`R/plot-methods.R`

`plot.pense_cvfit.Rd`

Plot the cross-validation performance or the coefficient path for fitted penalized elastic net S- or LS-estimates of regression.

# S3 method for pense_cvfit plot(x, what = c("cv", "coef.path"), alpha = NULL, se_mult = 1, ...)

x | fitted estimates with cross-validation information. |
---|---|

what | plot either the CV performance or the coefficient path. |

alpha | If |

se_mult | if plotting CV performance, multiplier of the estimated SE. |

... | currently ignored. |

Other functions for plotting and printing:
`plot.pense_fit()`

,
`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]])#> (Intercept) lag.quarterly.revenue price.index #> -6.6475338 0.2411667 -0.6985229 #> income.level market.potential #> 0.7098337 0.9619783# 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)#> (Intercept) lag.quarterly.revenue price.index #> -8.5228825 0.2072828 -0.6946405 #> income.level market.potential #> 0.6778202 1.1430756# ... or at the penalization level with prediction error # statistically indistinguishable from the minimum. coef(cv_results, lambda = '1-se')#> (Intercept) lag.quarterly.revenue price.index #> -8.9377554 0.2066104 -0.6851005 #> income.level market.potential #> 0.6654687 1.1777421