readData: Read the original datasets

In synMicrodata: Synthetic Microdata Generator

Read the original datasets

Description

Read the original input datasets to be learned for synthetic data generation. The package allows the input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output.

Usage

readData(Y_input, X_input, RandomSeed = 99)

## S3 method for class 'readData_passed'
print(x, ...)

Arguments

Y_input	data.frame consisting of continuous variables of the original data. It must contain only variables of class numeric. Non-numeric columns will cause an error.
X_input	data.frame consisting of categorical variables of the original data. It must contain only variables of class factor (ordered factors are allowed). Character or numeric variables are not converted automatically; non-factor columns will cause an error. Convert them to factors in advance, e.g., X_input[] <- lapply(X_input, factor).
RandomSeed	random seed number.
x	object of class readData_passed; a result of a call to readData().
...	further arguments passed to or from other methods.

Value

readData returns an object of "readData_passed" class.

An object of class "readData_passed" is a list containing the following components:

n_sample	number of records in the input dataset.
p_Y	number of continuous variables.
Y_mat_std	matrix with standardized values of Y_input, with mean 0 and standard deviation 1.
mean_Y_input	mean vectors of original Y_input.
sd_Y_input	standard deviation vectors of original Y_input.
NA_Y_mat	matrix indicating missing values in Y_input.
p_X	number of categorical variables.
D_l_vec	numbers of levels of each categorical variable.
X_mat_std	matrix with the numeric-transformed values of X_input.
levels_X_input	list of levels of each categorical variable.
NA_X_mat	matrix indicating missing values in X_input.
var_names	list containing variable names of X_input and Y_input.
orig_data	original dataset.

See Also

multipleSyn, createModel

Examples

## Example data: split into continuous (Y_input) and categorical (X_input)

### Continuous variables (numeric only)
Y_demo <- data.frame(
  Sepal.Length = iris$Sepal.Length,
  Sepal.Width  = iris$Sepal.Width,
  Petal.Length = iris$Petal.Length,
  Petal.Width  = iris$Petal.Width
)

### Categorical variables (factor)
X_demo_char <- data.frame(
  Species = as.character(iris$Species)
)

## Not run:
## This will produce an error because X_input is not a factor:
# readData(Y_input = Y_demo, X_input = X_demo_char)
## End(Not run)

## Proper conversion of X_input to factor:
X_demo <- data.frame(
  Species = factor(iris$Species)
)

dat_obj <- readData(Y_input = Y_demo, X_input = X_demo)
print(dat_obj)

synMicrodata documentation built on Nov. 26, 2025, 9:06 a.m.