
Multivariate or univariate regression with compositional data in the covariates side using the
alfa.pcr(y, x, a, k, model = "gaussian", xnew = NULL)
A numerical vector containing the response variable values. They can be continuous, binary, discrete (counts). This can also be a vector with discrete values or a factor for the multinomial regression (model = "multinomial").
A matrix with the predictor variables, the compositional data.
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
A number at least equal to 1. How many principal components to use.
The type of regression model to fit. The possible values are "gaussian", "multinomial", "binomial" and "poisson".
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.
A list tincluding:
If linear regression was fitted, the regression coefficients of the k principal component scores on the response variable y.
If another regression model was fitted its outcome as produced in the package Rfast.
The percentage of variance explained by the first k principal components.
The first k principal components, loadings or eigenvectors. These are useful for future prediction in the sense that one needs not fit the whole model again.
If the argument "xnew" was given these are the predicted or estimated values, otherwise it is NULL.
The
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://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. https://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)
mod <- alfa.pcr(y = y, x = x, 0.7, 1)
mod
# }
Run the code above in your browser using DataLab