Perform (repeated) Kfold crossvalidation for pense()
.
adapense_cv()
is a convenience wrapper to compute adaptive PENSE estimates.
pense_cv( x, y, standardize = TRUE, lambda, cv_k, cv_repl = 1, cv_metric = c("tau_size", "mape", "rmspe", "auroc"), fit_all = TRUE, cl = NULL, ... ) adapense_cv(x, y, alpha, alpha_preliminary = 0, exponent = 1, ...)
x 


y  vector of response values of length 
standardize  whether to standardize the 
lambda  optional usersupplied sequence of penalization levels. If given and not 
cv_k  number of folds per crossvalidation. 
cv_repl  number of crossvalidation replications. 
cv_metric  either a string specifying the performance metric to use, or a function to evaluate prediction errors in a single CV replication. If a function, the number of arguments define the data the function receives. If the function takes a single argument, it is called with a single numeric vector of prediction errors. If the function takes two or more arguments, it is called with the predicted values as first argument and the true values as second argument. The function must always return a single numeric value quantifying the prediction performance. The order of the given values corresponds to the order in the input data. 
fit_all  If 
cl  a parallel cluster. Can only be used if 
...  Arguments passed on to

alpha  elastic net penalty mixing parameter with \(0 \le \alpha \le 1\). 
alpha_preliminary 

exponent  the exponent for computing the penalty loadings based on the preliminary estimate. 
a list with components:
lambda
the sequence of penalization levels.
cvres
data frame of average crossvalidated performance.
cv_replications
matrix of crossvalidated performance metrics, one column per replication.
Rows are in the same order as in cvres
.
call
the original call.
estimates
the estimates fitted on the full data. Same format as returned by pense()
.
the object returned by adapense_cv()
has additional components
preliminary
the CV results for the preliminary estimate.
penalty_loadings
the penalty loadings used for the adaptive PENSE estimate.
The builtin CV metrics are
"tau_size"
\(\tau\)size of the prediction error, computed by tau_size()
(default).
"mape"
Median absolute prediction error.
"rmspe"
Root mean squared prediction error.
"auroc"
Area under the receiver operator characteristic curve (actually 1  AUROC). Only sensible for binary responses.
adapense_cv()
is a convenience wrapper which performs 3 steps:
compute preliminary estimates via pense_cv(..., alpha = alpha_preliminary)
,
computes the penalty loadings from the estimate beta
with best prediction performance by
adapense_loadings = 1 / abs(beta)^exponent
, and
compute the adaptive PENSE estimates via pense_cv(..., penalty_loadings = adapense_loadings)
.
pense()
for computing regularized Sestimates without crossvalidation.
coef.pense_cvfit()
for extracting coefficient estimates.
plot.pense_cvfit()
for plotting the CV performance or the regularization path.
Other functions to compute robust estimates with CV:
pensem_cv()
,
regmest_cv()
Other functions to compute robust estimates with CV:
pensem_cv()
,
regmest_cv()
# Compute the adaptive PENSE regularization path for Freeny's # revenue data (see ?freeny) data(freeny) x < as.matrix(freeny[ , 2:5]) ## Either use the convenience function directly ... ada_convenience < adapense_cv(x, freeny$y, alpha = 0.5, cv_repl = 2, cv_k = 4) ## ... or compute the steps manually: # Step 1: Compute preliminary estimates with CV preliminary_estimate < pense_cv(x, freeny$y, alpha = 0, cv_repl = 2, cv_k = 4) plot(preliminary_estimate, se_mult = 1)# Step 2: Use the coefficients with best prediction performance # to define the penality loadings: prelim_coefs < coef(preliminary_estimate, lambda = 'min') pen_loadings < 1 / abs(prelim_coefs[1]) # Step 3: Compute the adaptive PENSE estimates and estimate # their prediction performance. ada_manual < pense_cv(x, freeny$y, alpha = 0.5, cv_repl = 2, cv_k = 4, penalty_loadings = pen_loadings) # Visualize the prediction performance and coefficient path of # the adaptive PENSE estimates (manual vs. automatic) def.par < par(no.readonly = TRUE) layout(matrix(1:4, ncol = 2, byrow = TRUE)) plot(ada_convenience$preliminary) plot(preliminary_estimate) plot(ada_convenience) plot(ada_manual)par(def.par)