cv.OHPL: Cross-Validation for Ordered Homogeneity Pursuit Lasso In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Description

This function uses cross-validation to help select the optimal number of variable groups and the value of gamma.

Usage

 ```1 2 3``` ```cv.OHPL(X.cal, y.cal, maxcomp, gamma = seq(0.1, 0.9, 0.1), X.test, y.test, cv.folds = 5L, G = 30L, type = c("max", "median"), scale = TRUE, pls.method = "simpls") ```

Arguments

 `X.cal` Predictor matrix (training) `y.cal` Response matrix with one column (training) `maxcomp` Maximum number of components for PLS `gamma` A vector of the gamma sequence between (0, 1). `X.test` X.test Predictor matrix (test) `y.test` y.test Response matrix with one column (test) `cv.folds` Number of cross-validation folds `G` Maximum number of variable groups `type` Find the maximum absolute correlation (`"max"`) or find the median of absolute correlation (`"median"`). Default is `"max"`. `scale` Should the predictor matrix be scaled? Default is `TRUE`. `pls.method` Method for fitting the PLS model. Default is `"simpls"`. See the details section in `plsr` for all possible options.

Value

A list containing the optimal model, RMSEP, Q2, and other evaluation metrics. Also the optimal number of groups to use in group lasso.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```data("wheat") X = wheat\$x y = wheat\$protein n = nrow(wheat\$x) set.seed(1001) samp.idx = sample(1L:n, round(n * 0.7)) X.cal = X[samp.idx, ] y.cal = y[samp.idx] X.test = X[-samp.idx, ] y.test = y[-samp.idx] # This needs to run for a while ## Not run: cv.fit = cv.OHPL( x, y, maxcomp = 6, gamma = seq(0.1, 0.9, 0.1), x.test, y.test, cv.folds = 5, G = 30, type = "max") # the optimal G and gamma cv.fit\$opt.G cv.fit\$opt.gamma ## End(Not run) ```

OHPL documentation built on Aug. 9, 2017, 1:02 a.m.