AnotA {sensR} | R Documentation |
Computation of dprime and it's uncertainty for the monadic A-not-A test together with the one-tailed exact P-value of the difference test (Fisher's Exact test).
AnotA(x1, n1, x2, n2, ...)
x1 |
The number of (correct) A-answers on A-samples |
n1 |
The total number of A-samples |
x2 |
The number of A-answers on not-A-samples |
n2 |
The number of not-A-samples |
... |
Additional arguments passed to glm |
The function uses the glm
and fisher.test
functions
of the stats
package. Note that all arguments have to be
positive integers.
An object of class "discrim"
(which has a print method). This is
a list with elements
coef |
named vector of coefficients (d-prime) |
res.glm |
the glm-object from the fitting process |
vcov |
variance-covariance matrix of the coefficients |
se |
named vector with standard error of the coefficients (standard error of d-prime |
data |
a named vector with the data supplied to the function |
p.value |
one-sided p-value from Fisher's exact test (fisher.test ) |
test |
a string with the name of the test (A-Not A ) for
the print method |
call |
the matched call |
Rune Haubo B Christensen and Per Bruun Brockhoff
Brockhoff, P.B. and Christensen, R.H.B.(2008). Thurstonian models for sensory discrimination tests as generalized linear models. Manuscript for Food Quality and Preference.
print.discrim
, discrim
,
discrimPwr
, discrimSim
,
discrimSS
, findcr
# data: 10 of the A-samples were judged to be A # 20 A-samples in total # 3 of the not-A samples were judged to be A # 20 not-A-samples in total AnotA(10, 20, 3, 20) ## Extended example plotting the profile likelihood xt <- cbind(c(3, 10), c(20 - 3, 20 - 10)) lev <- gl(2, 1) summary(res <- glm(xt ~ lev, family = binomial(link = probit))) N <- 100 dev <- double(N) level <- c(0.95, 0.99) delta <- seq(1e-4, 5, length = N) for(i in 1:N) dev[i] <- glm(xt ~ 1 + offset(c(0, delta[i])), family = binomial(probit))$deviance plot(delta, exp(-dev/2), type = "l", xlab = expression(delta), ylab = "Normalized Profile Likelihood") ## Add Normal approximation: lines(delta, exp(-(delta - coef(res)[2])^2 / (2 * vcov(res)[2,2])), lty = 2) ## Add confidence limits: lim <- sapply(level, function(x) exp(-qchisq(x, df=1)/2) ) abline(h = lim, col = "grey")