| binom_lims {modelfree} | R Documentation |
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 )