| Title: | Statistical Tools for the Analysis of Psychophysical Data |
|---|---|
| Description: | Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012). |
| Authors: | Alessandro Moscatelli [aut, cre] (0000-0001-6269-4536), Priscilla Balestrucci [aut] (0000-0002-5764-9439) |
| Maintainer: | Alessandro Moscatelli <[email protected]> |
| License: | GPL (>=2) |
| Version: | 1.2.0 |
| Built: | 2025-02-18 15:22:52 UTC |
| Source: | https://github.com/moskante/mixedpsy |
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.
MixDelta(xplode.obj, alpha = 0.05, p = 0.75)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.
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)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)
Plot response curve for each individual in a population sample, given a GLMM with one continuous predictor and one (optional) factorial predictor. If the factorial predictor is specified, the response is plotted separately for each individual and each predictor level.
MixPlot(xplode.obj, facet_by = NULL, showData = TRUE)MixPlot(xplode.obj, facet_by = NULL, showData = TRUE)
xplode.obj |
an object of class |
facet_by |
optional. A string specifying the name of the faceting variable (either the participant identification or the factorial predictor). |
showData |
logical, defines if proportion of binomial responses for each stimulus level are presented. Default is TRUE. |
If the model includes only a continuous predictor, the figure consist of a single panel, and each individual's response is assigned a different color. If a factorial predictor is included in the model, the faceting variable can be either the participant identification or the factorial predictor. By default, each panel shows an individual's response, different levels of the factorial predictor are coded by color.
MixPlot returns a ggplot object.
xplode for objects of class xplode.
ggplot2 for creating data visualizations.
PsychPlot for plotting a a psychometric function from a GLM.
library(lme4) 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") MixPlot(xplode.multi) #alternative visualization MixPlot(xplode.multi, facet_by = "vibration", showData = FALSE)library(lme4) 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") MixPlot(xplode.multi) #alternative visualization MixPlot(xplode.multi, facet_by = "vibration", showData = FALSE)
Estimates the Point of Subjective Equivalence (PSE), the Just Noticeable
Difference (JND) and the related Standard Errors by means of Bootstrap Method,
given an object of class merMod.
pseMer( mer.obj, B = 200, FUN = NULL, alpha = 0.05, ci.type = c("norm", "basic", "perc"), beep = F )pseMer( mer.obj, B = 200, FUN = NULL, alpha = 0.05, ci.type = c("norm", "basic", "perc"), beep = F )
mer.obj |
an object of class |
B |
integer. Number of bootstrap samples. |
FUN |
an optional, custom made function to specify the required parameters to be estimated.
If NULL, |
alpha |
significance level of the confidence intervals. Default is 0.05 (95% confidence interval). |
ci.type |
vector of character strings representing the type of intervals required. The value
should be any subset of the values accepted by |
beep |
logical. If TRUE, a "ping" sound alerts that the simulation is complete. Default is FALSE. |
pseMer estimates PSE and JND (and additional user defined parameters) from a
fitted GLMM model (class merMod).
pseMer returns a list of length 3 including a summary table (estimate,
inferior and superior bounds of the confidence interval), the output of bootMer, and that of
boot.ci, for further analyses. Confidence intervals in the summary table are
based on the percentile method.
A first custom function was written in 2012 for the non-CRAN package MERpsychophisics,
based on the algorithm in Moscatelli et al. (2012). The current function is a wrapper
of function bootMer and boot.ci.
Increasing the number of bootstrap samples (B) makes the estimate more reliable.
However, this will also increase the duration of the computation.
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
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 51. https://doi.org/10.18637/jss.v067.i01
bootMer and boot.ci for estimation of confidence intervals with the bootstrap method.
