twins: Twins
In scoringTools: Credit Scoring Tools

Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs Reject Inference using the Twins technique. Note that this technique has no theoretical foundation.

1	twins(xf, xnf, yf)

`xf`	The matrix of financed clients' characteristics to be used in the scorecard.
`xnf`	The matrix of not financed clients' characteristics to be used in the scorecard (must be the same in the same order as xf!).
`yf`	The matrix of financed clients' labels

This function performs the Twins method on the data. When provided with labeled observations (x^\ell,y), it first fits the logistic regression model p_θ of x^\ell on y, then fits the logistic regression model p_ω of X on the binomial random variable denoting the observation of the data Z. We use predictions of both models on the labeled observations to construct a "meta"-score based on logistic regression which predicted probabilities are used to reweight samples and construct the final score p_η.

List containing the model using financed clients only, the model of acceptance and the model produced using the Twins method.

Adrien Ehrhardt

Enea, M. (2015), speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets, https://CRAN.R-project.org/package=speedglm Ehrhardt, A., Biernacki, C., Vandewalle, V., Heinrich, P. and Beben, S. (2018), Reject Inference Methods in Credit Scoring: a rational review,

glm, speedglm

# We simulate data from financed clients
df <- generate_data(n = 100, d = 2)
xf <- df[, -ncol(df)]
yf <- df$y
# We simulate data from not financed clients (MCAR mechanism)
xnf <- generate_data(n = 100, d = 2)[, -ncol(df)]
twins(xf, xnf, yf)