Rd file 'RODM_apply_model.Rd':
  \examples lines wider than 100 characters:
     data(titanic3, package="PASWR")                                             # Load survival data from Titanic
     ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
     print(svm2$model.apply.results[1:10,])                                # Print example of prediction results
     points(x=svm2$model.apply.results[, "X1"], svm2$model.apply.results[, "PREDICTION"], pch=20, col="red")
             rnorm(100, mean = 4, sd = 1), rnorm(100, mean = 10, sd = 1)) # Create and merge 5 Gaussian distributions
     Y1 <- c(rnorm(100, mean = 1, sd = 2), rnorm(100, mean = 4, sd = 1.5), rnorm(100, mean = 6, sd = 0.5),
     legend(5, -0.5, legend=c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"), pch = rep(20, 5), 

Rd file 'RODM_create_assoc_model.Rd':
  \examples lines wider than 100 characters:
     ards <- satfruit[,c("WH", "BA", "NAR", "COR", "SF", "VI", "PS", "ES", "AF", "CO", "AR", "AL", "OL")]  # Select subset of attributes

Rd file 'RODM_create_dt_model.Rd':
  \examples lines wider than 100 characters:
     data(titanic3, package="PASWR")                                             # Load survival data from Titanic
     ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
     print(dt2$model.apply.results[1:10,])                                  # Print example of prediction results

Rd file 'RODM_create_glm_model.Rd':
  \examples lines wider than 100 characters:
     data(titanic3, package="PASWR")                                             # Load survival data from Titanic
     ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
     train.rows <- length(titanic_train[,1])                                                    # Number of rows
     row.id <- matrix(seq(1, train.rows), nrow=train.rows, ncol=1, dimnames= list(NULL, c("ROW_ID"))) # Row id
     titanic_train <- cbind(row.id, titanic_train)                                                     # Add row id to dataset 
     print(glm2$model.apply.results[1:10,])                                  # Print example of prediction results

Rd file 'RODM_create_kmeans_model.Rd':
  \examples lines wider than 100 characters:
             rnorm(100, mean = 4, sd = 1), rnorm(100, mean = 10, sd = 1)) # Create and merge 5 Gaussian distributions
     Y1 <- c(rnorm(100, mean = 1, sd = 2), rnorm(100, mean = 4, sd = 1.5), rnorm(100, mean = 6, sd = 0.5),
     legend(5, -0.5, legend=c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"), pch = rep(20, 5), 

Rd file 'RODM_create_nb_model.Rd':
  \examples lines wider than 100 characters:
     data(titanic3, package="PASWR")                                             # Load survival data from Titanic
     ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
        data_table_name = "titanic_train", # (in quotes) Data frame or database table containing the input dataset
     print(nb2$model.apply.results[1:10,])                                  # Print example of prediction results

Rd file 'RODM_create_oc_model.Rd':
  \examples lines wider than 100 characters:
             rnorm(100, mean = 4, sd = 1), rnorm(100, mean = 10, sd = 1)) # Create and merge 5 Gaussian distributions
     Y1 <- c(rnorm(100, mean = 1, sd = 2), rnorm(100, mean = 4, sd = 1.5), rnorm(100, mean = 6, sd = 0.5),

Rd file 'RODM_create_svm_model.Rd':
  \examples lines wider than 100 characters:
     Y1 <- c(rnorm(200, mean = 1, sd = 2), rnorm(300, mean = 4, sd = 1.5), rnorm(300, mean = 6, sd = 0.5))
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
     data(titanic3, package="PASWR")                                             # Load survival data from Titanic
     ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
     print(svm2$model.apply.results[1:10,])                                  # Print example of prediction results

Rd file 'RODM_list_dbms_models.Rd':
  \examples lines wider than 100 characters:
     data(titanic3, package="PASWR")                                             # Load survival data from Titanic
     ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
     random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
        data_table_name = "titanic_train", # (in quotes) Data frame or database table containing the input dataset
        data_table_name = "titanic_train", # (in quotes) Data frame or database table containing the input dataset

These lines will be truncated in the PDF manual.