kld: QFASA diet estimates using various distances

In scpropreg: Simplicially Constrained Regression Models for Proportions

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 \eta parameter in the gradient descent algorithm.

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 [e-mail].

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

mkld, pcls

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)

scpropreg documentation built on March 24, 2026, 5:07 p.m.