CRAN Package Check Results for Package arulesCBA

Last updated on 2020-04-25 01:52:55 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.0 10.75 106.79 117.54 OK
r-devel-linux-x86_64-debian-gcc 1.2.0 9.96 80.98 90.94 OK
r-devel-linux-x86_64-fedora-clang 1.2.0 149.48 OK
r-devel-linux-x86_64-fedora-gcc 1.2.0 152.91 OK
r-devel-windows-ix86+x86_64 1.1.6 36.00 134.00 170.00 OK
r-patched-linux-x86_64 1.2.0 11.11 104.29 115.40 OK
r-patched-osx-x86_64 1.1.6 OK
r-patched-solaris-x86 1.2.0 186.50 NOTE
r-release-linux-x86_64 1.2.0 10.23 92.85 103.08 OK
r-release-windows-ix86+x86_64 1.2.0 26.00 190.00 216.00 OK
r-release-osx-x86_64 1.2.0 OK
r-oldrel-windows-ix86+x86_64 1.2.0 15.00 231.00 246.00 ERROR
r-oldrel-osx-x86_64 1.1.6 OK

Check Details

Version: 1.2.0
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘RWeka’
Flavor: r-patched-solaris-x86

Version: 1.2.0
Check: Rd cross-references
Result: NOTE
    Package unavailable to check Rd xrefs: ‘RWeka’
Flavor: r-patched-solaris-x86

Version: 1.2.0
Check: running examples for arch ‘i386’
Result: ERROR
    Running examples in 'arulesCBA-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: RCAR
    > ### Title: Regularized Class Association Rules for Multi-class Problems
    > ### (RCAR+)
    > ### Aliases: RCAR rcar
    >
    > ### ** Examples
    >
    > data("iris")
    >
    > classifier <- RCAR(Species~., iris)
    > classifier
    CBA Classifier Object
    Class:
    Default Class: NA
    Number of rules: 9
    Classification method: logit
    Description: RCAR+ based on RCAR (Azmi et al., 2019)
    
    >
    > # inspect the rule base sorted by the larges class weight
    > inspect(sort(rules(classifier), by = "weight"))
     lhs rhs support confidence lift count weight oddsratio
    [1] {Petal.Length=[-Inf,2.45)} => {Species=setosa} 0.3333333 1.0000000 3.000000 50 3.932345e+00 51.026488
    [2] {Petal.Width=[0.8,1.75)} => {Species=versicolor} 0.3266667 0.9074074 2.722222 49 2.175177e+00 8.803743
    [3] {Petal.Length=[4.75, Inf]} => {Species=virginica} 0.3266667 0.8909091 2.672727 49 1.936672e+00 6.935628
    [4] {Petal.Length=[2.45,4.75)} => {Species=versicolor} 0.2933333 0.9777778 2.933333 44 5.537206e-01 1.739714
    [5] {Sepal.Length=[6.15, Inf],
     Petal.Width=[1.75, Inf]} => {Species=virginica} 0.2466667 1.0000000 3.000000 37 5.327466e-01 1.703605
    [6] {Petal.Width=[1.75, Inf]} => {Species=virginica} 0.3000000 0.9782609 2.934783 45 3.582109e-01 1.430767
    [7] {Sepal.Length=[6.15, Inf],
     Petal.Length=[4.75, Inf],
     Petal.Width=[1.75, Inf]} => {Species=virginica} 0.2466667 1.0000000 3.000000 37 3.670022e-02 1.037382
    [8] {Petal.Length=[-Inf,2.45),
     Petal.Width=[-Inf,0.8)} => {Species=setosa} 0.3333333 1.0000000 3.000000 50 2.655090e-02 1.026907
    [9] {Petal.Width=[-Inf,0.8)} => {Species=setosa} 0.3333333 1.0000000 3.000000 50 2.106898e-15 1.000000
    >
    > # make predictions for the first few instances of iris
    > predict(classifier, head(iris))
    [1] setosa setosa setosa setosa setosa setosa
    Levels: setosa versicolor virginica
    >
    > # inspecting the regression model and the cross-validation results to determine lambda
    > str(classifier$model$reg_model)
    List of 15
     $ a0 : num [1:3, 1] -0.401 0.195 0.206
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
     .. ..$ : chr "s0"
     $ beta :List of 3
     ..$ setosa :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
     .. .. ..@ i : int [1:3] 4 5 23
     .. .. ..@ p : int [1:2] 0 3
     .. .. ..@ Dim : int [1:2] 55 1
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : chr [1:55] "{Sepal.Length=[5.55,6.15)}" "{Sepal.Width=[3.35, Inf]}" "{Petal.Length=[2.45,4.75)}" "{Petal.Width=[1.75, Inf]}" ...
     .. .. .. ..$ : chr "s0"
     .. .. ..@ x : num [1:3] 3.93 2.11e-15 2.66e-02
     .. .. ..@ factors : list()
     ..$ versicolor:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
     .. .. ..@ i : int [1:2] 2 7
     .. .. ..@ p : int [1:2] 0 2
     .. .. ..@ Dim : int [1:2] 55 1
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : chr [1:55] "{Sepal.Length=[5.55,6.15)}" "{Sepal.Width=[3.35, Inf]}" "{Petal.Length=[2.45,4.75)}" "{Petal.Width=[1.75, Inf]}" ...
     .. .. .. ..$ : chr "s0"
     .. .. ..@ x : num [1:2] 0.554 2.175
     .. .. ..@ factors : list()
     ..$ virginica :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
     .. .. ..@ i : int [1:4] 3 10 19 42
     .. .. ..@ p : int [1:2] 0 4
     .. .. ..@ Dim : int [1:2] 55 1
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : chr [1:55] "{Sepal.Length=[5.55,6.15)}" "{Sepal.Width=[3.35, Inf]}" "{Petal.Length=[2.45,4.75)}" "{Petal.Width=[1.75, Inf]}" ...
     .. .. .. ..$ : chr "s0"
     .. .. ..@ x : num [1:4] 0.3582 1.9367 0.5327 0.0367
     .. .. ..@ factors : list()
     $ dfmat : num [1:3, 1] 3 2 4
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
     .. ..$ : chr "s0"
     $ df : int 9
     $ dim : int [1:2] 55 1
     $ lambda : num 0.0461
     $ dev.ratio : num 0.856
     $ nulldev : num 330
     $ npasses : int 356
     $ jerr : int 0
     $ offset : logi FALSE
     $ classnames: chr [1:3] "setosa" "versicolor" "virginica"
     $ grouped : logi FALSE
     $ call : language (function (x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), weights, | __truncated__ ...
     $ nobs : int 150
     - attr(*, "class")= chr [1:2] "multnet" "glmnet"
    > plot(classifier$model$cv)
    >
    > # show progress report and use 5 instead of the default 10 cross-validation folds.
    > classifier <- RCAR(Species~., iris, cv.glmnet.args = list(nfolds = 5), verbose = TRUE)
    * Mining CARs...
    Apriori
    
