Ordered Homogeneity Pursuit Lasso
This function fits the ordered homogeneity pursuit lasso (OHPL) model.
OHPL(x, y, maxcomp, gamma, cv.folds = 5L, G = 30L, type = c("max",
"median"), scale = TRUE, pls.method = "simpls")x |
Predictor matrix. |
y |
Response matrix with one column. |
maxcomp |
Maximum number of components for PLS. |
gamma |
A number between (0, 1) for generating
the gamma sequence. An usual choice for gamma could be
|
cv.folds |
Number of cross-validation folds. |
G |
Maximum number of variable groups. |
type |
Find the maximum absolute correlation ( |
scale |
Should the predictor matrix be scaled?
Default is |
pls.method |
Method for fitting the PLS model.
Default is |
A list of fitted OHPL model object with performance metrics.
You-Wu Lin, Nan Xiao, Li-Li Wang, Chuan-Quan Li, and Qing-Song Xu (2017). Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data. Chemometrics and Intelligent Laboratory Systems 168, 62-71. https://doi.org/10.1016/j.chemolab.2017.07.004
# generate simulation data dat <- OHPL.sim( n = 100, p = 100, rho = 0.8, coef = rep(1, 10), snr = 3, p.train = 0.5, seed = 1010 ) # split training and test set x <- dat$x.tr y <- dat$y.tr x.test <- dat$x.te y.test <- dat$y.te # fit the OHPL model fit <- OHPL(x, y, maxcomp = 3, gamma = 0.5, G = 10, type = "max") # selected variables fit$Vsel # make predictions y.pred <- predict(fit, x.test) # compute evaluation metric RMSEP, Q2 and MAE for the test set perf <- OHPL.RMSEP(fit, x.test, y.test) perf$RMSEP perf$Q2 perf$MAE
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