Pingouin: statistics in Python

Author:

Vallat RaphaelORCID

Publisher

The Open Journal

Reference5 articles.

1. Bayesian t tests for accepting and rejecting the null hypothesis, Rouder, Jeffrey N and Speckman, Paul L and Sun, Dongchu and Morey, Richard D and Iverson, Geoffrey, Progress in science often comes from discovering invariances in relationships among variables; these invariances often correspond to null hypotheses. As is commonly known, it is not possible to state evidence for the null hypothesis in conventional significance testing. Here we highlight a Bayes factor alternative to the conventional t test that will allow researchers to express preference for either the null hypothesis or the alternative. The Bayes factor has a natural and straightforward interpretation, is based on reasonable assumptions, and has better properties than other methods of inference that have been advocated in the psychological literature. To facilitate use of the Bayes factor, we provide an easy-to-use, Web-based program that performs the necessary calculations., Psychon. Bull. Rev., Springer, 16, 2, 225–237, apr, 2009, en, 4

2. Repeated Measures Correlation, Bakdash, Jonathan Z and Marusich, Laura R, Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals. Simple regression/correlation is often applied to non-independent observations or aggregated data; this may produce biased, specious results due to violation of independence and/or differing patterns between-participants versus within-participants. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Rmcorr estimates the common regression slope, the association shared among individuals. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. All results are fully reproducible., Front. Psychol., 8, 456, apr, 2017, correlation; individual differences; intra-individual; multilevel modeling; repeated measures; statistical power, en, 4

3. Robust correlation analyses: false positive and power validation using a new open source matlab toolbox, Pernet, Cyril R and Wilcox, Rand and Rousselet, Guillaume A, Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab((R)) based toolbox (http://sourceforge.net/projects/robustcorrtool/) that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand., Front. Psychol., frontiersin.org, 3, 606, 2012, MATLAB; correlation; outliers; power; robust statistics, en

4. CircStat: A MATLAB Toolbox for Circular Statistics, Berens, Philipp, Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fifty years, there is only little software available that makes such methods easy to use for practioners. Most importantly, one of the most commonly used programming languages in biosciences, MATLAB, is currently not supporting directional statistics. To remedy this situation, we have implemented the CircStat toolbox for MATLAB which provides methods for the descriptive and inferential statistical analysis of directional data. We cover the statistical background of the available methods and describe how to apply them to data. Finally, we analyze a dataset from neurophysiology to demonstrate the capabilities of the CircStat toolbox., J. Stat. Softw., 31, 10, 1–21, 2009

5. McKinney, Wes, Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51–56, 2010, van der Walt, Stefan and Millman, Jarrod

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