synMicrodata (version 2.1.3)

readData: 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 readData_passed
print(x, ...)

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.

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.

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)