1d:T77a,# Plot aggregated structure Return a tree plot. ```r plot_TSLA(TSLA.object, X_2, X_2.org, lambda.index, alpha.index) ``` ## Arguments - `TSLA.object`: A fit output from `TSLA.fit()`, or the `TSLA.fit` object in `cv.TSLA()`. - `X_2`: Expanded design matrix in matrix form. - `X_2.org`: Original design matrix in matrix form. - `lambda.index`: Index of the $\lambda$ value selected. - `alpha.index`: Index of the $\alpha$ value selected. The $\alpha$ is the tuning parameter for generalized lasso penalty. ## Returns A plot ## Examples ```r # Load the synthetic data data(ClassificationExample) tree.org <- ClassificationExample$tree.org # original tree structure x2.org <- ClassificationExample$x.org # original design matrix x1 <- ClassificationExample$x1 y <- ClassificationExample$y # response # Do the tree-guided expansion expand.data <- getetmat(tree.org, x2.org) x2 <- expand.data$x.expand # expanded design matrix tree.expand <- expand.data$tree.expand # expanded tree structure # Do train-test split idtrain <- 1:200 x1.train <- as.matrix(x1[idtrain, ]) x2.train <- x2[idtrain, ] y.train <- y[idtrain, ] x1.test <- as.matrix(x1[-idtrain, ]) x2.test <- x2[-idtrain, ] y.test <- y[-idtrain, ] # specify some model parameters set.seed(100) control <- list(maxit = 100, mu = 1e-3, tol = 1e-5, verbose = FALSE) modstr <- list(nlambda = 5, alpha = seq(0, 1, length.out = 5)) simu.cv <- cv.TSLA(y = y.train, as.matrix(x1[idtrain, ]), X_2 = x2.train, treemat = tree.expand, family = 'logit', penalty = 'CL2', pred.loss = 'AUC', gamma.init = NULL, weight = c(1, 1), nfolds = 5, group.weight = NULL, feature.weight = NULL, control = control, modstr = modstr) plot_TSLA(simu.cv$TSLA.fit, x2, x2.org, simu.cv$lambda.min.index, simu.cv$alpha.min.index) ```

plot_TSLA function