MixDelta for confidence intervals with delta method.
library(lme4) #example 1: univariable GLMM mod.uni = glmer(formula = cbind(Longer, Total - Longer) ~ X + (1 | Subject), family = binomial(link = "probit"), data = simul_data) BootEstim.uni <- pseMer(mod.uni, B = 100, ci.type = c("perc")) #example 2: specify custom parameters for multivariable model mod.multi <- glmer(cbind(faster, slower) ~ speed * vibration + (1 + speed| subject), family = binomial(link = "probit"), data = vibro_exp3) fun2mod = function(mer.obj){ #allocate space: 4 parameters (jnd_A, jnd_B, pse_A, pse_B) jndpse = vector(mode = "numeric", length = 4) names(jndpse) = c("pse_0", "pse_32","jnd_0", "jnd_32") jndpse[1] = -fixef(mer.obj)[1]/fixef(mer.obj)[2] #pse_0 jndpse[2] = -(fixef(mer.obj)[1]+fixef(mer.obj)[3])/(fixef(mer.obj)[2]+ fixef(mer.obj)[4]) #pse_0 jndpse[3] = qnorm(0.75)/fixef(mer.obj)[2] #jnd_0 jndpse[4] = qnorm(0.75)/(fixef(mer.obj)[2]+ fixef(mer.obj)[4]) #jnd_32 return(jndpse) } BootEstim.multi = pseMer(mod.multi, B = 100, FUN = fun2mod)library(lme4) #example 1: univariable GLMM mod.uni = glmer(formula = cbind(Longer, Total - Longer) ~ X + (1 | Subject), family = binomial(link = "probit"), data = simul_data) BootEstim.uni <- pseMer(mod.uni, B = 100, ci.type = c("perc")) #example 2: specify custom parameters for multivariable model mod.multi <- glmer(cbind(faster, slower) ~ speed * vibration + (1 + speed| subject), family = binomial(link = "probit"), data = vibro_exp3) fun2mod = function(mer.obj){ #allocate space: 4 parameters (jnd_A, jnd_B, pse_A, pse_B) jndpse = vector(mode = "numeric", length = 4) names(jndpse) = c("pse_0", "pse_32","jnd_0", "jnd_32") jndpse[1] = -fixef(mer.obj)[1]/fixef(mer.obj)[2] #pse_0 jndpse[2] = -(fixef(mer.obj)[1]+fixef(mer.obj)[3])/(fixef(mer.obj)[2]+ fixef(mer.obj)[4]) #pse_0 jndpse[3] = qnorm(0.75)/fixef(mer.obj)[2] #jnd_0 jndpse[4] = qnorm(0.75)/(fixef(mer.obj)[2]+ fixef(mer.obj)[4]) #jnd_32 return(jndpse) } BootEstim.multi = pseMer(mod.multi, B = 100, FUN = fun2mod)
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.
PsychDelta(model.obj, alpha = 0.05, p = 0.75)PsychDelta(model.obj, alpha = 0.05, p = 0.75)
model.obj |
the fitted psychometric function. An object of class |
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: .
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)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)
Fit psychometric functions using glm or brglm.
Estimate PSE, JND, and related confidence intervals with Delta Method.
PsychFunction(ps.formula, ps.link, ps.data, br = F)PsychFunction(ps.formula, ps.link, ps.data, br = F)
ps.formula |
an object of class |
ps.link |
link function for the binomial family of error distribution. Default is |
ps.data |
a data frame including the variables used in the model. |
br |
logical. If TRUE, |
Estimates are computed only for GLM of the type F(Y) ~ X, where X is a continuous
predictor. Std. Errors and 95% confidence intervals of PSE and JND are estimated via Delta Methods.
Currently only working with probit link function.
PsychFunction returns a list including the fitted model,
the estimate of PSE and JND and a flag to indicate if brglm was called.
PsychFunction returns the same parameter estimate as PsychDelta, without an explicit call to glm.
Moreover, it allows to fit the model using brglm in case of complete or quasi separation.
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. doi:10.1167/12.11.26
glm for Generalized Linear Models.
brglm for fitting a GLM using bias reduction.
