Divergence based regression for compositional data with compositional data in the covariates side using the \(\alpha\)-transformation.
kl.alfapcr(y, x, covar = NULL, a, k, xnew = NULL, B = 1, ncores = 1, tol = 1e-07,
maxiters = 50)
A numerical matrixc with compositional data with or without zeros.
A matrix with the predictor variables, the compositional data. Zero values are allowed.
If you have other covariates as well put themn here.
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 \(\alpha=0\) the isometric log-ratio transformation is applied.
A number at least equal to 1. How many principal components to use.
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.
If B is greater than 1 bootstrap estimates of the standard error are returned. If B=1, no standard errors are returned.
If ncores is 2 or more parallel computing is performed. This is to be used for the case of bootstrap. If B=1, this is not taken into consideration.
The tolerance value to terminate the Newton-Raphson procedure.
The maximum number of Newton-Raphson iterations.
A list including:
The time required by the regression.
The number of iterations required by the Newton-Raphson in the kl.compreg function.
The log-likelihood. This is actually a quasi multinomial regression. This is bascially minus the half deviance, or \(- sum_{i=1}^ny_i\log{y_i/\hat{y}_i}\).
The beta coefficients.
The standard error of the beta coefficients, if bootstrap is chosen, i.e. if B > 1.
The fitted values of xnew if xnew is not NULL.
The \(\alpha\)-transformation is applied to the compositional data first, the first k principal component scores are calcualted and used as predictor variables for the Kullback-Leibler divergence based regression model.
Alenazi A. (2019). Regression for compositional data with compositioanl data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219-238. http://www.jds-online.com/file_download/688/01+No.10+315+REGRESSION+FOR+COMPOSITIONAL+DATA+WITH+COMPOSITIONAL+DATA+AS+PREDICTOR+VARIABLES+WITH+OR+WITHOUT+ZERO+VALUES.pdf
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 <- rdiri(214, runif(4, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- alfa.pcr(y = y, x = x, a = 0.7, k = 1)
mod
# }
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