Generates synthetic version(s) of a data set. syn.strata performs
stratified synthesis.
syn(data, method = vector("character", length = ncol(data)),
visit.sequence = (1:ncol(data)), predictor.matrix = NULL,
m = 1, k = nrow(data), proper = FALSE,
minnumlevels = -1, maxfaclevels = 60,
rules = NULL, rvalues = NULL,
cont.na = NULL, semicont = NULL,
smoothing = NULL, event = NULL, denom = NULL,
drop.not.used = FALSE, drop.pred.only = FALSE,
default.method = c("normrank", "logreg", "polyreg", "polr"),
numtocat = NULL, catgroups = rep(5, length(numtocat)),
models = FALSE, print.flag = TRUE, seed = "sample", ...)
syn.strata(data, strata = NULL,
minstratumsize = 10 + 10 * length(visit.sequence),
tab.strataobs = TRUE, tab.stratasyn = FALSE,
method = vector("character", length = ncol(data)),
visit.sequence = (1:ncol(data)), predictor.matrix = NULL,
m = 1, k = nrow(data), proper = FALSE,
minnumlevels = -1, maxfaclevels = 60,
rules = NULL, rvalues = NULL,
cont.na = NULL, semicont = NULL,
smoothing = NULL, event = NULL, denom = NULL,
drop.not.used = FALSE, drop.pred.only = FALSE,
default.method = c("normrank", "logreg", "polyreg", "polr"),
numtocat = NULL, catgroups = rep(5,length(numtocat)),
models = FALSE, print.flag = TRUE, seed = "sample", ...)
# S3 method for synds
print(x, …)a data frame or a matrix (n x p) containing the
original data. Observations are in rows and variables are in columns.
a single string or a vector of strings of length
ncol(data) specifying the synthesising method to be
used for each variable in the data. Order of variables is exactly the
same as in data. If specified as a single string, the same method
is used for all variables in a visit sequence unless a data type or
a position in a visit sequence requires a different method.
If method is set to "parametric" the
default synthesising method specified by the default.method argument
are applied. Variables that are transformations of other variables can
be synthesised using a passive method that is specified as a string
starting with ~ (see syn.passive). Variables that need
not to be synthesised have the empty method "". By default all
variables are synthesised using "cart" method, which is
rpart implementation of a CART model (see syn.cart).
See details for more information on method.
a character vector of names of variables or an integer
vector of their column indices specifying the order of synthesis.
The default sequence 1:ncol(data) implies that column variables are
synthesised from left to right. See details for more information.
a square matrix of size ncol(data) specifying
the set of column predictors to be used for each target variable in the row.
Each entry has value 0 or 1. A value of 1 means that the column
variable is used as a predictor for the row variable. Order of
variables is exactly the same as in data. By default all
variables that are earlier in the visit sequence are used as predictors.
For the default visit sequence (1:ncol(data)) the default
predictor.matrix will have values of 1 in the lower triangle.
See details for more information.
number of synthetic copies of the original (observed) data to be
generated. The default is m = 1.
a size of the synthetic data set (k x p),
which can be smaller or greater than the size of the original data
set (n x p). The default is nrow(data) which means
that the number of individuals in the synthesised data is the same
as in the original (observed) data (k = n).
a logical value with default set to FALSE.
If TRUE proper synthesis is conducted.
a minimum number of values a numeric variable should
have to be treated as numeric. Numeric variables with fewer levels
than minnumlevels are changed into factors. If set to -1
(default) numeric variables are left unchanged regardless of the number
of values.
a maximum number of factor levels that can be handled. It can be increased but it may cause computational problems, especially for parametric methods.
a named list of rules for restricted values. Restricted values are those that are determined explicitly by values of other variables. The names of the list elements must correspond to the variables names for which the rules need to be specified.
a named list of the values corresponding to the rules
specified by rules.
a named list of codes for missing values for continuous
variables if different from the R missing data code NA.
The names of the list elements must correspond to the variables names for
which the missing data codes need to be specified.
a named list of values at which semi-continuous variables have spikes. The names of the list elements must correspond to the names of the semi-continuous variables.
a named list specifying smoothing method ("density"
or "") to be used for selected variables. Smoothing can only be
applied to continuous variables synthesised using sample,
ctree, cart, normrank or nested method. The names
of the list elements must correspond to the names of the variables whose values
are to be smoothed. Smoothing is applied to the synthesised values.