PsychPlot for plotting a psychometric function given a glm (or brglm) object.
PsychPlot for plotting a a psychometric function from a GLM.
PsychShape for plotting a psychometric function given PSE and JND.
data.S1 <- subset(simul_data, Subject == "S1") psych.S1 <- PsychFunction(ps.formula = cbind(Longer, Total - Longer) ~ X, ps.link = "probit", ps.data = data.S1)data.S1 <- subset(simul_data, Subject == "S1") psych.S1 <- PsychFunction(ps.formula = cbind(Longer, Total - Longer) ~ X, ps.link = "probit", ps.data = data.S1)
This function generates an interpolated dataset by predicting values across a range of an independent variable from a list of generalized linear models (GLMs).
PsychInterpolate(model_list, n_points = 100)PsychInterpolate(model_list, n_points = 100)
model_list |
A structured list of grouped models obtained from |
n_points |
An integer number. It specifies the number of points to interpolate along the independent variable range. Default is 100. |
The function takes a structured list of models, as produced by PsychModels, and generates a new dataset with interpolated values for the independent variable.
Predictions are computed at evenly spaced points across the observed range for each model, and the results are returned in a long-format data frame.
A data frame containing the interpolated independent variable, the corresponding predicted values from the GLM model, and columns for the grouping factors.
model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X, data = simul_data, group_factors = "Subject") longData <- PsychInterpolate(model_list) # use the interpolated dataset to plot model: library(ggplot2) ggplot(longData, aes(X, prediction, color = Subject)) + geom_line() + geom_point(data = simul_data, aes(X, Longer/Total))model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X, data = simul_data, group_factors = "Subject") longData <- PsychInterpolate(model_list) # use the interpolated dataset to plot model: library(ggplot2) ggplot(longData, aes(X, prediction, color = Subject)) + geom_line() + geom_point(data = simul_data, aes(X, Longer/Total))
The function fits psychometric functions to data using glm for multiple groups. It supports the use of a binomial error distribution.
PsychModels(data, group_factors = NULL, formula, link = "probit")PsychModels(data, group_factors = NULL, formula, link = "probit")
data |
a data frame containing the variables to be used in the model. |
group_factors |
a character vector specifying the grouping variables in the dataset. If |
formula |
the formula of the |
link |
the link function. A character string specifying the link function to be used. By default, |
This function allows the fitting of psychometric functions to grouped data. If grouping variables are provided through group_factors, separate models are fit to each group. The function returns a list of models, one for each group, where the model for each group is fitted using the specified formula and link.
The models are returned as a named list, with each list element containing the fitted GLM model and the associated group-level information.
model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X, data = simul_data, group_factors = "Subject") model_list_vibro <- PsychModels(vibro_exp3, group_factors = c("subject", "vibration"), formula = cbind(faster, slower) ~ speed)model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X, data = simul_data, group_factors = "Subject") model_list_vibro <- PsychModels(vibro_exp3, group_factors = c("subject", "vibration"), formula = cbind(faster, slower) ~ speed)
This function calculates the Point of Subjective Equality (PSE) and Just Noticeable Difference (JND) from a list of fitted Generalized Linear Models (GLMs). It extracts these parameters using the PsychDelta function and returns them in a structured dataframe.
PsychParameters(model_list, se = TRUE)PsychParameters(model_list, se = TRUE)
model_list |
A structured list of grouped models obtained from |
se |
Logical. if |
The function supports three types of input:
A structured list of models (as produced by PsychModels): Extracts PSE and JND for each model and includes the corresponding grouping factors in the output.
A single GLM model: Returns a one-row data frame with PSE, JND, and (if requested) standard errors.
A list of GLM models: Computes PSE and JND for each model and returns a data frame.
A data frame containing PSE and JND estimates, along with their standard errors (if se = TRUE).
