simulate.sp_synthesizer

simulate.sp_ts_synthesizer

Draws <code>nsim</code> independent synthetic data sets from a fitted model, for
example to account for synthesis uncertainty with multiple synthetic data
sets (Raghunathan, Reiter and Rubin, 2003).

Generates synthetic versions of numeric, categorical, mixed-type
and time-series data. Marginal distributions are modelled with
interpolated empirical quantile functions and dependence with Gaussian or
Student-t copulas (Sklar, 1959). Discrete and categorical variables enter
the copula through the distributional transform (Ruschendorf, 2009,
<doi:10.1016/j.jspi.2009.05.030>), and their latent correlations are
estimated by maximum likelihood as polychoric (Olsson, 1979,
<doi:10.1007/BF02296207>) and polyserial (Olsson, Drasgow and Dorans,
1982, <doi:10.1007/BF02294164>) correlations. Unordered categories are
placed on their latent coordinate in an order estimated from their
association with the other variables. Missing values are
modelled by missingness indicators inside the copula, so that
missingness may depend on other variables. Supports stratified models,
a closeness parameter with calibration to a target distance or
disclosure-risk level, chunked generation of very large data sets,
optional hybrid copula and conditional CART or GAM synthesis for
one continuous response (requiring 'rpart' or 'mgcv'), training-only
automatic selection of the conditional engine, experimental nominal
conditional synthesis of one unordered categorical response (requiring
'nnet'), copula vector-autoregressive and bootstrap time-series synthesis,
augmentation of small training sets for machine learning, and random
generation from multivariate distributions and elliptical and
Archimedean copulas. Fidelity, utility and disclosure risk are assessed
with marginal distances, association matrices, propensity score
mean-squared error (Snoke et al., 2018, <doi:10.1111/rssa.12358>),
confidence-interval overlap, correct attribution probability and
distance to closest record, with 'ggplot2' visualisations. Data can be
read from and written to CSV, TSV, RDS, RData, Parquet, Feather, HDF5,
JSON, NDJSON, Excel, SPSS, Stata, SAS and fst files. The
empirical-quantile and copula approach builds on ideas of the
'synthesizer' package by Mark van der Loo.

Bogdan Oancea

SynthesizerPlus

Synthetic Data Generation with Copulas, Evaluation and
Multi-Format I/O

simulate.sp_synthesizer function

<dl><dt>object</dt>
<dd>A fitted <code>sp_synthesizer</code> or <code>sp_ts_synthesizer</code>.</dd>
<dt>nsim</dt>
<dd>Number of synthetic data sets.</dd>
<dt>seed</dt>
<dd>Optional random seed.</dd>
<dt>...</dt>
<dd>Passed to <code>generate()</code>, e.g. <code>n</code>.</dd></dl>

Arguments

Simulate several synthetic data sets — simulate.sp_synthesizer

<dl>

<dt>object</dt>
<dd>A fitted <code>sp_synthesizer</code> or <code>sp_ts_synthesizer</code>.</dd>


<dt>nsim</dt>
<dd>Number of synthetic data sets.</dd>


<dt>seed</dt>
<dd>Optional random seed.</dd>


<dt>...</dt>
<dd>Passed to <code>generate()</code>, e.g. <code>n</code>.</dd>

</dl>

simulate.sp_synthesizer: Simulate several synthetic data sets

Description

Usage

Value

Arguments

References

Examples