Compositional (version 4.7)

Cross-validation for the LASSO Kullback-Leibler divergence based regression: Cross-validation for the LASSO Kullback-Leibler divergence based regression

Description

Cross-validation for the LASSO Kullback-Leibler divergence based regression.

Usage

cv.lasso.klcompreg(y, x, alpha = 1, nfolds = 10, folds = NULL,
seed = FALSE, graph = FALSE)

Arguments

y

A numerical matrix with compositional data with or without zeros.

x

A matrix with the predictor variables.

alpha

The elastic net mixing parameter, with \(0 \leq \alpha \leq 1\). The penalty is defined as a weighted combination of the ridge and of the Lasso regression. When \(\alpha=1\) LASSO is applied, while \(\alpha=0\) yields the ridge regression.

nfolds

The number of folds for the K-fold cross validation, set to 10 by default.

folds

If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.

seed

If seed is TRUE the results will always be the same.

graph

If graph is TRUE (default value) a filled contour plot will appear.

Value

The outcome is the same as in the R package glmnet. The extra addition is that if "graph = TRUE", then the plot of the cross-validated object is returned. The contains the logarithm of \(\lambda\) and the deviance. The numbers on top of the figure show the number of set of coefficients for each component, that are not zero.

Details

The K-fold cross validation is performed in order to select the optimal value for \(\lambda\), the penalty parameter in LASSO.

References

Friedman, J., Hastie, T. and Tibshirani, R. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, Vol. 33(1), 1-22.

See Also

link{lasso.klcompreg}, lassocoef.plot, kl.compreg, ols.compreg, alfa.pcr, alfa.knn.reg

Examples

# NOT RUN {
library(MASS)
y <- rdiri( 214, runif(4, 1, 3) )
x <- as.matrix( fgl[, 2:9] )
x <- x / rowSums(x)
mod <- cv.lasso.klcompreg(y, x)
# }