MixTreatment: PSE/JND for Multivariable GLMM Using Delta Methods
In MixedPsy: Statistical Tools for the Analysis of Psychophysical Data

Description Usage Arguments Details Value References See Also Examples

View source: R/MixTreatment.R

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 glmer and MixDelta

1	MixTreatment(xplode.obj, datafr)

`xplode.obj`	an object of class `xplode.obj`. The fitted model (object of class `"merMod"`) from `xplode.obj` includes one continuous predictor and one factorial predictor.
`datafr`	the data frame fitted with the GLMM model

The function MixTreatment is based on a recursive use of glmer and PsychDelta to multivariable GLMM including continuous and factorial predictors. The same caveats of PsychDelta 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

glmer for Generalized Linear Mixed Models (including random effects).MixDelta for univariable model with delta method. pseMer for bootstrap-based confidence intervals.

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)