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> ### simulateData
> # test extreme values
> library(nlcv)
Loading required package: a4Core
Loading required package: MLInterfaces
Loading required package: Rcpp
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

The following objects are masked from 'package:base':

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: annotate
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    I, expand.grid, unname


Attaching package: 'IRanges'

The following object is masked from 'package:grDevices':

    windows

Loading required package: XML
Loading required package: cluster
Loading required package: xtable

nlcv version 0.3.6

> 
> library(a4Core)
> myEset <- simulateData(nCols = 40, nRows = 1000, nEffectRows = 0)
> 
> ### check the converter works as expected
> set.seed(120)
> x <- matrix(rnorm(1000*20), ncol=20)
> y <- sample(c(1:4), size=20, replace=TRUE)
> 
> traindf <- cbind.data.frame(t(x[,1:15]), y = y[1:15])
> alldf <- cbind.data.frame(t(x), y)
> 
> pamrMLObj <- pamrML(y ~ ., traindf)
123456789101112131415161718192021222324252627282930> 
> nlcv:::pamrIconverter(obj = pamrMLObj, data = alldf, trainInd = 1:15)
MLInterfaces classification output container
The call was:
`<undef>`()
Predicted outcome distribution for test set:

2 3 4 
1 3 1 
> 
> ### test pamrI for an ExpressionSet
> EsetStrongSignal <- simulateData(nCols = 40, nRows = 1000, nEffectRows = 10, nNoEffectCols = 0,
+     betweenClassDifference = 3, withinClassSd = 0.5)
> 
> library(MLInterfaces)
> idxTrain <- sample(1:40, 20)
> mlobj <- MLearn(type ~ .,
+     data = EsetStrongSignal,
+     .method = pamrI,
+     trainInd = idxTrain)
[1] "type"
123456789101112131415161718192021222324252627282930> 
> mlobj
MLInterfaces classification output container
The call was:
MLearn(formula = type ~ ., data = EsetStrongSignal, .method = pamrI, 
    trainInd = idxTrain)
Predicted outcome distribution for test set:

 A  B 
10 10 
> 
> # nlda (to check export of predict.lda)
> 
> mlNldaObj <- MLearn(type ~ .,
+ 	data = EsetStrongSignal,
+ 	.method = nldaI,
+ 	trainInd = idxTrain)
[1] "type"
Warning message:
In lda.default(x, grouping, ...) : variables are collinear
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   8.51    0.78    9.28