syn_da: Generation of Synthetic Data Utilizing Data Augmentation

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Generation of Synthetic Data Utilizing Data Augmentation

Description

This function generates synthetic data utilizing data augmentation (Jiang et al., 2022; Grund et al., 2022). Continuous and ordinal variables can be handled. The order of the synthesized variables can be defined using the argument syn_vars.

Usage

syn_da(dat, syn_vars=NULL, fix_vars=NULL, ord_vars=NULL, da_noise=0.5,
   use_pls=TRUE, ncomp=20, exact_regression=TRUE, exact_marginal=TRUE,
   imp_maxit=5)

Arguments

dat	Original dataset
syn_vars	Vector with variable names that should be synthesized
fix_vars	Vector with variable names that are held fixed in the synthesis
ord_vars	Vector with ordinal variables that are treated as factors when modeled as predictors in the regression model
da_noise	Proportion of variance (i.e., unreliability) that is added as noise in data augmentation. The argument can be numeric or a vector, depending on whether it is made variable-specific.
use_pls	Logical indicating whether partial least squares (PLS) should be used for dimension reduction
ncomp	Number of PLS factors
exact_regression	Logical indicating whether residuals are forced to be uncorrelated with predictors in the synthesis model
exact_marginal	Logical indicating whether marginal distributions of the variables should be preserved
imp_maxit	Number of iterations in the imputation if the original dataset contains missing values

Value

A list with entries

dat_syn	generated synthetic data
dat2	Data frame containing original and synthetic data
...	more entries

References

Grund, S., Luedtke, O., & Robitzsch, A. (2022). Using synthetic data to improve the reproducibility of statistical results in psychological research. Psychological Methods. Epub ahead of print. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000526")}

Jiang, B., Raftery, A. E., Steele, R. J., & Wang, N. (2022). Balancing inferential integrity and disclosure risk via model targeted masking and multiple imputation. Journal of the American Statistical Association, 117(537), 52-66. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2021.1909597")}

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Generate synthetic data with item responses and covariates
#############################################################################

data(data.ma09, package="miceadds")
dat <- data.ma09

# fixed variables in synthesis
fix_vars <- c("PV1MATH", "SEX","AGE")
# ordinal variables in synthesis
ord_vars <- c("FISCED", "MISCED", items)
# variables that should be synthesized
syn_vars <- c("HISEI", "FISCED", "MISCED", items)

#-- synthesize data
mod <- miceadds::syn_da( dat=dat0, syn_vars=syn_vars, fix_vars=fix_vars,
            ord_vars=ord_vars, da_noise=0.5, imp_maxit=2, use_pls=TRUE, ncomp=20,
            exact_regression=TRUE, exact_marginal=TRUE)
#- extract synthetic dataset
mod$dat_syn

## End(Not run)

miceadds documentation built on May 29, 2024, 11:05 a.m.