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 crossvalidation 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.
YouWu Lin, Nan Xiao, LiLi Wang, ChuanQuan Li, and QingSong Xu (2017). Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data. Chemometrics and Intelligent Laboratory Systems 168, 6271. 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$Vsel#> [1] 1 2 3 4 5 6 7 8 9 10# make predictions y.pred = predict(fit, x.test) # compute evaluation metric RMSEP, Q2 and MAE for the test set perf = OHPL.RMSEP(fit, x.test, y.test) perf$RMSEP#> [1] 4.328435perf$Q2#> [1] 0.4259523perf$MAE#> [1] 3.404655