Functions

Table of Contents

• Functions

  • ANOVA and T-test

  • Bayesian

  • Circular

  • Contingency

  • Correlation and regression

  • Distribution

  • Effect sizes

  • Multiple comparisons and post-hoc tests

  • Multivariate tests

  • Non-parametric

  • Others

  • Plotting

  • Power analysis

  • Reliability and consistency

ANOVA and T-test

anova([data, dv, between, ss_type, …])	One-way and N-way ANOVA.
ancova([data, dv, between, covar, effsize])	ANCOVA with one or more covariate(s).
rm_anova([data, dv, within, subject, …])	One-way and two-way repeated measures ANOVA.
epsilon(data[, dv, within, subject, correction])	Epsilon adjustement factor for repeated measures.
mixed_anova([data, dv, within, subject, …])	Mixed-design (split-plot) ANOVA.
welch_anova([data, dv, between])	One-way Welch ANOVA.
tost(x, y[, bound, paired, correction])	Two One-Sided Test (TOST) for equivalence.
ttest(x, y[, paired, tail, correction, r])	T-test.

Bayesian

bayesfactor_binom(k, n[, p])	Bayes factor of a binomial test with \(k\) successes, \(n\) trials and base probability \(p\).
bayesfactor_ttest(t, nx[, ny, paired, tail, r])	Bayes Factor of a T-test.
bayesfactor_pearson(r, n[, tail, method, kappa])	Bayes Factor of a Pearson correlation.

Circular

convert_angles(angles[, low, high, positive])	Element-wise conversion of arbitrary-unit circular quantities to radians.
circ_axial(angles, n)	Transforms n-axial data to a common scale.
circ_corrcc(x, y[, tail, correction_uniform])	Correlation coefficient between two circular variables.
circ_corrcl(x, y[, tail])	Correlation coefficient between one circular and one linear variable random variables.
circ_mean(angles[, w, axis])	Mean direction for (binned) circular data.
circ_r(angles[, w, d, axis])	Mean resultant vector length for circular data.
circ_rayleigh(angles[, w, d])	Rayleigh test for non-uniformity of circular data.
circ_vtest(angles[, dir, w, d])	V test for non-uniformity of circular data with a specified mean direction.

Contingency

chi2_independence(data, x, y[, correction])	Chi-squared independence tests between two categorical variables.
chi2_mcnemar(data, x, y[, correction])	Performs the exact and approximated versions of McNemar’s test.
dichotomous_crosstab(data, x, y)	Generates a 2x2 contingency table from a pandas.DataFrame that contains only dichotomous entries, which are converted to 0 or 1.

Correlation and regression

corr(x, y[, tail, method])	(Robust) correlation between two variables.
pairwise_corr(data[, columns, covar, tail, …])	Pairwise (partial) correlations between columns of a pandas dataframe.
partial_corr([data, x, y, covar, x_covar, …])	Partial and semi-partial correlation.
pcorr(self)	Partial correlation matrix (pandas.DataFrame method).
rcorr(self[, method, upper, decimals, …])	Correlation matrix of a dataframe with p-values and/or sample size on the upper triangle (pandas.DataFrame method).
distance_corr(x, y[, tail, n_boot, seed])	Distance correlation between two arrays.
rm_corr([data, x, y, subject, tail])	Repeated measures correlation.
linear_regression(X, y[, add_intercept, …])	(Multiple) Linear regression.
logistic_regression(X, y[, coef_only, …])	(Multiple) Binary logistic regression.
mediation_analysis([data, x, m, y, covar, …])	Mediation analysis using a bias-correct non-parametric bootstrap method.

Distribution

anderson(*args[, dist])	Anderson-Darling test of distribution.
gzscore(x)	Geometric standard (Z) score.
homoscedasticity(data[, dv, group, method, …])	Test equality of variance.
normality(data[, dv, group, method, alpha])	Univariate normality test.
sphericity(data[, dv, within, subject, …])	Mauchly and JNS test for sphericity.

