binom_revweib: Reverse Weibull model for the psychometric function

In modelfree: Model-Free Estimation of a Psychometric Function

Reverse Weibull model for the psychometric function

Description

This function finds the maximum likelihood estimates of the parameters of the reverse Weibull model for the psychometric function.

Usage

binom_revweib( r, m, x, p = 1, initK = 2, guessing = 0, lapsing = 0 )

Arguments

r	number of successes at points x
m	number of trials at points x
x	stimulus levels
p	(optional) degree of the polynomial; default is 1
initK	(optional) initial value for K (power parameter in reverse Weibull model); default is 2
guessing	(optional) guessing rate; default is 0
lapsing	(optional) lapsing rate; default is 0

Value

⁠b ⁠ vector of estimated coefficients for the linear part

⁠K ⁠ estiamte of the power parameter in the reverse Weibull model

⁠fit ⁠ glm object to be used in evaluation of fitted values

Examples

data("Miranda_Henson")
x = Miranda_Henson$x
r = Miranda_Henson$r
m = Miranda_Henson$m
numxfit <- 199; # Number of new points to be generated minus 1
xfit <- (max(x)-min(x)) * (0:numxfit) / numxfit + min(x)
val <- binom_revweib( r, m, x )
# Plot the fitted curve
plot( x, r / m, xlim = c( 0.1, 1.302 ), ylim = c( 0.0165, 0.965 ), type = "p", pch="*" )
pfit <- predict( val$fit, data.frame( x = xfit ), type = "response" )
lines(xfit, pfit, col = "green" )

modelfree documentation built on June 8, 2025, 10:07 a.m.