PsychDelta: PSE/JND from GLM Using Delta Method
In MixedPsy: Statistical Tools for the Analysis of Psychophysical Data

Description Usage Arguments Details Value Note References See Also Examples

View source: R/psych_utils.R

Estimate Point of Subjective Equivalence (PSE), Just Noticeable Difference (JND), and related Standard Errors of an individual participant by means of Delta Method. The method only applies to a GLM (object of class glm) with one continuous predictor and a probit link function.

1	PsychDelta(model.obj, alpha = 0.05, p = 0.75)

`model.obj`	the fitted psychometric function. An object of class `glm`.
`alpha`	significance level of the confidence interval.Default is 0.05 (95% confidence interval).
`p`	probability value relative to the JND upper limit. Default is 0.75 (value for 50% JND).

PsychDelta estimates PSE and JND of a psychometric function (object of class glm).

PsychDelta returns a matrix including estimate, standard error, inferior and superior bounds of the confidence interval of PSE and JND. Confidence Intervals are computed as: Estimate +/- z(1-(α/2)) * Std.Error.

The function assumes that the first model coefficient is the intercept and the second is the slope. The estimate of the JND assumes a probit link function.

Faraggi, D., Izikson, P., & Reiser, B. (2003). Confidence intervals for the 50 per cent response dose. Statistics in medicine, 22(12), 1977-1988. https://doi.org/10.1002/sim.1368

Knoblauch, K., & Maloney, L. T. (2012). Modeling psychophysical data in R (Vol. 32). Springer Science & Business Media.

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. doi:10.1167/12.11.26

glm for fitting a Generalized Linear Model to a single-subject response. glmer for Generalized Linear Mixed Models (including fixed and random effects). MixDelta for estimating PSE and JND at a population level with delta method.

data.S1 <- subset(simul_data, Subject == "S1")
model.glm = glm(formula = cbind(Longer, Total - Longer) ~ X,
family = binomial(link = "probit"), data = data.S1)
PsychDelta(model.glm)