If the input is a grouped list of models, the output includes columns for the grouping factors.
model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X, data = simul_data, group_factors = "Subject") psych_parameters <- PsychParameters(model_list) model_list_vibro <- PsychModels(vibro_exp3, group_factors = c("subject", "vibration"), formula = cbind(faster, slower) ~ speed) psych_parameters_vibro <- PsychParameters(model_list_vibro)model_list <- PsychModels(formula = cbind(Longer, Total - Longer) ~ X, data = simul_data, group_factors = "Subject") psych_parameters <- PsychParameters(model_list) model_list_vibro <- PsychModels(vibro_exp3, group_factors = c("subject", "vibration"), formula = cbind(faster, slower) ~ speed) psych_parameters_vibro <- PsychParameters(model_list_vibro)
Plot a psychometric function given an object of class glm or brglm.
The plot can be drawn on a new or existing ggplot object.
PsychPlot( model.obj, addTo = NULL, showData = TRUE, ps.type = "solid", ps.size = 1, ps.lab = "" )PsychPlot( model.obj, addTo = NULL, showData = TRUE, ps.type = "solid", ps.size = 1, ps.lab = "" )
model.obj |
the fitted psychometric function. An object of class |
addTo |
specifies an existing |
showData |
logical, defines if proportion of binomial responses for each stimulus level are presented. Default is TRUE. |
ps.type, ps.size
|
type and size of the plotted line (see |
ps.lab |
label assigned to the psychometric curve. The label is coded by the color aesthetic. |
PsychPlot returns a ggplot object.
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
Knoblauch, K., & Maloney, L. T. (2012). Modeling psychophysical data in R (Vol. 32). Springer Science & Business Media.
glm for for Generalized Linear Models.
PsychFunction for estimation of PSE and JND.
MixPlot for plotting individual responses from a GLMM.
data.S1 <- subset(simul_data, Subject == "S1") psych.S1 <- PsychFunction(ps.formula = cbind(Longer, Total - Longer) ~ X, ps.link = "probit", ps.data = data.S1) plotP1 <- PsychPlot(psych.S1$model, showData = TRUE, ps.lab = "S1") data.S2 <- subset(simul_data, Subject == "S2") glm.S2 <- glm(formula = cbind(Longer, Total - Longer) ~ X, family = binomial(link = "probit"), data = data.S2) plotP2 <- PsychPlot(glm.S2, addTo = plotP1, ps.lab = "S2")data.S1 <- subset(simul_data, Subject == "S1") psych.S1 <- PsychFunction(ps.formula = cbind(Longer, Total - Longer) ~ X, ps.link = "probit", ps.data = data.S1) plotP1 <- PsychPlot(psych.S1$model, showData = TRUE, ps.lab = "S1") data.S2 <- subset(simul_data, Subject == "S2") glm.S2 <- glm(formula = cbind(Longer, Total - Longer) ~ X, family = binomial(link = "probit"), data = data.S2) plotP2 <- PsychPlot(glm.S2, addTo = plotP1, ps.lab = "S2")
Plot a psychometric function with known PSE and JND on a new or existing ggplot object.
PsychShape( pse = 0, jnd = 1, p = 0.75, x.range = c(NA, NA), ps.link = c("probit"), ps.type = "solid", ps.size = 1, ps.color = "black", addTo = NULL )PsychShape( pse = 0, jnd = 1, p = 0.75, x.range = c(NA, NA), ps.link = c("probit"), ps.type = "solid", ps.size = 1, ps.color = "black", addTo = NULL )
pse, jnd
|
point of subjective equivalence (PSE) and just noticeable difference (JND) of the desired psychometric function. |
p |
probability value relative to the JND upper limit. Default is 0.75 (value for 50% JND). |
x.range |
vector of length two specifying the range of the psychometric function. |
ps.link |
a link function for the binomial family of error distribution. |
ps.type, ps.size, ps.color
|
type, size, and color of the plotted line (see |
addTo |
specifies an existing |
PsychShape() can be used to visualize the predicted results of a
psychophysical experiment or to plot a fitted psychometric function whose
values of pse and jnd are known. Currently only working with probit and logit
link function.
