Cargando…
ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density
The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are often modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computation...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5939052/ https://www.ncbi.nlm.nih.gov/pubmed/29765345 http://dx.doi.org/10.3389/fpsyg.2018.00612 |
_version_ | 1783320899468918784 |
---|---|
author | Moret-Tatay, Carmen Gamermann, Daniel Navarro-Pardo, Esperanza Fernández de Córdoba Castellá, Pedro |
author_facet | Moret-Tatay, Carmen Gamermann, Daniel Navarro-Pardo, Esperanza Fernández de Córdoba Castellá, Pedro |
author_sort | Moret-Tatay, Carmen |
collection | PubMed |
description | The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are often modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex-Gaussian probability density. In order to validate the package, we present an extensive analysis of fits obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fits (which is usually an overlooked point in most literature in the area). The analysis done allows one to identify outliers in the empirical datasets and criteriously determine if there is a need for data trimming and at which points it should be done. |
format | Online Article Text |
id | pubmed-5939052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59390522018-05-14 ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density Moret-Tatay, Carmen Gamermann, Daniel Navarro-Pardo, Esperanza Fernández de Córdoba Castellá, Pedro Front Psychol Psychology The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are often modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex-Gaussian probability density. In order to validate the package, we present an extensive analysis of fits obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fits (which is usually an overlooked point in most literature in the area). The analysis done allows one to identify outliers in the empirical datasets and criteriously determine if there is a need for data trimming and at which points it should be done. Frontiers Media S.A. 2018-05-01 /pmc/articles/PMC5939052/ /pubmed/29765345 http://dx.doi.org/10.3389/fpsyg.2018.00612 Text en Copyright © 2018 Moret-Tatay, Gamermann, Navarro-Pardo and Fernández de Córdoba Castellá. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Moret-Tatay, Carmen Gamermann, Daniel Navarro-Pardo, Esperanza Fernández de Córdoba Castellá, Pedro ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density |
title | ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density |
title_full | ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density |
title_fullStr | ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density |
title_full_unstemmed | ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density |
title_short | ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density |
title_sort | exgutils: a python package for statistical analysis with the ex-gaussian probability density |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5939052/ https://www.ncbi.nlm.nih.gov/pubmed/29765345 http://dx.doi.org/10.3389/fpsyg.2018.00612 |
work_keys_str_mv | AT morettataycarmen exgutilsapythonpackageforstatisticalanalysiswiththeexgaussianprobabilitydensity AT gamermanndaniel exgutilsapythonpackageforstatisticalanalysiswiththeexgaussianprobabilitydensity AT navarropardoesperanza exgutilsapythonpackageforstatisticalanalysiswiththeexgaussianprobabilitydensity AT fernandezdecordobacastellapedro exgutilsapythonpackageforstatisticalanalysiswiththeexgaussianprobabilitydensity |