* using log directory 'd:/Rcompile/CRANpkg/local/3.5/arulesCBA.Rcheck' * using R version 3.5.3 (2019-03-11) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'arulesCBA/DESCRIPTION' ... OK * this is package 'arulesCBA' version '1.2.0' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'arulesCBA' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [15s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking pragmas in C/C++ headers and code ... OK * checking compiled code ... OK * checking examples ... ** running examples for arch 'i386' ... 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 -> Execution halted ** running examples for arch 'x64' ... 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 -> Execution halted * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... [27s] OK Running 'testthat.R' [26s] ** running tests for arch 'x64' ... [25s] OK Running 'testthat.R' [25s] * checking PDF version of manual ... OK * DONE Status: 2 ERRORs