lassoCV: CV for Lasso regression

In chemometrics: Multivariate Statistical Analysis in Chemometrics

CV for Lasso regression

Description

Performs cross-validation (CV) for Lasso regression and plots the results in order to select the optimal Lasso parameter.

Usage

lassoCV(formula, data, K = 10, fraction = seq(0, 1, by = 0.05), trace = FALSE, 
plot.opt = TRUE, sdfact = 2, legpos = "topright", ...)

Arguments

formula	formula, like y~X, i.e., dependent~response variables
data	data frame to be analyzed
K	the number of segments to use for CV
fraction	fraction for Lasso parameters to be used for evaluation, see details
trace	if 'TRUE', intermediate results are printed
plot.opt	if TRUE a plot will be generated that shows optimal choice for "fraction"
sdfact	factor for the standard error for selection of the optimal parameter, see details
legpos	position of the legend in the plot
...	additional plot arguments

Details

The parameter "fraction" is the sum of absolute values of the regression coefficients for a particular Lasso parameter on the sum of absolute values of the regression coefficients for the maximal possible value of the Lasso parameter (unconstrained case), see also lars. The optimal fraction is chosen according to the following criterion: Within the CV scheme, the mean of the SEPs is computed, as well as their standard errors. Then one searches for the minimum of the mean SEPs and adds sdfact*standarderror. The optimal fraction is the smallest fraction with an MSEP below this bound.

Value

cv	MSEP values at each value of fraction
cv.error	standard errors for each value of fraction
SEP	SEP value for each value of fraction
ind	index of fraction with optimal choice for fraction
sopt	optimal value for fraction
fraction	all values considered for fraction

Author(s)

Peter Filzmoser <[e-mail]>

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

cv.lars, lassocoef

Examples

data(PAC)
# takes some time: # res <- lassoCV(y~X,data=PAC,K=5,fraction=seq(0.1,0.5,by=0.1))

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.