Nothing
### simulateData
# test extreme values
library(nlcv)
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)
nlcv:::pamrIconverter(obj = pamrMLObj, data = alldf, trainInd = 1:15)
### 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)
mlobj
# nlda (to check export of predict.lda)
mlNldaObj <- MLearn(type ~ .,
data = EsetStrongSignal,
.method = nldaI,
trainInd = idxTrain)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.