MixedPsy (version 1.1.0)

PsychDelta: PSE/JND from GLM Using Delta Method

Description

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.

Usage

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

Value

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: \(Estimate +/- z(1-(\alpha/2)) * Std.Error\).

Arguments

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).

Details

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

References

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

See Also

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.

Examples

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)