PsychDelta: PSE/JND for univariable GLM Using Delta Method

Description Usage Arguments Details Value Note References See Also Examples

View source: R/psych_utils.R

Description

Estimate the Point of Subjective Equivalence (PSE), the Just Noticeable Difference (JND) and the related Standard Errors by means of Delta Method. The method only applies to univariable GLMs (psychometric functions) having a probit link function.

Usage

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PsychDelta(model, alpha = 0.05)

Arguments

model

the fitted psychometric function. An object of class "glm".

alpha

significance level of the confidence interval.

Details

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

Value

PsychDelta returns a matrix including Estimate, Standard Error, Inferior and Superior Confidence Interval of PSE and JND. Confidence Intervals are computed as: Estimate +/- z(1-(α/2)) * Std.Error.

Note

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.

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

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

See Also

glm for for Generalized Linear Models (without random effects) and glmer for Generalized Linear Mixed Models (including random effects). MixDelta and MixTreatment for univarible and multivariable GLMM, respectively (object of class "merMod"). pseMer for bootstrap-based confidence intervals.

Examples

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#load simulated data
data(simul_data)
#fit a glm (probit link)
model.glm = glm(formula = cbind(Longer, Total - Longer) ~ X,
family = binomial(link = "probit"), data = simul_data)
PsychDelta(model.glm)

MixedPsy documentation built on May 2, 2019, 3:40 p.m.