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NOTES: (1) a maintainance reboot of lemming prevented these builds to complete on this machine (2) these are the last builds with R 2.6.0 (2007-06-03 r41797) on lamb1 and lemming, next builds will be with R 2.6.0 (2007-06-10 r41908) configured with --enable-strict-barrier |
Package 142/238 | Hostname | OS | Arch | BUILD | CHECK | BUILD BIN |
MLInterfaces1.11.2V. CareyLast Changed Date: 2007-05-11 21:56:41 -0700 Last Changed Rev: 24616 | lamb1 | Linux (SUSE 10.1) | x86_64 | OK | ERROR | |
wellington | Linux (SUSE 9.2) | i686 | OK | ERROR | ||
churchill | Solaris 2.9 | sparc | OK | ERROR | ||
lemming | Windows Server 2003 (32-bit) | x64 | OK | [ ERROR ] | skipped |
Package: MLInterfaces |
Version: 1.11.2 |
Command: D:\biocbld\bbs-2.1-bioc\R\bin\R.exe CMD check MLInterfaces_1.11.2.tar.gz |
RetCode: 1 |
Time: 190.4 seconds |
Status: ERROR |
CheckDir: MLInterfaces.Rcheck |
Warnings: NA |
* checking for working latex ... OK * using log directory 'D:/biocbld/bbs-2.1-bioc/meat/MLInterfaces.Rcheck' * using R version 2.6.0 Under development (unstable) (2007-06-03 r41797) * checking for file 'MLInterfaces/DESCRIPTION' ... OK * this is package 'MLInterfaces' version '1.11.2' * checking package dependencies ... OK * checking if this is a source package ... OK * checking whether package 'MLInterfaces' can be installed ... OK * checking package directory ... OK * checking for portable file names ... OK * checking DESCRIPTION meta-information ... OK * checking top-level 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 * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking for unstated 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 ... NOTE makeCVFunc: multiple local function definitions for 'resfunc' with different formal arguments * checking Rd files ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * creating MLInterfaces-Ex.R ... OK * checking examples ... ERROR Running examples in 'MLInterfaces-Ex.R' failed. The error most likely occurred in: > ### * knnB > > flush(stderr()); flush(stdout()) > > ### Name: knnB > ### Title: An interface to various machine learning methods for > ### ExpressionSets > ### Aliases: allClass stat.diag.daB > ### stat.diag.daB,ExpressionSet,character,integer-method cvB knn1B knnP > ### lcaB logitboostB lvq2B lvq3B olvq1B predLabels RObject knnB nnetB > ### lvq1B naiveBayesB svmB baggingB ipredknnB sldaB ldaB qdaB pamrB > ### rpartB randomForestB gbmB allClass,classifOutput-method > ### cvB,ExpressionSet,character-method predLabels,MLOutput-method > ### predLabels,classifOutput-method last.warning distMat,MLOutput-method > ### RObject,MLOutput-method trainInds,classifOutput-method > ### show,probMat-method show,probArray-method show,membMat-method > ### show,qualScore-method show,silhouetteVec-method show,MLOutput-method > ### baggingB,ExpressionSet,character,integer-method > ### gbmB,ExpressionSet,character,integer-method > ### ipredknnB,ExpressionSet,character,integer-method > ### knn1B,ExpressionSet,character,integer-method > ### knnB,ExpressionSet,character,integer-method > ### lcaB,ExpressionSet,numeric-method > ### ldaB,ExpressionSet,character,integer-method > ### logitboostB,ExpressionSet,character,integer,numeric-method > ### lvq1B,ExpressionSet,character,integer-method > ### lvq2B,ExpressionSet,character,integer-method > ### lvq3B,ExpressionSet,character,integer-method > ### naiveBayesB,ExpressionSet,character,integer-method > ### nnetB,ExpressionSet,character,integer-method > ### olvq1B,ExpressionSet,character,integer-method > ### pamrB,ExpressionSet,character,integer-method > ### qdaB,ExpressionSet,character,integer-method > ### randomForestB,ExpressionSet,character,integer-method > ### rpartB,ExpressionSet,character,integer-method > ### sldaB,ExpressionSet,character,integer-method > ### svmB,ExpressionSet,character,integer-method > ### Keywords: classif > > ### ** Examples > > # access and trim an ExpressionSet > library(golubEsets) > data(Golub_Merge) > smallG <- Golub_Merge[1:60,] > # set a PRNG seed for reproducibilitiy > set.seed(1234) # needed for nnet initialization > # now run the classifiers > knnB( smallG, "ALL.AML", 1:40 ) MLOutput instance, method= knn Call: knnB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 25 7 summary of class assignment quality scores: Min. 1st Qu. Median Mean 3rd Qu. Max. 1 1 1 1 1 1 > nnetB( smallG, "ALL.AML", 1:40, size=5, decay=.01 ) # weights: 311 initial value 34.887063 iter 10 value 23.023237 iter 20 value 22.682248 iter 30 value 22.457325 iter 40 value 21.954686 iter 50 value 21.315800 iter 60 value 20.914228 iter 70 value 20.695695 iter 80 value 20.614069 iter 90 value 19.198363 iter 100 value 17.709287 final value 17.709287 stopped after 100 iterations MLOutput instance, method= nnet a 60-5-1 network with 311 weights inputs: AFFX.BioB.5_at AFFX.BioB.M_at AFFX.BioB.3_at AFFX.BioC.5_at AFFX.BioC.3_at AFFX.BioDn.5_at AFFX.BioDn.3_at AFFX.CreX.5_at AFFX.CreX.3_at AFFX.BioB.5_st AFFX.BioB.M_st AFFX.BioB.3_st AFFX.BioC.5_st AFFX.BioC.3_st AFFX.BioDn.5_st AFFX.BioDn.3_st AFFX.CreX.5_st AFFX.CreX.3_st hum_alu_at AFFX.DapX.5_at AFFX.DapX.M_at AFFX.DapX.3_at AFFX.LysX.5_at AFFX.LysX.M_at AFFX.LysX.3_at AFFX.PheX.5_at AFFX.PheX.M_at AFFX.PheX.3_at AFFX.ThrX.5_at AFFX.ThrX.M_at AFFX.ThrX.3_at AFFX.TrpnX.5_at AFFX.TrpnX.M_at AFFX.TrpnX.3_at AFFX.HUMISGF3A.M97935_5_at AFFX.HUMISGF3A.M97935_MA_at AFFX.HUMISGF3A.M97935_MB_at AFFX.HUMISGF3A.M97935_3_at AFFX.HUMRGE.M10098_5_at AFFX.HUMRGE.M10098_M_at AFFX.HUMRGE.M10098_3_at AFFX.HUMGAPDH.M33197_5_at AFFX.HUMGAPDH.M33197_M_at AFFX.HUMGAPDH.M33197_3_at AFFX.HSAC07.X00351_5_at AFFX.HSAC07.X00351_M_at AFFX.HSAC07.X00351_3_at AFFX.HUMTFRR.M11507_5_at AFFX.HUMTFRR.M11507_M_at AFFX.HUMTFRR.M11507_3_at AFFX.M27830_5_at AFFX.M27830_M_at AFFX.M27830_3_at AFFX.HSAC07.X00351_3_st AFFX.HUMGAPDH.M33197_5_st AFFX.HUMGAPDH.M33197_M_st AFFX.HUMGAPDH.M33197_3_st AFFX.HSAC07.X00351_5_st AFFX.HSAC07.X00351_M_st A28102_at output(s): sampLab options were - entropy fitting decay=0.01 Call: nnetB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40, size = 5, decay = 0.01) predicted class distribution: ALL AML 23 9 summary of class membership probabilities: [,1] Min. 0.01127 1st Qu. 0.07319 Median 0.07551 Mean 0.23300 3rd Qu. 0.61790 Max. 0.72640 > lvq1B( smallG, "ALL.AML", 1:40 ) MLOutput instance, method= lvq1 Call: lvq1B(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL 32 > naiveBayesB( smallG, "ALL.AML", 1:40 ) MLOutput instance, method= naiveBayes Call: naiveBayesB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 16 16 > svmB( smallG, "ALL.AML", 1:40 ) MLOutput instance, method= svm Call: svmB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL 32 > baggingB( smallG, "ALL.AML", 1:40 ) Loading required package: MASS Attaching package: 'MASS' The following object(s) are masked from package:genefilter : area Loading required package: mlbench Loading required package: nnet Loading required package: class Attaching package: 'ipred' The following object(s) are masked from package:genefilter : cv MLOutput instance, method= bagging Call: baggingB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 28 4 > ipredknnB( smallG, "ALL.AML", 1:40 ) MLOutput instance, method= ipredknn Call: ipredknnB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL 32 summary of class assignment quality scores: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.6 0.6 0.6 0.7 0.8 1.0 > sldaB( smallG, "ALL.AML", 1:40 ) MLOutput instance, method= slda Call: sldaB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 31 1 summary of class membership probabilities: ALL AML Min. 