# Cross-Validation for Nonconvex Loss Boosting Cross-validated estimation of the empirical risk/error, can be used for tuning parameter selection. ```r cv.rbst(x, y, K = 10, cost = 0.5, rfamily = c("tgaussian", "thuber", "thinge", "tbinom", "binomd", "texpo", "tpoisson", "clossR", "closs", "gloss", "qloss"), learner = c("ls", "sm", "tree"), ctrl = bst_control(), type = c("loss", "error"), plot.it = TRUE, main = NULL, se = TRUE, n.cores=2,...) ``` ## Arguments - `x`: a data frame containing the variables in the model. - `y`: vector of responses. `y` must be in {1, -1} for binary classification - `K`: K-fold cross-validation - `cost`: price to pay for false positive, 0 < `cost` < 1; price of false negative is 1-`cost`. - `rfamily`: nonconvex loss function types. - `learner`: a character specifying the component-wise base learner to be used: `ls` linear models, `sm` smoothing splines, `tree` regression trees. - `ctrl`: an object of class `bst_control`. - `type`: cross-validation criteria. For `type="loss"`, loss function values and `type="error"` is misclassification error. - `plot.it`: a logical value, to plot the estimated loss or error with cross validation if `TRUE`. - `main`: title of plot - `se`: a logical value, to plot with standard errors. - `n.cores`: The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores. - ``...``: additional arguments. ## Returns object with - **residmat**: empirical risks in each cross-validation at boosting iterations - **mstop**: boosting iteration steps at which CV curve should be computed. - **cv**: The CV curve at each value of mstop - **cv.error**: The standard error of the CV curve - **rfamily**: nonconvex loss function types. ... ## See Also `rbst` ## Author(s) Zhu Wang ## Examples ```r ## Not run: x <- matrix(rnorm(100*5),ncol=5) c <- 2*x[,1] p <- exp(c)/(exp(c)+exp(-c)) y <- rbinom(100,1,p) y[y != 1] <- -1 x <- as.data.frame(x) cv.rbst(x, y, ctrl = bst_control(mstop=50), rfamily = "thinge", learner = "ls", type="lose") cv.rbst(x, y, ctrl = bst_control(mstop=50), rfamily = "thinge", learner = "ls", type="error") dat.m <- rbst(x, y, ctrl = bst_control(mstop=50), rfamily = "thinge", learner = "ls") dat.m1 <- cv.rbst(x, y, ctrl = bst_control(twinboost=TRUE, coefir=coef(dat.m), xselect.init = dat.m$xselect, mstop=50), family = "thinge", learner="ls") ## End(Not run) ```

cv.rbst function