    Parameter specification:
     confidence minval smax arem aval originalSupport maxtime support minlen
     0.5 0.1 1 none FALSE TRUE 5 0.1 1
     maxlen target ext
     5 rules FALSE
    
    Algorithmic control:
     filter tree heap memopt load sort verbose
     0.1 TRUE TRUE FALSE TRUE 2 TRUE
    
    Absolute minimum support count: 15
    
    set item appearances ...[15 item(s)] done [0.00s].
    set transactions ...[15 item(s), 150 transaction(s)] done [0.00s].
    sorting and recoding items ... [15 item(s)] done [0.00s].
    creating transaction tree ... done [0.00s].
    checking subsets of size 1 2 3 4 5 done [0.00s].
    writing ... [55 rule(s)] done [0.00s].
    creating S4 object ... done [0.00s].
    * Creating model matrix
    * Determine lambda using cross-validation: Error in glmnet(x, y, weights = weights, offset = offset, lambda = lambda, :
     unused argument (trace.it = TRUE)
    Calls: RCAR -> do.call -> <Anonymous>
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 1.2.0
Check: running examples for arch ‘x64’
Result: ERROR
    Running examples in 'arulesCBA-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: RCAR
    > ### Title: Regularized Class Association Rules for Multi-class Problems
    > ### (RCAR+)
    > ### Aliases: RCAR rcar
    >
    > ### ** Examples
    >
    > data("iris")
    >
    > classifier <- RCAR(Species~., iris)
    > classifier
    CBA Classifier Object
    Class:
    Default Class: NA
    Number of rules: 9
    Classification method: logit
    Description: RCAR+ based on RCAR (Azmi et al., 2019)
    
