPopObj = ghanaP,
relation = "relate",
head = "head",
additional = c("nation", "ethnic", "religion"), nr_cpus = 1
)
str(ghanaP)
}
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