| lassoCV {chemometrics} | R Documentation |
Performs cross-validation (CV) for Lasso regression and plots the results in order to select the optimal Lasso parameter.
lassoCV(formula, data, K = 10, fraction = seq(0, 1, by = 0.05), trace = FALSE, plot.opt = TRUE, sdfact = 2, ...)
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 |
... |
additional plot arguments |
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 that is below this bound.
cv |
CV curve 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 |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press. To appear.
data(PAC) res=lassoCV(y~X,data=PAC,K=5,fraction=seq(0.1,0.5,by=0.1))