cv1EMtrain {mclust} | R Documentation |
For the ten available discriminant models the leave-one-out cross validation error is calulated. The models for one-dimensional data are "E" and "V"; for higher dimensions they are "EII", "VII", "EEI", "VEI", "EVI", "VVI", "EEE", "EEV", "VEV" and "VVV".
cv1EMtrain(data, labels, modelNames)
data |
A data matrix |
labels |
Labels for each row in the data matrix |
modelNames |
Vector of model names that should be tested. |
Returns a vector where each element is the error rate for the corresponding model.
C. Fraley
data(lansing) odd <- seq(from=1, to=nrow(lansing), by=2) round(cv1EMtrain(data=lansing[odd,-3], labels=lansing[odd,3]), 3) cv1Modd <- mstepEEV(data=lansing[odd,-3], z=unmap(lansing[odd,3])) cv1Zodd <- do.call("estepEEV", c(cv1Modd, list(data=lansing[odd,-3])))$z compareClass(map(cv1Zodd), lansing[odd,3]) even <- (1:nrow(lansing))[-odd] cv1Zeven <- do.call("estepEEV", c(cv1Modd, list(data=lansing[even,-3])))$z compareClass(map(cv1Zodd), lansing[odd,3])$error