Nothing

```
##
## Calculate penalty weights via logistic regression for adaptive lasso with
## binary outcomes
##
penaltyWeightsBinary <- function(X, y, polyTerms, weights)
{
## Compute polynomial-expanded model matrix
X.expand <- expandMatrix(X, polyTerms, intercept = TRUE)
## Compute coefficients
ans <- suppressWarnings(glm.fit(x = X.expand,
y = y,
weights = weights,
family = binomial()))
## Separation check and convergence check (same as in 'glm.fit', but with
## warning messages tailored for this use case)
if (!ans$converged)
warning("'glm.fit' did not converge when computing penalty weights; consider using penwt.method = \"lm\"")
eps <- 10 * .Machine$double.eps
if (any(ans$fitted > 1 - eps) || any(ans$fitted < eps))
warning("fitted probabilities numerically 0 or 1 occurred when computing penalty weights; consider using penwt.method = \"lm\"")
## Calculate weights from coefficients (exclude intercept)
ans <- 1 / abs(ans$coef[-1])
return(ans)
}
```

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