Estimate the Point of Subjective Equivalence (PSE), the Just Noticeable
Difference (JND) and the related Standard Errors for a multivariate distribution by means of Delta Method.
The method applies to multivariable GLMM having a probit link function.
The function is based on a recursive use of
an object of class
the data frame fitted with the GLMM model
MixTreatment is based on a recursive use of
PsychDelta to multivariable GLMM including
continuous and factorial predictors. The same caveats of
apply (e.g., confidence interval based on normality assumption).
A list, whose lenght is equal to the levels of the factorial predictor, i.
Each cell of the list is equal to the output of
delta.psy.probit applied to
a multivariable model whose baseline is level i of the factorial predictor.
Moscatelli, A., Mezzetti, M., & Lacquaniti, F. (2012). Modeling psychophysical data at the population-level: The generalized linear mixed model. Journal of Vision, 12(11):26, 1-17. https://doi.org/10.1167/12.11.26
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library(lme4) data(vibro_exp3) formula.mod <- cbind(faster, slower) ~ speed * vibration + (1 + speed| subject) mod <- glmer(formula = formula.mod, family = binomial(link = "probit"), data = vibro_exp3) xplode.mod <- xplode(model = mod, name.cont = "speed", name.factor = "vibration") MixTreatment(xplode.mod, vibro_exp3)
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