## Not run:library(h2o)h2o.init()# Import the USArrests dataset into H2O:arrests <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")# Split the dataset into a train and valid set:arrests_splits <- h2o.splitFrame(data = arrests, ratios =0.8, seed =1234)train <- arrests_splits[[1]]valid <- arrests_splits[[2]]# Build and train the model:glrm_model = h2o.glrm(training_frame = train, k =4, loss ="Quadratic", gamma_x =0.5, gamma_y =0.5, max_iterations =700, recover_svd =TRUE, init ="SVD", transform ="STANDARDIZE")# Eval performance:arrests_perf <- h2o.performance(glrm_model)# Generate predictions on a validation set (if necessary):arrests_pred <- h2o.predict(glrm_model, newdata = valid)# Transform the data using the dataset "valid" to retrieve the new coefficients:glrm_transform <- h2o.transform_frame(glrm_model, valid)## End(Not run)