Diagonotic plots for the test:
Histogram of \(\{L_i\}\), where \(L_i = resid_i \times h_i\).
\(\{L_i\}\) against the index \(i\), where \(i\) refers to the \(i\)-th observation in the full dataset. Only those \(i\)'s in the test split are drawn. The mean is drawn as a horizontal line. Extremes values under the null can result in bad normal approximation. In this case, consider setting
trim.outlier.hunt=TRUE.Residuals (negative scores) versus the hunted signal. A horizontal segment is drawn between each pair of raw hunted signal and the debiased hunted signal. If debiased gets higher, colored in red; otherwise colored in green. A regression line (blue) with a large positive slope indicates the model is misspecified.
Normalized \(\{L_i\}\) drawn in order.
Usage
# S3 method for class 'dScoreTest'
plot(x, ...)