binom_lims: Maximum likelihood parameter estimates for a psychometric...

In modelfree: Model-Free Estimation of a Psychometric Function

Maximum likelihood parameter estimates for a psychometric function with guessing and lapsing rates

Description

This function finds the maximum likelihood estimates of the parameters of the psychometric function with guessing and lapsing rates, only guessing rate, or only lapsing rate.

Usage

binom_lims( r, m, x, gl = "both", link = "logit", p = 1, K = 2, initval = NULL )

Arguments

r	number of successes at points x
m	number of trials at points x
x	stimulus levels
gl	(optional) indicator, calulate only guessing if "guessing", only lapsing if "lapsing" and both guessing and lapsing if "both"; default is "both"
link	(optional) name of the link function; default is "logit"
p	(optional) degree of the polynomial; default is 1
K	(optional) power parameter for Weibull and reverse Weibull link; default is 2
initval	(optional) initial value for guessing and lapsing; default is c(.01 .01) if guessing and rates are estimated, and .01 if only guessing or only lapsing rate is estimated

Value

⁠b ⁠estimated coefficients for the linear part

⁠guessing ⁠ estimated guessing rate (if estimated)

⁠lapsing ⁠ estimated lapsing rate (if estimated)

⁠fit ⁠ glm object to be used in evaluation of fitted values

Examples

data("Baker_etal")
x = Baker_etal$x
r = Baker_etal$r
m = Baker_etal$m
plot( x, r / m, xlim = c( 0.16, 7.83 ), ylim = c( -0.01, 1.01 ), type = "p", pch="*" )
val <- binomfit_lims( r, m, x, link = "probit" )
numxfit <- 199; # Number of new points to be generated minus 1
xfit <- (max(x)-min(x)) * (0:numxfit) / numxfit + min(x)
# Plot the fitted curve
pfit<-predict( val$fit, data.frame( x = xfit ), type = "response" )
lines(xfit, pfit )

modelfree documentation built on May 31, 2023, 7:17 p.m.