generate_ordered_data {ocf} | R Documentation |
Generate Ordered Data
Description
Generate a synthetic data set with an ordered non-numeric outcome, together with conditional probabilities and covariates' marginal effects.
Usage
generate_ordered_data(n)
Arguments
n |
Sample size. |
Details
First, a latent outcome is generated as follows:
Y_i^* = g ( X_i ) + \epsilon_i
with:
g ( X_i ) = X_i^T \beta
X_i := (X_{i, 1}, X_{i, 2}, X_{i, 3}, X_{i, 4}, X_{i, 5}, X_{i, 6})
X_{i, 1}, X_{i, 3}, X_{i, 5} \sim \mathcal{N} \left( 0, 1 \right)
X_{i, 2}, X_{i, 4}, X_{i, 6} \sim \textit{Bernoulli} \left( 0, 1 \right)
\beta = \left( 1, 1, 1/2, 1/2, 0, 0 \right)
\epsilon_i \sim logistic (0, 1)
Second, the observed outcomes are obtained by discretizing the latent outcome into three classes using uniformly spaced threshold parameters.
Third, the conditional probabilities and the covariates' marginal effects at the mean are generated using standard textbook formulas. Marginal effects are approximated using a sample of 1,000,000 observations.
Value
A list storing a data frame with the observed data, a matrix of true conditional probabilities, and a matrix of true marginal effects at the mean of the covariates.
Author(s)
Riccardo Di Francesco
References
Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. doi:10.1080/07474938.2024.2429596.
See Also
Examples
## Generate synthetic data.
set.seed(1986)
data <- generate_ordered_data(1000)
head(data$true_probs)
data$me_at_mean
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]
## Fit ocf.
forests <- ocf(Y, X)