Description Usage Arguments Value Author(s) References See Also Examples
In a crossvalidation setting or similar, often similar GLoMo objects occur: matching the same form of dataset, each of equal size or at least very similar. This is a method that combines them reasonably into 1 GLoMo model object.
1 | combineGLoMos(..., listOfGLoMos=NULL, verbosity=0)
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... |
several |
listOfGLoMos |
if you have the parameters already in a list, this is the easier way of passing them. |
verbosity |
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) |
a GLoMo
object
Nick Sabbe (nick.sabbe@ugent.be)
"Statistical Analysis with Missing Values"
1 2 3 4 5 6 7 8 9 10 11 | iris.md<-randomNA(iris, 0.1)
iris.md.nd<-numdfr(iris.md)
iris.nd.rnd<-rCatsAndCntInDfr(iris.md.nd, orgriName=NULL, verbosity=1)
iris.weights<-iris.nd.rnd$weights
iris.nd.rnd<-iris.nd.rnd[,1:5]
halfrows<-1:(as.integer(nrow(iris.nd.rnd)/2))
iris.glomo1<-GLoMo(iris.nd.rnd[halfrows,], weights=iris.weights[halfrows], verbosity=1)
iris.glomo2<-GLoMo(iris.nd.rnd[-halfrows,], weights=iris.weights[-halfrows], verbosity=1)
iris.glomo<-combineGLoMos(iris.glomo1, iris.glomo2, verbosity=1)
iris.pred.cond<-predict(iris.glomo, nobs=100, verbosity=1)
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