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Fast estimation of the value of
alfa.tune(x, B = 1, ncores = 1)A matrix with the compositional data. No zero vaues are allowed.
If no (bootstrap based) confidence intervals should be returned this should be 1 and more than 1 otherwise.
If ncores is greater than 1 parallel computing is performed. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process.
A vector with the best alpha, the maximised log-likelihood and the log-likelihood at
The best alpha and the value of the log-likelihod, along with the 95% bootstrap based confidence intervals.
A message with some information about the histogram.
The time (in seconds) of the process.
This is a faster function than alfa.profile for choosing the value of
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
# NOT RUN {
library(MASS)
x <- as.matrix(iris[, 1:4])
x <- x / rowSums(x)
alfa.tune(x)
alfa.profile(x)
# }
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