PsychShape returns a ggplot object.
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
Knoblauch, K., & Maloney, L. T. (2012). Modeling psychophysical data in R (Vol. 32). Springer Science & Business Media.
glm for for Generalized Linear Models.
PsychFunction and PsychDelta for estimation of PSE and JND from response data.
PsychPlot for plotting a a psychometric function from a GLM.
p <- PsychShape(pse = 0, jnd = 6, x.range = c(-40, 40), ps.color = "gray", ps.size = 3) p1 <- PsychShape(pse = 6, jnd = 6, x.range = c(-40, 40), ps.col = "black", addTo = p) p2 <- PsychShape(pse = 6, jnd = 6, x.range = c(-40, 40), ps.col = "red", ps.link = "logit", ps.type = "dashed", addTo = NULL)p <- PsychShape(pse = 0, jnd = 6, x.range = c(-40, 40), ps.color = "gray", ps.size = 3) p1 <- PsychShape(pse = 6, jnd = 6, x.range = c(-40, 40), ps.col = "black", addTo = p) p2 <- PsychShape(pse = 6, jnd = 6, x.range = c(-40, 40), ps.col = "red", ps.link = "logit", ps.type = "dashed", addTo = NULL)
The function simulates data from a typical psychophysics experiment using a 2-alternative forced choice task and the method of constant stimuli. For each simulated participant, the function returns the following information: individual slope and intercept coefficients, based on the fixed and random effects parameters provided as input; summary of the simulated binomial response across a range of intensity levels within a specified stimulus range; individual lapse and guess rates, if applicable.
PsySimulate( fixeff = c(-7, 0.0875), raneff = c(2.4, -0.002, 2e-06), nsubjects = 8, ntrials = 40, nintervals = 9, xint = c(40, 120), constant = T, lapse = FALSE, guess = FALSE, lapse_range = c(0, 0.05), guess_range = c(0, 0.05) )PsySimulate( fixeff = c(-7, 0.0875), raneff = c(2.4, -0.002, 2e-06), nsubjects = 8, ntrials = 40, nintervals = 9, xint = c(40, 120), constant = T, lapse = FALSE, guess = FALSE, lapse_range = c(0, 0.05), guess_range = c(0, 0.05) )
fixeff |
Numeric array of fixed effects. The first item is the intercept, the second element is the slope. |
raneff |
Numeric array of random effects. The first element is the intercept, the second is the covariance, the third is the slope variance. |
nsubjects |
Integer. Number of subjects to simulate. Default is 8. |
ntrials |
Integer. Number of trials for each stimulus level. Default is 40. |
nintervals |
Integer. Number of stimulus levels. Default is 9. |
xint |
Numeric array specifying the range of stimulus intensity. Default is c(40,120) |
constant |
Logical. If TRUE (defualt), stimulus levels are evenly spaced across 'xint'. If FALSE, stimulus levels are randomly generated within the interval. |
lapse |
Logical or numeric. If FALSE (default), no lapse rate is applied. If TRUE, a random lapse rate is drawn from 'lapse_range'. If a numeric value is provided, all subjects will have the same lapse rate. |
guess |
Logical or numeric. If FALSE (default), no guessing rate is applied. If TRUE, a random guessing rate is drawn from 'guess_range'. If a numeric value is provided, all subjects will have the same guess rate. |
lapse_range |
Numeric array defining the minimum and maximum lapse rates when 'lapse = TRUE'. Default is c(0, 0.05). |
guess_range |
Numeric array defining the minimum and maximum guessing rates when 'guess = TRUE'. Default is c(0.05, 0.10). |
A data frame containing simulated psychophysical data with the following columns:
Subject - Subject identifier.
X - Stimulus intensity levels.
Intercept - Individual intercept values.
Slope - Individual slope values.
