Description Usage Arguments Details Value Note References See Also Examples

Estimate Points of Subjective Equivalence (PSE), Just Noticeable
Differences (JND) and the related Standard Errors from a GLMM by means of
delta method.
The method applies to models with a *probit* link function, one continuous predictor,
and one (optional) factorial predictor.

1 | ```
MixDelta(xplode.obj, alpha = 0.05, p = 0.75)
``` |

`xplode.obj` |
an object of class |

`alpha` |
significance level of the confidence intervals. Default is 0.05 (value for 95% confidence interval). |

`p` |
probability value relative to the JND upper limit. Default is 0.75 (value for 50% JND). |

When the model includes a factorial predictor, the function is based on a recursive use of
`glmer`

and re-order of levels of the factorial predictor.
The JND estimate assumes a *probit* link function.

A matrix including estimate, standard error, inferior and superior bounds of the confidence interval of PSE and JND. If a factorial predictor is included in the model, the function returns a list, each item containing a matrix for the estimates relative to a level of the predictor.

The delta method is based on the assumption of asymptotic normal distribution of the parameters estimates.
This may result in an incorrect variance estimation. For a more reliable (but more time-consuming) estimation
based on bootstrap method, use `pseMer`

.

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

Casella, G., & Berger, R. L. (2002). Statistical inference (2nd ed.). Pacific Grove, CA: Duxbury Press

`glmer`

for fitting Generalized Linear Mixed Models.
`xplode`

for interfacing values from a fitted GLMM to `MixedPsy`

functions.
`pseMer`

for bootstrap-based confidence intervals of psychometric parameters.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
library(lme4)
#univariable GLMM (one continuous predictor)
mod.uni = glmer(formula = cbind(Longer, Total - Longer) ~ X + (1 | Subject),
family = binomial(link = "probit"), data = simul_data)
xplode.uni = xplode(model = mod.uni, name.cont = "X")
MixDelta(xplode.uni)
#multivariable GLMM (one continuous and one factorial predictor)
mod.multi <- glmer(cbind(faster, slower) ~ speed * vibration + (1 + speed| subject),
family = binomial(link = "probit"), data = vibro_exp3)
xplode.multi <- xplode(model = mod.multi, name.cont = "speed", name.factor = "vibration")
MixDelta(xplode.multi)
``` |

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