plot.pense_fit | R Documentation |
Plot the coefficient path for fitted penalized elastic net S- or LS-estimates of regression.
## S3 method for class 'pense_fit' plot(x, alpha, ...)
x |
fitted estimates. |
alpha |
Plot the coefficient path for the fit with the given hyper-parameter value.
If missing of |
... |
currently ignored. |
Other functions for plotting and printing:
plot.pense_cvfit()
,
prediction_performance()
,
summary.pense_cvfit()
# Compute the PENSE regularization path for Freeny's revenue data # (see ?freeny) data(freeny) x <- as.matrix(freeny[ , 2:5]) regpath <- pense(x, freeny$y, alpha = 0.5) plot(regpath) # Extract the coefficients at a certain penalization level coef(regpath, lambda = regpath$lambda[[1]][[40]]) # What penalization level leads to good prediction performance? set.seed(123) cv_results <- pense_cv(x, freeny$y, alpha = 0.5, cv_repl = 2, cv_k = 4) plot(cv_results, se_mult = 1) # Extract the coefficients at the penalization level with # smallest prediction error ... coef(cv_results) # ... or at the penalization level with prediction error # statistically indistinguishable from the minimum. coef(cv_results, lambda = '1-se')
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