psyphy (version 0.3)

mafc: Links for Binomial Family for m-alternative Forced-choice

Description

These provide links for the binomial family for fitting m-alternative forced-choice psychophysical functions.

Usage

mafc.logit( .m = 2 )
mafc.probit( .m = 2 )
mafc.cloglog( .m = 2 )
mafc.weib( ... )
mafc.cauchit( .m = 2 )

Value

Each link returns a list containing functions required for relating the response to the linear predictor in generalized linear models and the name of the link.

linkfun

The link function

linkinv

The inverse link function

mu.eta

The derivative of the inverse link

valideta

The domain over which the linear predictor is valid

link

A name to be used for the link

Arguments

.m

is the integer number (>1) of choices (Default to 2AFC). For m = 1 (Yes/No paradigm), use one of the built-in links for the binomial family.

...

just to pass along the formals of mafc.cloglog.

Author

Kenneth Knoblauch

Details

These functions provide links for fitting psychometric functions arising from an m-alternative forced-choice experiment. The estimated coefficients of the linear predictor influence both the location and the slope of the psychometric function(s), but provide no means of estimating the upper aymptote which is constrained to approach 1. If the upper asympotote must be estimated, it would be better to maximize directly the likelihood, either with a function like optim or gnlr from package gnlm (available at https://www.commanster.eu/rcode.html). Alternatively, the function probit.lambda can be used with a known upper asymptote, or glm.lambda or glm.WH to estimate one, with a probit link. mafc.weib is just an alias for mafc.cloglog.

References

Williams J, Ramaswamy D and Oulhaj A (2006) 10 Hz flicker improves recognition memory in older people BMC Neurosci. 2006 5;7:21 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1434755/ (for an example developed prior to this one, but for m = 2).

Klein S. A. (2001) Measuring, estimating, and understanding the psychometric function: a commentary. Percept Psychophys., 63(8), 1421--1455.

Wichmann, F. A. and Hill, N. J. (2001) The psychometric function: I.Fitting, sampling, and goodness of fit. Percept Psychophys., 63(8), 1293--1313.

Yssaad-Fesselier, R. and Knoblauch, K. (2006) Modeling psychometric functions in R. Behav Res Methods., 38(1), 28--41. (for examples with gnlr).

See Also

family, make.link, glm, optim, probit.lambda, glm.lambda, glm.WH

Examples

#A toy example,
b <- 3.5
g <- 1/3
d <- 0.0
a <- 0.04
p <- c(a, b, g, d)
num.tr <- 160
cnt <- 10^seq(-2, -1, length = 6) # contrast levels

#simulated observer responses
truep <- g + (1 - g - d) * pweibull(cnt, b, a)
ny <- rbinom(length(cnt), num.tr, truep)
nn <- num.tr - ny
phat <- ny/(ny + nn)
resp.mat <- matrix(c(ny, nn), ncol = 2)

ddprob.glm <- glm(resp.mat ~ cnt, family = binomial(mafc.probit(3)))
ddlog.glm <- glm(resp.mat ~ cnt, family = binomial(mafc.logit(3)))
# Can fit a Weibull function, but use log contrast as variable
ddweib.glm <- glm(resp.mat ~ log(cnt), family = binomial(mafc.cloglog(3))) 
ddcau.glm <- glm(resp.mat ~ log(cnt), family = binomial(mafc.cauchit(3)))

plot(cnt, phat, log = "x", cex = 1.5, ylim = c(0, 1))
pcnt <- seq(0.01, 0.1, len = 100)
lines(pcnt, predict(ddprob.glm, data.frame(cnt = pcnt),
			type = "response"), lwd = 2)
lines(pcnt, predict(ddlog.glm, data.frame(cnt = pcnt),
			type = "response"), lwd = 2, lty = 2)
lines(pcnt, predict(ddweib.glm, data.frame(cnt = pcnt),
			type = "response"), lwd = 3, col = "grey")
lines(pcnt, predict(ddcau.glm, data.frame(cnt = pcnt),
			type = "response"), lwd = 3, col = "grey", lty = 2)

# Weibull parameters \alpha and \beta
cc <- coef(ddweib.glm)
alph <- exp(-cc[1]/cc[2])
bet <- cc[2]


#More interesting example with data from Yssaad-Fesselier and Knoblauch
data(ecc2)
ecc2.glm <- glm(cbind(Correct, Incorrect) ~ Contr * Size * task, 
			family = binomial(mafc.probit(4)), data = ecc2)
summary(ecc2.glm)
ecc2$fit <- fitted(ecc2.glm)
library(lattice)
xyplot(Correct/(Correct + Incorrect) ~ Contr | Size * task, data = ecc2,
	subscripts = TRUE, ID = with(ecc2, Size + as.numeric(task)),
	scale = list(x = list(log = TRUE), 
				 y = list(limits = c(0, 1.05))),
	xlab = "Contrast", ylab = "Proportion Correct Response",
	aspect = "xy",
	panel = function(x, y, subscripts, ID, ...) {
		which = unique(ID[subscripts])
		llines(x, ecc2$fit[which ==ID], col = "black", ...)
		panel.xyplot(x, y, pch = 16, ...)
		panel.abline(h = 0.25, lty = 2, ...)
		}
)