For "density" smoothing a Gaussian kernel density estimator is
applied with bandwidth selected using the Sheather-Jones
'solve-the-equation' method (see bw.SJ).
a named list specifying for survival data the names of corresponding event indicators. The names of the list elements must correspond to the names of the survival variables.
a named list specifying for variables to be modelled using binomial regression the names of corresponding denominator variables. The names of the list elements must correspond to the names of the variables to be modelled using binomial regression.
a logical value. If TRUE (default) variables not
used in synthesis are not saved in the synthesised data and are not
included in the corresponding synthesis parameters.
a logical value. If TRUE (default) variables not
synthesised and used as predictors only are not saved in the synthesised
data.
a vector of four strings containing the default
parametric synthesising methods for numerical variables, factors
with two levels, unordered factors with more than two levels
and ordered factors with more than two levels respectively.
They are used when method is set to "parametric" or
when there is an inconsistency between variable type and provided method.
a vector of numbers or names to indicate columns of data
that are to be categorised into factors before synthesis.
After the categorical variables have been synthesised the numerical variables
are synthesised from them by the method syn.nested and are placed in the
same position in the synthetic data as in the original.
The categorised variables are not stored in the synthetic data.
If you want to keep the categorised values you should change
the relevant variables in data before running syn
with the function numtocat.syn()
An integer or a vector of integers of the same length as
numtocat giving the target number of groups into which of the
numeric variables is to be categorised. The function group_var
performs the categorisation.
if TRUE parameters of models fitted to the original data
and used to generate the synthetic values are stored.
if TRUE (default) synthesising history and
information messages will be printed at the console. For silent
computation use print.flag = FALSE.
an integer to be used as an argument for the set.seed().
If no integer is provided, the default "sample" will generate one
and it will be stored. To prevent generating an integer set seed
to NA.
additional arguments to be passed to synthesising functions. See section 'Details' below for more information.
a numeric vector with strata identifiers or a string vector with names of stratifying variable(s).
minimum size of each stratum.
a logical value indicating whether a frequency table of the number of observations in strata in the original data set should be printed.
a logical value indicating whether a frequency table of the number of observations in strata in the synthetic data set(s) should be printed.
an object of class synds; a result of a call to syn.
An object of class synds, which stands for 'synthesised
data set'. It is a list with the following components:
an original call to syn.
number of synthetic versions of the original (observed) data.
a data frame (for m = 1) or a list of m data frames
(for m > 1) with synthetic data set(s).
a vector of synthesising methods applied to each variable in the saved synthesised data.
a vector of column indices of the visiting sequence. The indices refer to the columns in the saved synthesised data.
a matrix specifying the set of predictors used for each variable in the saved synthesised data.
a vector specifying smoothing methods applied to each variable in the saved synthesised data.
a vector of integers specifying for survival data the column indices for corresponding event indicators. The indices refer to the columns in the saved synthesised data.
a vector of integers specifying for variables modelled using binomial regression the column indices for corresponding denominator variables. The indices refer to the columns in the saved synthesised data.
a logical value indicating whether proper synthesis was conducted.
a number of cases in the original data.
a number of cases in the synthesised data.
a list of rules for restricted values applied to the synthetic data.
a list of the values corresponding to the rules
specified by rules.
a list of codes for missing values for continuous variables.
a list of values for semi-continuous variables at which they have spikes.
a logical value indicating whether variables not used in synthesis are saved in the synthesised data and corresponding synthesis parameters.
a logical value indicating whether variables not synthesised and used as predictors only are saved in the synthesised data.
if models = TRUE a named list of estimates of models
fitted to the original data and used to generate the synthetic values is
returned from the $fit component of each method
(e.g. syn.cart()). The list is ordered by the variables position
in the data, and any models used to predict missing values are appended
to the list.
an integer used as a set.seed() argument.
a vector of variable labels for data imported from SPSS using
read.obs().
a list value labels for factors for data imported from SPSS
using read.obs().
a vector of all variable names in the observed data set.
Only variables that are in visit.sequence with corresponding non-empty
method are synthesised. The only exceptions are event indicators. They
are synthesised along with the corresponding time to event variables and should
not be included in visit.sequence. All other variables (not in
visit.sequence or in visit.sequence with a corresponding blank
method) can be used as predictors. Including them in visit.sequence
generates a default predictor.matrix reflecting the order of variables
in the visit.sequence otherwise predictor.matrix has to be
adjusted accordingly. All predictors of the variables that are not in
visit.sequence or are in visit.sequence but with a blank method
are removed from predictor.matrix.
