1d:T7ea,# cross-validation for l0ara Does k-fold cross-validation for l0ara, produces a plot, and returns the optimal `lambda` ```r cv.l0ara(x, y, family, lam, measure, nfolds, maxit, eps, seed) ``` ## Arguments - `x`: Input matrix as in `l0ara`. - `y`: Response variable as in `l0ara`. - `family`: Response type as in `l0ara`. - `lam`: A user supplied `lambda` sequence in descending or asecending order. This function does not fit models. To fit a model with given `lam` value, use `l0ara`. - `measure`: Loss function used for corss validation. `measurer="mse"` or `"mae"` for all models. `"measure"="class"` or `"measure"="auc"` only for logsitic regression. - `nfolds`: Number of folds. Default value is 10. Smallest value is 3. - `maxit`: Maximum number of passes over the data for `lambda`. Default value is `1e3`. - `eps`: Convergence threshold. Default value is `1e-4`. - `seed`: Seed of random number generator. ## Returns An object with S3 class "cv.l0ara" containing: - **cv.error**: The mean cross validated error for given lambda sequence - **cv.std**: The estimates of standard error of `cv.error` - **lam.min**: The lambda gives min cv.error - **lambda**: The lambda used - **measure**: Type of measure - **family**: Model used - **x**: Design matrix - **y**: Response variable - **name**: Full name of the measure ## Details This function calls `l0ara` `nfolds` times, each time leaving out `1/nfolds` of the data. The cross-validation error is based on etiher mean square error (`mse`) or mean absolute error (`mae`). ## Examples ```r #' # Linear regression # Generate design matrix and response variable n <- 100 p <- 40 x <- matrix(rnorm(n*p), n, p) beta <- c(1,0,2,3,rep(0,p-4)) noise <- rnorm(n) y <- x%*%beta+noise lam <- c(0.1, 0.3, 0.5) fit <- cv.l0ara(x, y, family="gaussian", lam, measure = "mse") ``` ## See Also `l0ara`, `coef.cv.l0ara`, `plot.cv.l0ara` methods. ## Author(s) Wenchuan Guo , Shujie Ma , Zhenqiu Liu

cv.l0ara function