0.4703 0.0462 1st Qu. 0.6843 0.1463 Median 0.7586 0.2414 Mean 0.7546 0.2454 3rd Qu. 0.8537 0.3157 Max. 0.9538 0.5297 > ldaB( smallG, "ALL.AML", 1:40 ) Warning in lda.default(x, grouping, ...) : variables are collinear MLOutput instance, method= lda Call: ldaB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 28 4 summary of class membership probabilities: ALL AML Min. 0.000643 1.762e-09 1st Qu. 0.886000 5.023e-04 Median 0.998400 1.600e-03 Mean 0.851600 1.484e-01 3rd Qu. 0.999500 1.140e-01 Max. 1.000000 9.994e-01 > qdaB( smallG[1:10,], "ALL.AML", 1:40 ) MLOutput instance, method= qda Call: qdaB(exprObj = smallG[1:10, ], classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 22 10 summary of class membership probabilities: ALL AML Min. 6.831e-06 3.661e-21 1st Qu. 3.295e-01 1.332e-06 Median 9.942e-01 5.814e-03 Mean 7.035e-01 2.965e-01 3rd Qu. 1.000e+00 6.705e-01 Max. 1.000e+00 1.000e+00 > pamrB( smallG, "ALL.AML", 1:40 ) Loading required package: pamr Loading required package: cluster 123456789101112131415161718192021222324252627282930MLOutput instance, method= pamr Call: pamrB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:40) predicted class distribution: ALL AML 31 1 dimensions of (threshold-based) class membership probabilities: [1] 40 2 30 > rpartB( smallG, "ALL.AML", 1:35 ) MLOutput instance, method= rpart Call: rpartB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:35) predicted class distribution: ALL AML 23 14 > randomForestB( smallG, "ALL.AML", 1:35 ) MLOutput instance, method= randomForest Call: randomForestB(exprObj = smallG, classifLab = "ALL.AML", trainInd = 1:35) predicted class distribution: ALL AML 28 9 > gbmB( smallG, "ALL.AML", 1:40, n.minobsinnode=3 , n.trees=6000) Loading required package: gbm Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called 'gbm' Error in gbmB(smallG, "ALL.AML", 1:40, n.minobsinnode = 3, n.trees = 6000) : could not find function "gbm.fit" Execution halted
MLInterfaces.Rcheck/00install.out:
installing R.css in D:/biocbld/bbs-2.1-bioc/meat/MLInterfaces.Rcheck ---------- Making package MLInterfaces ------------ adding build stamp to DESCRIPTION installing R files preparing package MLInterfaces for lazy loading Loading required package: Biobase Loading required package: tools Welcome to Bioconductor Vignettes contain introductory material. To view, type 'openVignette()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation(pkgname)'. Loading required package: genefilter Loading required package: survival Loading required package: splines Loading required package: rpart Loading required package: rda installing inst files installing man source files installing indices installing help >>> Building/Updating help pages for package 'MLInterfaces' Formats: text html latex example chm MLIclust text html latex example chm MLIntInternals text html latex example chm MLearn-methods text html latex example chm RAB text html latex example chm SOMB text html latex example chm classifOutput-class text html latex example chm clustOutput-class text html latex example chm confuMat-methods text html latex example chm knnB text html latex example chm planarPlot-methods text html latex example chm raboostCont-class text html latex example chm varImpStruct-class text html latex example chm xval-methods text html latex example chm xvalLoop-methods text html latex chm xvalLoop text html latex example chm Microsoft HTML Help Compiler 4.74.8702 Compiling d:\biocbld\bbs-2.1-bioc\meat\MLInterfaces.Rcheck\00_pkg_src\MLInterfaces\chm\MLInterfaces.chm Compile time: 0 minutes, 0 seconds 16 Topics 245 Local links 0 Internet links 1 Graphic Created d:\biocbld\bbs-2.1-bioc\meat\MLInterfaces.Rcheck\00_pkg_src\MLInterfaces\chm\MLInterfaces.chm, 47,787 bytes Compression decreased file by 87,677 bytes. adding MD5 sums * DONE (MLInterfaces)