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Ridge regression with compositional data in the covariates side using the
alfa.ridge(y, x, a, lambda, B = 1, xnew = NULL)
A numerical vector containing the response variable values. If they are percentages, they are mapped onto
A matrix with the predictor variables, the compositional data. Zero values are allowed, but you must be carefull to choose strictly positive vcalues of
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If
The value of the regularisation parameter,
If B > 1 bootstrap estimation of the standard errors is implemented.
A matrix containing the new compositional data whose response is to be predicted. If you have no new data, leave this NULL as is by default.
The output of the ridge.reg.
The
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. http://arxiv.org/pdf/1508.01913v1.pdf
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. http://arxiv.org/pdf/1106.1451.pdf
# NOT RUN {
library(MASS)
y <- as.vector(fgl[, 1])
x <- as.matrix(fgl[, 2:9])
x <- x/ rowSums(x)
mod1 <- alfa.ridge(y, x, a = 0.5, lambda = 0.1, B = 1, xnew = NULL)
mod2 <- alfa.ridge(y, x, a = 0.5, lambda = 1, B = 1, xnew = NULL)
# }
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