hypothesis_func: Function to set penalties according to hypotheses

In NINAnor/oneimpact: Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies

Function to set penalties according to hypotheses

Description

Function to set penalties according to hypotheses

Usage

hypothesis_func(coefs, expectation = -1, phi_hyp = 1)

Arguments

coefs	Vector of estimated coefficients through a Ridge regression.
expectation	Expected/Hypothetical signal of the coefficient for a given covariate. Should take value hyp = -1 or hyp = +1 depending on the expected signal of the coefficient. If expectation = 0, no change is made and the penalty if penalty.factor = 1/abs(coef)**gamma.
phi_hyp	Additional penalty constant for the hypothesis-based penalties. A value in the interval 1, Inf where 1 is no additional penalty and higher values correspond to higher penalties when

Examples

# set coefficients
coefs <- c(-1, -0.5, -0.1, 0.8, 0.3, -0.1)
expected_sign <- -1
hypothesis_func(coefs)

x <- seq(-2, 2, length.out = 101)
plot(x, exp(x), ylab = "Penalty", xlab = "Coefficient")
plot(x, hypothesis_func(x), ylab = "Penalty", xlab = "Coefficient")
plot(x, hypothesis_func(x, phi_hyp = 50), ylab = "Penalty", xlab = "Coefficient")
plot(x, exp(x)*hypothesis_func(x, phi_hyp = 10), ylab = "Penalty", xlab = "Coefficient")

NINAnor/oneimpact documentation built on June 14, 2025, 12:27 a.m.