Divergence based regression for compositional data with compositional data in the covariates side using the alpha-transformation {Compositional} | R Documentation |
Divergence based regression for compositional data with compositional data in the covariates side using the α-transformation.
kl.alfapcr(y, x, covar = NULL, a, k, xnew = NULL, B = 1, ncores = 1, tol = 1e-07, maxiters = 50)
y |
A numerical matrixc with compositional data with or without zeros. |
x |
A matrix with the predictor variables, the compositional data. Zero values are allowed. |
covar |
If you have other covariates as well put themn here. |
a |
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 α=0 the isometric log-ratio transformation is applied. |
k |
A number at least equal to 1. How many principal components to use. |
xnew |
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. |
B |
If B is greater than 1 bootstrap estimates of the standard error are returned. If B=1, no standard errors are returned. |
ncores |
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. |
tol |
The tolerance value to terminate the Newton-Raphson procedure. |
maxiters |
The maximum number of Newton-Raphson iterations. |
The α-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.
A list including:
runtime |
The time required by the regression. |
iters |
The number of iterations required by the Newton-Raphson in the kl.compreg function. |
loglik |
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}. |
be |
The beta coefficients. |
seb |
The standard error of the beta coefficients, if bootstrap is chosen, i.e. if B > 1. |
est |
The fitted values of xnew if xnew is not NULL. |
Initial code by Abdulaziz Alenazi. Modifications by Michail Tsagris.
R implementation and documentation: Abdulaziz Alenazi a.alenazi@nbu.edu.sa and Michail Tsagris mtsagris@uoc.gr.
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
klalfapcr.tune, tflr, pcr, glm.pcr, alfapcr.tune
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