Description Usage Arguments Value References See Also Examples
quickpsy
fits, by direct maximization of the likelihood
(Prins and Kingdom, 2010; Knoblauch and Maloney, 2012),
psychometric functions of the form
ψ(x) = γ + (1  γ  λ) * fun(x)
where γ is the guess rate, λ is the lapse rate and fun is a sigmoidalshape function with asymppotes at 0 and 1.
1 2 3 4 
d 
Data frame with the results of a YesNo experiment to fit. It should have a tidy form in which each column corresponds to a variable and each row is an observation. 
x 
Name of the explanatory variable. 
k 
Name of the response variable. The response variable could be the number of trials in which a yestype response was given or a vector of 0s (or 1s; notype response) and 1s (yestype response) indicating the response on each trial. 
n 
Only necessary if 
grouping 
Name of the grouping variables. It should be specified as

random 
Name of the random variable. It should be specified as

within 
Name of the within variable. It should be specified as

between 
Name of the between variable. It should be specified as

xmin 
Minimum value of the explanatory variable for which the curves should be calculated (the default is the minimum value of the explanatory variable). 
xmax 
Maximum value of the explanatory variable for which the curves should be calculated (the default is the maximum value of the explanatory variable). 
log 
If 
fun 
Name of the shape of the curve to fit. It could be a predefined
shape ( 
parini 
Initial parameters. quickpsy calculates default
initial parameters using probit analysis, but it is also possible to
specify a vector of initial parameters or a list of the form

guess 
Value indicating the guess rate γ (default is 0). If

lapses 
Value indicating the lapse rate λ (default is 0).
If 
prob 
Probability to calculate the threshold (default is

thresholds 
If 
bootstrap 

B 
number of bootstrap samples (default is 100 ONLY). 
ci 
Confidence intervals level based on percentiles (default is .95). 
optimization 
Method used for optimizization. The default is 'optim' which uses
the 
A list containing the following components:
x, k, n
groups
The grouping variables.
funname
String with the name of the shape of the curve.
psyfunguesslapses
Curve including guess and lapses.
limits
Limits of the curves.
parini
Initial parameters.
optimization
Method to optimize.
pariniset
FALSE
if initial parameters are not given.
ypred
Predicted probabilities at the values of the explanatory
variable.
curves
Curves.
par
Fitted parameters and its confidence intervals.
curvesbootstrap
Bootstrap curves.
thresholds
Thresholds.
thresholdsci
Confidence intervals for the thresholds.
logliks
Loglikelihoods of the model.
loglikssaturated
Loglikelihoods of the saturated model.
deviance
Deviance of the model and the pvalue calculated by
bootstraping.
aic
AIC of the model defined as
 2 * loglik + 2 *k
where k is the number of parameters of the model.
Burnham, K. P., & Anderson, D. R. (2003). Model selection and multimodel inference: a practical informationtheoretic approach. Springer Science & Business Media.
Knoblauch, K., & Maloney, L. T. (2012). Modeling Psychophysical Data in R. New York: Springer.
Prins, N., & Kingdom, F. A. A. (2016). Psychophysics: a practical introduction. London: Academic Press.
1 2 3 4 5 6 7 8  # make sure that all the requires packages are installed
# and loaded; instructions at https://github.com/danilinares/quickpsy
library(MPDiR) # contains the Vernier data; use ?Vernier for the reference
fit < quickpsy(Vernier, Phaseshift, NumUpward, N,
grouping = .(Direction, WaveForm, TempFreq), B = 10)
plotcurves(fit)
plotpar(fit)
plotthresholds(fit, geom = 'point')

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