Variables to be synthesised that are not synthesised yet cannot be used
as predictors. Also all variables used in passive synthesis or in restricted
values rules (rules) have to be synthesised before the variables they
apply to.
Mismatch between data type and synthesising method stops execution and
print an error message but numeric variables with number of levels less
than minnumlevels are changed into factors and methods are changed
automatically, if necessary, to methods for categorical variables.
Methods for variables not in a visit sequence will be changed into blank.
The built-in elementary synthesising methods defined by conditional distributions include:
classification and regression trees (CART),
see syn.cart
methods using ensembles of CART trees,
see syn.bag and syn.rf
classification and regression trees (CART)
for duration time data (parametric methods for survival data are
not implemented yet), see syn.survctree
normal linear regression, see syn.norm
normal linear regression preserving the marginal
distribution, see syn.normrank
normal linear regression after
natural logarithmic, square root and cube root transformation of
a dependent variable respectively, see syn.lognorm
logistic regression, see syn.logreg
unordered polytomous regression, see
syn.polyreg
ordered polytomous regression, see syn.polr
predictive mean matching, see syn.pmm
random sample from the observed data,
see syn.sample
function of other synthesised data,
see syn.passive
bootstrap sample within each category of the original
grouping variable, see syn.nested
bootstrap sample within each category of the
crosstabulation of all the predictor variables,
see syn.satcat
These methods use a group of variables that are synthesised together. They must always be together at the start of the visit sequence:
fit a saturated log-linear model,
see syn.catall
fit a log-linear model, defined by its margins, by iterative
proportional fitting see syn.ipf
The functions corresponding to these methods are called syn.method,
where method is a string with the name of a synthesising method.
For instance a function corresponding to ctree function is called
syn.ctree. A new synthesising method can be introduced by writing
a function named syn.newmethod and then specifying method
parameter of syn function as "newmethod".
In order to use "nested" sampling, method parameter of syn
function has to be specified as "nested.varname", where "varname"
is the name of the grouped (less detailed) variable, the only one used in
nested synthesis. A variable synthesised using "nested" method is
excluded from synthesising other variables except when used for "nested"
method.
Additional parameters can be passed to synthesising methods as part of the
dots argument. They have to be named using period-separated method and
parameter name (method.parameter). For instance, in order to set
a minbucket (minimum number of observations in any terminal node of
a CART model) for a ctree synthesising method, ctree.minbucket
has to be specified. The parameters are method-specific and will be used for
all variables to be synthesised using that method. See help for
syn.method for further details about the allowed parameters for
a specific method.
Nowok, B., Raab, G.M and Dibben, C. (2016). synthpop: Bespoke creation of synthetic data in R. Journal of Statistical Software, 74(11), 1-26. 10.18637/jss.v074.i11.
# NOT RUN {
### selection of variables
vars <- c("sex","age","marital","income","ls","smoke")
ods <- SD2011[1:1000, vars]
### default synthesis
s1 <- syn(ods)
s1
### synthesis with default parametric methods
s2 <- syn(ods, method = "parametric", seed = 1)
s2$method
### multiple synthesis of selected variables with customised methods
s3 <- syn(ods, visit.sequence = c(2, 1, 4, 5), m = 2,
method = c("logreg","sample","","normrank","ctree",""),
ctree.minbucket = 10)
summary(s3)
summary(s3, msel = 1:2)
### adjustment to the default predictor matrix
s4.ini <- syn(data = ods, visit.sequence = c(1, 2, 5, 3),
m = 0, drop.not.used = FALSE)
pM.cor <- s4.ini$predictor.matrix
pM.cor["marital","ls"] <- 0
s4 <- syn(data = ods, visit.sequence = c(1, 2, 5, 3),
predictor.matrix = pM.cor)
### handling missing values in continuous variables
s5 <- syn(ods, cont.na = list(income = c(NA, -8)))
### rules for restricted values - marital status of males under 18 should be 'single'
s6 <- syn(ods, rules = list(marital = "age < 18 & sex == 'MALE'"),
rvalues = list(marital = 'SINGLE'), method = "parametric", seed = 1)
with(s6$syn, table(marital[age < 18 & sex == 'MALE']))
### results for default parametric synthesis without the rule
with(s2$syn, table(marital[age < 18 & sex == 'MALE']))
### synthesis with ipf for all variables
s7 <- syn(ods[, 1:3], method = "ipf", numtocat = "age")
### stratified synthesis
s8 <- syn.strata(ods, strata = "sex")
# }
Run the code above in your browser using DataLab