CRAN Package Check Results for Package RODM

Last updated on 2020-08-07 01:49:58 CEST.

Check Details

Version: 1.1
Check: DESCRIPTION meta-information
Result: NOTE
    Package listed in more than one of Depends, Imports, Suggests, Enhances:
     'RODBC'
    A package should be listed in only one of these fields.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.1
Check: dependencies in R code
Result: NOTE
    'library' or 'require' call to 'RODBC' which was already attached by Depends.
     Please remove these calls from your code.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.1
Check: Rd line widths
Result: NOTE
    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.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64