power.fx: Function to computes the result of the power model, as a...

In biometrics: A Package for Biometrics and Modelling

Function to computes the result of the power model, as a classical allometric functional form.

Description

Function of the power model, based upon the model parameters, and a single predictor variable as follows

y_i = \alpha x_i^{\beta}

where: y_i and x_i are the response and predictor variable, respectively for the i-th observation; and the rest are parameters (i.e., coefficients).

Usage

power.fx(x, alpha, beta, upsilon = 0)

Arguments

x	is the predictor variable.
alpha	is the coefficient-parameter \alpha.
beta	is the coefficient-parameter \beta.
upsilon	is an optional constant term that force the prediction of y when x=0. Thus, the new model becomes y_i = \Upsilon+ f(x_i,\mathbf{\theta}), where \mathbf{\theta} is the vector of coefficients of the above described function represented by f(\cdot). The default value for \Upsilon is 0.

Value

Returns the response variable based upon the predictor variable and the coefficients.

Author(s)

Christian Salas-Eljatib.

References

• Salas-Eljatib C. 2025. Funciones alométricas: reparametrizaciones y características matemáticas. Documento de trabajo No. 1, Serie: Cuadernos de biometría, Laboratorio de Biometría y Modelación Forestal, Universidad de Chile. Santiago, Chile. 51 p. https://biometriaforestal.uchile.cl

Examples

# Predictor variable to be used is 30 
# Using the function
power.fx(x=30,alpha=2.86,beta=.49)
 

biometrics documentation built on March 20, 2026, 5:09 p.m.