linear_in_log_odds_pwf: The linear in log odds probability weighting function.

In gary-au/pt: Computational models for prospect theory and other theories of risky decision making

Description

The linear in log odds probability weighting function is given by

w(p) = beta * p^alpha / (beta * p^alpha + (1 - p)^alpha),

where p is the probability constrained by

w(0) = 0, w(1) = 1, 0 < p < 1.

Usage

linear_in_log_odds_pwf(par, p)

Arguments

par	vector, contains the alpha and beta parameters for the pwf
p	numeric, the probability

References

p. 139, Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38, 129-166.

p. 208, Wakker, P. P. (2010). Prospect theory: For risk and ambiguity. Cambridge, UK: Cambridge University Press.

See Also

plotProbW, plotTwoParProbWFam

gary-au/pt documentation built on May 16, 2019, 5:41 p.m.