group.test(groups, A = 1:I, s = rep(1/J, J), constrained = TRUE)1:I, where I is the number of stimuli1/J for all parameters,
where J is the number of parameters Overall compares a 1-parameter Poisson model to a saturated
Poisson model, thereby testing the equality of the frequencies in each cell
of the array. This test corresponds to simultaneously testing for a null
effect of (1) the context induced by a given pair, (2) the grouping factor,
(3) the stimuli, and (4) the imbalance between pairs. The deviances of
the remaining tests sum to the total deviance associated with the overall
test.
EBA.g tests an EBA group model against a saturated
binomial group model, which corresponds to a goodness of fit test of
the EBA group model.
Group tests an EBA model having its parameters restricted to
be equal across groups (single set of
parameters) against the EBA group model allowing its parameters to vary
freely across groups (one set of parameters per
group); this corresponds to testing for group differences.
Effect tests an indifference model (where all choice probabilities
equal 0.5) against the restricted EBA model,
which corresponds to testing for a stimulus effect.
Imbalance tests for differences in the number of observations
per pair by comparing the average sample size (1-parameter Poisson model)
to the actual sample sizes (saturated Poisson model).
See Duineveld, Arents, & King (2000) for further details, and Choisel &
Wickelmaier (2007) for an application.
Choisel, S., & Wickelmaier, F. (2007). Evaluation of multichannel reproduced sound: scaling auditory attributes underlying listener preference. Journal of the Acoustical Society of America, 121, 388--400.
eba, wald.test.data(pork) # Is there a difference between Judge 1 and Judge 2?
groups <- array(c(apply(pork[,,1:5], 1:2, sum),
apply(pork[,,6:10], 1:2, sum)), c(3,3,2))
group.test(groups) # Yes, there is.Run the code above in your browser using DataLab