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Cross validation for some compositional regression models.
cv.comp.reg(y, x, type = "comp.reg", nfolds = 10, folds = NULL, seed = NULL)
A matrix with compositional data. Zero values are allowed for some regression models.
The predictor variable(s).
This can be one of the following: "comp.reg", "robust", "kl.compreg", "js.compreg", "diri.reg" or "zadr".
The number of folds to be used. This is taken into consideration only if the folds argument is not supplied.
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
If seed is TRUE the results will always be the same.
A list including:
The runtime of the cross-validation procedure.
The Kullback-Leibler divergences for all runs.
The Jensen-Shannon divergences for all runs.
The average Kullback-Leibler divergence and average Jensen-Shannon divergence.
A k-fold cross validation for a compositional regression model is performed.
comp.reg, kl.compreg, compppr.tune, aknnreg.tune
# NOT RUN { y <- as.matrix( iris[, 1:3] ) y <- y / rowSums(y) x <- iris[, 4] mod <- cv.comp.reg(y, x) # }
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