* checking for working pdflatex ... OK
* using log directory '/Users/biocbuild/bbs-2.6-bioc/meat/MLInterfaces.Rcheck'
* using R version 2.11.0 Under development (unstable) (2009-11-30 r50622)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'MLInterfaces/DESCRIPTION' ... OK
* this is package 'MLInterfaces' version '1.27.0'
* 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 for sufficient/correct file permissions ... 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 package can be unloaded cleanly ... OK
* checking whether the name space can be loaded with stated dependencies ... OK
* checking whether the name space can be unloaded cleanly ... 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
41 39
> nn1 = MLearn(sp~CW+RW, data=crabs, nnetI, kp, size=3, decay=.01 )
# weights: 13
initial value 89.644304
iter 10 value 83.034454
iter 20 value 82.116930
iter 30 value 75.091319
iter 40 value 74.048246
iter 50 value 73.129257
iter 60 value 71.664448
iter 70 value 71.162182
iter 80 value 69.903265
iter 90 value 69.834599
iter 100 value 69.823854
final value 69.823854
stopped after 100 iterations
> 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
33 47
Summary of scores on test set (use testScores() method for details):
[1] 0.5093793
> 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
Warning in fun(...) :
no valid postscript previewer found; consider setting
options("eps_view"= "....") yourself
> 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
44 36
> confuMat(ada1)
predicted
given B O
B 23 14
O 21 22
> #
> 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
49 31
> 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 21 22
> #
> # 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