Effect sizes

compute_effsize(x, y[, paired, eftype])	Calculate effect size between two set of observations.
compute_effsize_from_t(tval[, nx, ny, N, eftype])	Compute effect size from a T-value.
convert_effsize(ef, input_type, output_type)	Conversion between effect sizes.
compute_esci([stat, nx, ny, paired, eftype, …])	Parametric confidence intervals around a Cohen d or a correlation coefficient.
compute_bootci(x[, y, func, method, paired, …])	Bootstrapped confidence intervals of univariate and bivariate functions.

Multiple comparisons and post-hoc tests

pairwise_corr(data[, columns, covar, tail, …])	Pairwise (partial) correlations between columns of a pandas dataframe.
pairwise_ttests([data, dv, between, within, …])	Pairwise T-tests.
pairwise_tukey([data, dv, between, alpha, …])	Pairwise Tukey-HSD post-hoc test.
pairwise_gameshowell([data, dv, between, …])	Pairwise Games-Howell post-hoc test.
multicomp(pvals[, alpha, method])	P-values correction for multiple comparisons.

Multivariate tests

multivariate_normality(X[, alpha])	Henze-Zirkler multivariate normality test.
multivariate_ttest(X[, Y, paired])	Hotelling T-squared test (= multivariate T-test)

Non-parametric

cochran([data, dv, within, subject])	Cochran Q test.
friedman([data, dv, within, subject])	Friedman test for repeated measurements.
kruskal([data, dv, between, detailed])	Kruskal-Wallis H-test for independent samples.
mad(a[, normalize, axis])	Median Absolute Deviation (MAD) along given axis of an array.
madmedianrule(a)	Robust outlier detection based on the MAD-median rule.
mwu(x, y[, tail])	Mann-Whitney U Test (= Wilcoxon rank-sum test).
wilcoxon(x, y[, tail])	Wilcoxon signed-rank test.
harrelldavis(x[, quantile, axis])	Harrell-Davis robust estimate of the \(q^{th}\) quantile(s) of the data.

Others

print_table(df[, floatfmt, tablefmt])	Pretty display of table.
remove_na(x[, y, paired, axis])	Remove missing values along a given axis in one or more (paired) numpy arrays.
remove_rm_na([data, dv, within, subject, …])	Remove missing values in long-format repeated-measures dataframe.
read_dataset(dname)	Read example datasets.
list_dataset()	List available example datasets.

Plotting

plot_blandaltman(x, y[, agreement, …])	Generate a Bland-Altman plot to compare two sets of measurements.
plot_circmean(angles[, figsize, dpi, ax, …])	Plot the circular mean and vector length of a set of angles on the unit circle.
plot_paired([data, dv, within, subject, …])	Paired plot.
plot_shift(x, y[, paired, n_boot, …])	Shift plot.
plot_rm_corr([data, x, y, subject, legend, …])	Plot a repeated measures correlation.
plot_skipped_corr(x, y[, xlabel, ylabel, …])	Plot the bootstrapped 95% confidence intervals and distribution of a robust Skipped correlation.
qqplot(x[, dist, sparams, confidence, …])	Quantile-Quantile plot.

Power analysis

power_anova([eta, k, n, power, alpha])	Evaluate power, sample size, effect size or significance level of a one-way balanced ANOVA.
power_rm_anova([eta, m, n, power, alpha, …])	Evaluate power, sample size, effect size or significance level of a balanced one-way repeated measures ANOVA.
power_chi2(dof[, w, n, power, alpha])	Evaluate power, sample size, effect size or significance level of chi-squared tests.
power_corr([r, n, power, alpha, tail])	Evaluate power, sample size, correlation coefficient or significance level of a correlation test.
power_ttest([d, n, power, alpha, contrast, tail])	Evaluate power, sample size, effect size or significance level of a one-sample T-test, a paired T-test or an independent two-samples T-test with equal sample sizes.
power_ttest2n(nx, ny[, d, power, alpha, tail])	Evaluate power, effect size or significance level of an independent two-samples T-test with unequal sample sizes.

Reliability and consistency

cronbach_alpha([data, items, scores, …])	Cronbach’s alpha reliability measure.
intraclass_corr([data, targets, raters, …])	Intraclass correlation.