Nothing
#Example 6
#Warning, it will take a while
#Load the library
library(brnn)
#Load the dataset
data(GLS)
#Subset of data for location Harare
HarareOrd=subset(phenoOrd,Loc=="Harare")
#Eigen value decomposition for GOrdm keep those
#eigen vectors whose corresponding eigen-vectors are bigger than 1e-10
#and then compute principal components
evd=eigen(GOrd)
evd$vectors=evd$vectors[,evd$value>1e-10]
evd$values=evd$values[evd$values>1e-10]
PC=evd$vectors%*%sqrt(diag(evd$values))
rownames(PC)=rownames(GOrd)
#Response variable
y=phenoOrd$rating
gid=as.character(phenoOrd$Stock)
Z=model.matrix(~gid-1)
colnames(Z)=gsub("gid","",colnames(Z))
if(any(colnames(Z)!=rownames(PC))) stop("Ordering problem\n")
#Matrix of predictors for Neural net
X=Z%*%PC
#Cross-validation
set.seed(1)
testing=sample(1:length(y),size=as.integer(0.10*length(y)),replace=FALSE)
isNa=(1:length(y)%in%testing)
yTrain=y[!isNa]
XTrain=X[!isNa,]
nTest=sum(isNa)
neurons=2
fmOrd=brnn_ordinal(XTrain,yTrain,neurons=neurons,verbose=FALSE)
#Predictions for testing set
XTest=X[isNa,]
predictions=predict(fmOrd,XTest)
predictions
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.