README.md

In danilinares/quickpsy: Fits Psychometric Functions for Multiple Groups

quickpsy

quickpsy is an R package developed by Daniel Linares and Joan López-Moliner to quickly fit and plot psychometric functions for multiple conditions.

You can find more information at the website.

Installation

You can install the released version of quickpsy from CRAN with:

install.packages("quickpsy")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("danilinares/quickpsy")

At present, the development version of quickpsy is slower than older versions. If you need to perform bootstrap for many conditions you might want to use an older version.

Example

library(quickpsy)

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"))

plot(fit, color = cond)

Features

• Fits and plots multiple conditions with minimal coding.

• The user does not need to introduce initial parameters for default shapes of the psychometric functions.

• Calculates parametric and non-parametric bootstrap confidence intervals.

• Compares parameters and thresholds for different conditions using bootstrap.

• Guess and lapses can be fixed or free as parameters.

• Fits cumulative normal, logistic, weibull functions or any function defined by the user.

• Facilitates the reading of several data files.

Development version (at present it is slower than the CRAN version)

• Different conditions could share parameters.

• Easy model comparisons using likelihood ratios or Akaike Information Criterion.

Citation

Linares, D., & López-Moliner, J. (2016). quickpsy: An R package to fit psychometric functions for multiple groups. The R Journal, 2016, vol. 8, num. 1, p. 122-131.

Other R packages to fit psychometric functions

• psyphy: among other things, it provides links functions to fit psychometric functions using an approach based on generalized linear models.

• modelfree: fits psychometric functions using a non-parametric approach.

danilinares/quickpsy documentation built on Feb. 13, 2023, 8:44 p.m.