BioC 2.5: CHECK report for MLInterfaces on liverpool

Package: MLInterfaces

Version: 1.26.0

Command: E:\biocbld\bbs-2.5-bioc\R\bin\R.exe CMD check --no-vignettes MLInterfaces_1.26.0.tar.gz

StartedAt: 2009-12-14 05:43:48 -0800 (Mon, 14 Dec 2009)

EndedAt: 2009-12-14 05:45:26 -0800 (Mon, 14 Dec 2009)

EllapsedTime: 98.3 seconds

RetCode: 1

Status: ERROR

CheckDir: MLInterfaces.Rcheck

Warnings: NA

* checking for working pdflatex ... OK
* using log directory 'E:/biocbld/bbs-2.5-bioc/meat/MLInterfaces.Rcheck'
* using R version 2.10.0 (2009-10-26)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'MLInterfaces/DESCRIPTION' ... OK
* this is package 'MLInterfaces' version '1.26.0'
* checking package name space information ... OK
* 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 whether the name space 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 ... OK
* checking Rd files ... NOTE
prepare_Rd: MLIntInternals.Rd:40-41: Dropping empty section \examples
* 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 examples ... ERROR
Running examples in 'MLInterfaces-Ex.R' failed.
The error most likely occurred in:

> ### * MLearn-new
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MLearn
> ### Title: revised MLearn interface for machine learning
> ### Aliases: MLearn_new MLearn baggingI dlda glmI.logistic knnI knn.cvI
> ###   ksvmI ldaI lvqI naiveBayesI nnetI qdaI RABI randomForestI rpartI svmI
> ###   dlda2 dldaI sldaI blackboostI knn2 knn.cv2 ldaI.predParms lvq rab
> ###   adaI MLearn,formula,ExpressionSet,character,numeric-method
> ###   MLearn,formula,ExpressionSet,learnerSchema,numeric-method
> ###   MLearn,formula,data.frame,learnerSchema,numeric-method
> ###   MLearn,formula,data.frame,learnerSchema,xvalSpec-method
> ###   MLearn,formula,ExpressionSet,learnerSchema,xvalSpec-method
> ###   MLearn,formula,data.frame,clusteringSchema,ANY-method plotXvalRDA
> ###   rdacvI rdaI BgbmI gbm2 rdaML rdacvML hclustI kmeansI pamI
> ###   makeLearnerSchema standardMLIConverter
> ### Keywords: models
> 
> ### ** Examples
> 
> data(crabs)
> set.seed(1234)
> kp = sample(1:200, size=120)
> rf1 = MLearn(sp~CW+RW, data=crabs, randomForestI, kp, ntree=600 )
> rf1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = randomForestI, 
    trainInd = kp, ntree = 600)
Predicted outcome distribution for test set:

 B  O 
42 38 
> nn1 = MLearn(sp~CW+RW, data=crabs, nnetI, kp, size=3, decay=.01 )
# weights:  13
initial  value 99.720399 
iter  10 value 82.101689
iter  20 value 71.605623
iter  30 value 70.270945
iter  40 value 68.649772
iter  50 value 66.818899
iter  60 value 66.704999
iter  70 value 66.692149
final  value 66.691861 
converged
> nn1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = nnetI, 
    trainInd = kp, size = 3, decay = 0.01)
Predicted outcome distribution for test set:

 B  O 
41 39 
Summary of scores on test set (use testScores() method for details):
[1] 0.5032742
> RObject(nn1)
a 2-3-1 network with 13 weights
inputs: CW RW 
output(s): sp 
options were - entropy fitting  decay=0.01
> knn1 = MLearn(sp~CW+RW, data=crabs, knnI(k=3,l=2), kp)
> knn1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = knnI(k = 3, 
    l = 2), trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
44 35 
Summary of scores on test set (use testScores() method for details):
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.5000  0.6667  0.6667  0.7552  1.0000  1.0000 
> names(RObject(knn1))
[1] "traindat" "ans"      "traincl" 
> dlda1 = MLearn(sp~CW+RW, data=crabs, dldaI, kp )
Loading required package: sfsmisc
> dlda1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = dldaI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
40 40 
> names(RObject(dlda1))
[1] "traindat" "ans"      "traincl" 
> lda1 = MLearn(sp~CW+RW, data=crabs, ldaI, kp )
> lda1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = ldaI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
43 37 
> names(RObject(lda1))
 [1] "prior"   "counts"  "means"   "scaling" "lev"     "svd"     "N"      
 [8] "call"    "terms"   "xlevels"
> slda1 = MLearn(sp~CW+RW, data=crabs, sldaI, kp )
Loading required package: mlbench
Loading required package: class
Loading required package: nnet
> slda1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = sldaI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
49 31 
Summary of scores on test set (use testScores() method for details):
       B        O 
0.539828 0.460172 
> names(RObject(slda1))
[1] "scores"  "mylda"   "terms"   "call"    "xlevels"
> svm1 = MLearn(sp~CW+RW, data=crabs, svmI, kp )
> svm1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = svmI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
50 30 
> names(RObject(svm1))
 [1] "call"      "type"      "kernel"    "cost"      "degree"    "gamma"    
 [7] "coef0"     "nu"        "epsilon"   "sparse"    "scaled"    "x.scale"  
[13] "y.scale"   "nclasses"  "levels"    "tot.nSV"   "nSV"       "labels"   
[19] "SV"        "index"     "rho"       "compprob"  "probA"     "probB"    
[25] "sigma"     "coefs"     "na.action" "fitted"    "terms"    
> ldapp1 = MLearn(sp~CW+RW, data=crabs, ldaI.predParms(method="debiased"), kp )
> ldapp1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = ldaI.predParms(method = "debiased"), 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
43 37 
> names(RObject(ldapp1))
 [1] "prior"   "counts"  "means"   "scaling" "lev"     "svd"     "N"      
 [8] "call"    "terms"   "xlevels"
> qda1 = MLearn(sp~CW+RW, data=crabs, qdaI, kp )
> qda1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = qdaI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
47 33 
> names(RObject(qda1))
 [1] "prior"   "counts"  "means"   "scaling" "ldet"    "lev"     "N"      
 [8] "call"    "terms"   "xlevels"
> logi = MLearn(sp~CW+RW, data=crabs, glmI.logistic(threshold=0.5), kp, family=binomial ) # need family
> logi
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = glmI.logistic(threshold = 0.5), 
    trainInd = kp, family = binomial)
Predicted outcome distribution for test set:

 B  O 
42 38 
Summary of scores on test set (use testScores() method for details):
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1621  0.3617  0.4868  0.5175  0.6751  0.8831 
> names(RObject(logi))
 [1] "coefficients"      "residuals"         "fitted.values"    
 [4] "effects"           "R"                 "rank"             
 [7] "qr"                "family"            "linear.predictors"
[10] "deviance"          "aic"               "null.deviance"    
[13] "iter"              "weights"           "prior.weights"    
[16] "df.residual"       "df.null"           "y"                
[19] "converged"         "boundary"          "model"            
[22] "call"              "formula"           "terms"            
[25] "data"              "offset"            "control"          
[28] "method"            "contrasts"         "xlevels"          
> rp2 = MLearn(sp~CW+RW, data=crabs, rpartI, kp)
> rp2
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = rpartI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
50 30 
Summary of scores on test set (use testScores() method for details):
        B         O 
0.5619216 0.4380784 
> ## recode data for RAB
> #nsp = ifelse(crabs$sp=="O", -1, 1)
> #nsp = factor(nsp)
> #ncrabs = cbind(nsp,crabs)
> #rab1 = MLearn(nsp~CW+RW, data=ncrabs, RABI, kp, maxiter=10)
> #rab1
> #
> # new approach to adaboost
> #
> ada1 = MLearn(sp ~ CW+RW, data = crabs, .method = adaI, 
+     trainInd = kp, type = "discrete", iter = 200)
> ada1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = adaI, 
    trainInd = kp, type = "discrete", iter = 200)
Predicted outcome distribution for test set:

 B  O 
41 39 
> confuMat(ada1)
     predicted
given  B  O
    B 22 15
    O 19 24
> #
> lvq.1 = MLearn(sp~CW+RW, data=crabs, lvqI, kp )
> lvq.1
MLInterfaces classification output container
The call was:
MLearn(formula = sp ~ CW + RW, data = crabs, .method = lvqI, 
    trainInd = kp)
Predicted outcome distribution for test set:

 B  O 
 2 78 
> nb.1 = MLearn(sp~CW+RW, data=crabs, naiveBayesI, kp )
> confuMat(nb.1)
     predicted
given  B  O
    B 25 12
    O 17 26
> bb.1 = MLearn(sp~CW+RW, data=crabs, baggingI, kp )
> confuMat(bb.1)
     predicted
given  B  O
    B 20 17
    O 22 21
> #
> # new mboost interface -- you MUST supply family for nonGaussian response
> #
> blb.1 = MLearn(sp~CW+RW+FL, data=crabs, blackboostI, kp, family=mboost::Binomial() )
Error in trafo(obj) : could not find function "trafo"
Calls: MLearn ... initVariableFrame -> initVariableFrame -> .local -> trafo
Execution halted

* install options are ' --no-html'

* installing *source* package 'MLInterfaces' ...
** R
** inst
** preparing package for lazy loading

Welcome to Bioconductor

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  'openVignette()'. To cite Bioconductor, see
  'citation("Biobase")' and for packages 'citation(pkgname)'.


Attaching package: 'genefilter'


	The following object(s) are masked from package:MASS :

	 area 

Loading required package: AnnotationDbi
Loading required package: survival
Loading required package: splines
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Warning in .findOrCopyClass(class2, classDef2, where, "subclass") :
  Class "df_OR_list" is defined (with package slot "party") but no metadata object found to revise subclass information---not exported?  Making a copy in package "MLInterfaces"
** help
*** installing help indices
** building package indices ...
** MD5 sums
* DONE (MLInterfaces)

Command output