    >
    > # inspect the rule base sorted by the larges class weight
    > inspect(sort(rules(classifier), by = "weight"))
     lhs rhs support confidence lift count weight oddsratio
    [1] {Petal.Length=[-Inf,2.45)} => {Species=setosa} 0.3333333 1.0000000 3.000000 50 3.932345e+00 51.026488
    [2] {Petal.Width=[0.8,1.75)} => {Species=versicolor} 0.3266667 0.9074074 2.722222 49 2.175177e+00 8.803743
    [3] {Petal.Length=[4.75, Inf]} => {Species=virginica} 0.3266667 0.8909091 2.672727 49 1.936672e+00 6.935628
    [4] {Petal.Length=[2.45,4.75)} => {Species=versicolor} 0.2933333 0.9777778 2.933333 44 5.537206e-01 1.739714
    [5] {Sepal.Length=[6.15, Inf],
     Petal.Width=[1.75, Inf]} => {Species=virginica} 0.2466667 1.0000000 3.000000 37 5.327466e-01 1.703605
    [6] {Petal.Width=[1.75, Inf]} => {Species=virginica} 0.3000000 0.9782609 2.934783 45 3.582109e-01 1.430767
    [7] {Sepal.Length=[6.15, Inf],
     Petal.Length=[4.75, Inf],
     Petal.Width=[1.75, Inf]} => {Species=virginica} 0.2466667 1.0000000 3.000000 37 3.670022e-02 1.037382
    [8] {Petal.Length=[-Inf,2.45),
     Petal.Width=[-Inf,0.8)} => {Species=setosa} 0.3333333 1.0000000 3.000000 50 2.655090e-02 1.026907
    [9] {Petal.Width=[-Inf,0.8)} => {Species=setosa} 0.3333333 1.0000000 3.000000 50 1.152876e-15 1.000000
    >
    > # make predictions for the first few instances of iris
    > predict(classifier, head(iris))
    [1] setosa setosa setosa setosa setosa setosa
    Levels: setosa versicolor virginica
    >
    > # inspecting the regression model and the cross-validation results to determine lambda
    > str(classifier$model$reg_model)
    List of 15
     $ a0 : num [1:3, 1] -0.401 0.195 0.206
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
     .. ..$ : chr "s0"
     $ beta :List of 3
     ..$ setosa :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
     .. .. ..@ i : int [1:3] 4 5 23
     .. .. ..@ p : int [1:2] 0 3
     .. .. ..@ Dim : int [1:2] 55 1
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : chr [1:55] "{Sepal.Length=[5.55,6.15)}" "{Sepal.Width=[3.35, Inf]}" "{Petal.Length=[2.45,4.75)}" "{Petal.Width=[1.75, Inf]}" ...
     .. .. .. ..$ : chr "s0"
     .. .. ..@ x : num [1:3] 3.93 1.15e-15 2.66e-02
     .. .. ..@ factors : list()
     ..$ versicolor:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
     .. .. ..@ i : int [1:2] 2 7
     .. .. ..@ p : int [1:2] 0 2
     .. .. ..@ Dim : int [1:2] 55 1
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : chr [1:55] "{Sepal.Length=[5.55,6.15)}" "{Sepal.Width=[3.35, Inf]}" "{Petal.Length=[2.45,4.75)}" "{Petal.Width=[1.75, Inf]}" ...
     .. .. .. ..$ : chr "s0"
     .. .. ..@ x : num [1:2] 0.554 2.175
     .. .. ..@ factors : list()
     ..$ virginica :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
     .. .. ..@ i : int [1:4] 3 10 19 42
     .. .. ..@ p : int [1:2] 0 4
     .. .. ..@ Dim : int [1:2] 55 1
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : chr [1:55] "{Sepal.Length=[5.55,6.15)}" "{Sepal.Width=[3.35, Inf]}" "{Petal.Length=[2.45,4.75)}" "{Petal.Width=[1.75, Inf]}" ...
     .. .. .. ..$ : chr "s0"
     .. .. ..@ x : num [1:4] 0.3582 1.9367 0.5327 0.0367
     .. .. ..@ factors : list()
     $ dfmat : num [1:3, 1] 3 2 4
     ..- attr(*, "dimnames")=List of 2
     .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
     .. ..$ : chr "s0"
     $ df : int 9
     $ dim : int [1:2] 55 1
     $ lambda : num 0.0461
     $ dev.ratio : num 0.856
     $ nulldev : num 330
     $ npasses : int 356
     $ jerr : int 0
     $ offset : logi FALSE
     $ classnames: chr [1:3] "setosa" "versicolor" "virginica"
     $ grouped : logi FALSE
     $ call : language (function (x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), weights, | __truncated__ ...
     $ nobs : int 150
     - attr(*, "class")= chr [1:2] "multnet" "glmnet"
    > plot(classifier$model$cv)
    >
    > # show progress report and use 5 instead of the default 10 cross-validation folds.
    > classifier <- RCAR(Species~., iris, cv.glmnet.args = list(nfolds = 5), verbose = TRUE)
    * Mining CARs...
    Apriori
    
    Parameter specification:
     confidence minval smax arem aval originalSupport maxtime support minlen
     0.5 0.1 1 none FALSE TRUE 5 0.1 1
     maxlen target ext
     5 rules FALSE
    
    Algorithmic control:
     filter tree heap memopt load sort verbose
     0.1 TRUE TRUE FALSE TRUE 2 TRUE
    
    Absolute minimum support count: 15
    
    set item appearances ...[15 item(s)] done [0.00s].
    set transactions ...[15 item(s), 150 transaction(s)] done [0.00s].
    sorting and recoding items ... [15 item(s)] done [0.00s].
    creating transaction tree ... done [0.00s].
    checking subsets of size 1 2 3 4 5 done [0.00s].
    writing ... [55 rule(s)] done [0.00s].
    creating S4 object ... done [0.00s].
    * Creating model matrix
    * Determine lambda using cross-validation: Error in glmnet(x, y, weights = weights, offset = offset, lambda = lambda, :
     unused argument (trace.it = TRUE)
    Calls: RCAR -> do.call -> <Anonymous>
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64