Gamma (optional) - Guess rate, included if 'guess' is not FALSE.
Lambda (optional) - Lapse rate, included if 'lapse' is not FALSE.
Longer - Number of "Longer" responses at each stimulus level.
Total - Total number of trials per stimulus level.
PsychShape for plotting a psychometric function given PSE and JND. simul_data for a dataset simulated with the function.
datafr.S1 <- PsySimulate(fixeff = c(0, 1), xint = c(-5,5), nsubject = 1, ntrials = 60, nintervals = 10, constant = FALSE) simul_data <- PsySimulate(ntrials = 160, nsubjects = 10, guess = TRUE, lapse = TRUE)datafr.S1 <- PsySimulate(fixeff = c(0, 1), xint = c(-5,5), nsubject = 1, ntrials = 60, nintervals = 10, constant = FALSE) simul_data <- PsySimulate(ntrials = 160, nsubjects = 10, guess = TRUE, lapse = TRUE)
A dataset containing simulated responses to a forced-choice task for eight clusters. Created using
PsySimulate(). The variables are as follows:
data(simul_data)data(simul_data)
A data frame with 72 rows (9 observations x 8 simulated participants) and 6 variables:
samples in the continuous interval (range c(40,120))
simulated participant's coefficients (combination of random and fixed effect)
number of trials in which response is judged "longer" than standard
total number of trials for sample in X
simulated participant's identification code (S1 to S8)
PsySimulate for simulating dataframes with custom parameters.
A dataset containing the response recorded from a tactile discrimination task (nine participants). In a forced-choice experiment, participants were required to discriminate the motion speed of a moving surface by touching it. Simultaneously with the motion stimulus, a 32Hz masking vibration occurred in half of the trials.
data(vibro_exp3)data(vibro_exp3)
A data frame with 126 rows (14 observations x 9 participants) and 5 variables:
numeric, speed of the moving surface (in cm/s, range c(1,16))
factor representing the vibration frequency of the masking stimulus. Two levels: 32 (vibration in the experimental condition, in Hz) or 0 (no vibration - control condition)
proportion of trials in which the comparison stimulus was judged as faster than the reference
proportion of trials in which the comparison stimulus was judged as slower than the reference
participant's identification code
Original data were published in Dallmann et al. (2015).
Dallmann, C. J., Ernst, M. O., & Moscatelli, A. (2015). The role of vibration in tactile speed perception. Journal of Neurophysiology, 114(6), 3131–3139. <doi:10.1152/jn.00621.2015>
Extract values from an object of class merMod
(more specifically, from an object of subclass glmerMod).
xplode(model.obj, name.cont = NA, name.factor = NA, names.response = NA)xplode(model.obj, name.cont = NA, name.factor = NA, names.response = NA)
model.obj |
The GLMM fitted with |
name.cont |
A string providing the name of the continuous predictor, as in the formula object of the fitted model |
name.factor |
A string providing the name of name of the categorical predictor, as in the formula object of the fitted model |
names.response |
Optional. A string providing the name of name of the response variable, as in the formula object of the fitted model |
For simplicity and maintenance reasons, several MixedPsy functions take as input an
object of class xplode instead of an object of class merMod.
merMod-class and glmer.
MixDelta, MixPlot for use of objects of class xplode.
library(lme4) multi.mod <- glmer(cbind(faster, slower) ~ speed * vibration + (1 + speed| subject), family = binomial(link = "probit"), data = vibro_exp3) xplode.mod <- xplode(multi.mod, name.cont = "speed", name.factor = "vibration") MixPlot(xplode.mod) MixDelta(xplode.mod)library(lme4) multi.mod <- glmer(cbind(faster, slower) ~ speed * vibration + (1 + speed| subject), family = binomial(link = "probit"), data = vibro_exp3) xplode.mod <- xplode(multi.mod, name.cont = "speed", name.factor = "vibration") MixPlot(xplode.mod) MixDelta(xplode.mod)