| Type: | Package |
| Title: | Simplicially Constrained Regression Models for Proportions |
| Version: | 1.0 |
| Date: | 2025-11-16 |
| Author: | Michail Tsagris [aut, cre] |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | quadprog, Rfast, Rfast2 |
| Description: | Simplicially constrained regression models for proportions in both sides. The constraint is always that the betas are non-negative and sum to 1. References: Iverson S.J.., Field C., Bowen W.D. and Blanchard W. (2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. <doi:10.1890/02-4105>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2025-11-16 11:01:55 UTC; mtsag |
| Repository: | CRAN |
| Date/Publication: | 2025-11-20 14:40:01 UTC |
Simplicially Constrained Regression Models for Proportions
Description
Simplicially Constrained Regression Models for Proportions. The constraint is always that the betas are non-negative and sum to 1.
Details
| Package: | scpropreg |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2025-11-16 |
Maintainers
Michail Tsagris <mtsagris@uoc.gr>.
Author(s)
Michail Tsagris mtsagris@uoc.gr
References
Iverson Sara J., Field Chris, Bowen W. Don and Blanchard Wade (2004) Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets. Ecological Monographs, 74(2): 211-235.
Positive and unit sum constrained least squares
Description
Positive and unit sum constrained least squares.
Usage
pcls(y, x)
mpcls(y, x)
Arguments
y |
The response variable. For the pcls() a numerical vector with observations, but for the mpcls() a numerical matrix. |
x |
A matrix with independent variables, the design matrix. |
Details
The constraint is that all beta coefficients are positive and sum to 1. That is
min \sum_{i=1}^n(y_i-\bm{x}_i\top\bm{\beta})^2 such that 0\leq \beta_j \leq 1 and \sum_{j=1}^d\beta_j=1.
The pcls() function performs a single regression model, whereas the mpcls() function performs a regression for each column of y.
Each regression is independent of the others.
Value
A list including:
coefficients |
A numerical matrix with the positively constrained beta coefficients. |
value |
A numerical vector with the mean squared error. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
See Also
Examples
x <- matrix(runif(30 * 8), ncol = 30)
x <- t( x / rowSums(x) )
y <- runif(30)
y <- y / sum(y)
pcls(y, x)
QFASA diet estimates for many predators using various distances
Description
QFASA diet estimates for many predators using various distances.
Usage
mkld(Y, x, tol = 1e-8, maxit = 50000, alpha = 0.1)
mait(Y, x, tol = 1e-8, maxit = 50000, alpha = 0.01)
mlsq(Y, x, tol = 1e-8, maxit = 50000, alpha = 0.01)
mlr(Y, x, tol = 1e-8, maxit = 100)
Arguments
Y |
The response variable, a matrix with values between 0 and 1 that sum to 1. For some functions, zero values are allowed. Every column corresponds to the food composition of a predator. The column-wise sums are equal to 1. |
x |
A matrix with independent variables, values between 0 and 1. Each column contains a prey's diet. The column-wise sums are equal to 1. |
tol |
The tolerance value to terminate the algorithm. |
maxit |
The maximum iterations allowed. |
alpha |
The step-size parameter of the fixed points iteration algorithm. This is similar to the |
Details
The function estimates the betas that minimize a distance. The fitted values are linear constraints of the observed xs. The constraint is that all beta coefficients are positive and sum to 1. That is
\hat{y}_i= \sum_{j=1}\bm{x}_{ij}\beta_j such that 0\leq \beta_j \leq 1 and \sum_{j=1}^d\beta_j=1.
Value
A list including:
coefficients |
A numerical matrix with the positively constrained beta coefficients. |
value |
A numerical vector with the value of the objective function. |
iters |
The number of iterations required until termination of the algorithm. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Iverson Sara J., Field Chris, Bowen W. Don and Blanchard Wade (2004) Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets. Ecological Monographs, 74(2): 211-235.
See Also
Examples
x <- matrix(runif(30 * 6), ncol = 30)
x <- t( x / rowSums(x) )
Y <- matrix(runif(30 * 10), ncol = 30)
Y <- t( Y / rowSums(Y) )
mkld(Y, x)
QFASA diet estimates using various distances
Description
QFASA diet estimates using various distances.
Usage
kld(y, x, tol = 1e-8, maxit = 50000, alpha = 0.1)
ait(y, x, tol = 1e-8, maxit = 50000, alpha = 0.01)
lsq(y, x, tol = 1e-8, maxit = 50000, alpha = 0.01)
jsd(y, x, tol = 1e-8, maxit = 300000, alpha = 0.01)
lr(y, x, tol = 1e-8, maxit = 100)
Arguments
y |
The response variable. The predator's food composition. A vector with values between 0 and 1 that sum to 1. For some functions, zero values are allowed. |
x |
A matrix with independent variables, values between 0 and 1. Each column contains a prey's diet. The column-wise sums are equal to 1. |
tol |
The tolerance value to terminate the algorithm. |
maxit |
The maximum iterations allowed. |
alpha |
The step-size parameter of the fixed points iteration algorithm. This is similar to the |
Details
The function estimates the betas that minimize a distance. The fitted values are linear constraints of the observed xs. The constraint is that all beta coefficients are positive and sum to 1. That is
\hat{y}_i= \sum_{j=1}\bm{x}_{ij}\beta_j such that 0\leq \beta_j \leq 1 and \sum_{j=1}^d\beta_j=1.
Value
A list including:
coefficients |
A numerical matrix with the positively constrained beta coefficients. |
value |
A numerical vector with the value of the objective function. |
iters |
The number of iterations required until termination of the algorithm. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Iverson Sara J., Field Chris, Bowen W. Don and Blanchard Wade (2004) Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets. Ecological Monographs, 74(2): 211-235.
See Also
Examples
x <- matrix(runif(30 * 8), ncol = 30)
x <- t( x / rowSums(x) )
y <- runif(30)
y <- y / sum(y)
kld(y, x)
ait(y, x)
lsq(y, x)
lr(y, x)