| lassoCV {chemometrics} | R Documentation |
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 <P.Filzmoser@tuwien.ac.at>
References
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
See Also
Examples
data(PAC)
# takes some time: # res <- lassoCV(y~X,data=PAC,K=5,fraction=seq(0.1,0.5,by=0.1))