Table of Contents
anova([dv, between, data, detailed, …]) |
One-way and two-way ANOVA. |
welch_anova([dv, between, data, export_filename]) |
One-way Welch ANOVA. |
rm_anova([dv, within, subject, data, …]) |
One-way repeated measures ANOVA. |
rm_anova2([dv, within, subject, data, …]) |
Two-way repeated measures ANOVA. |
epsilon(data[, correction]) |
Epsilon adjustement factor for repeated measurements. |
mixed_anova([dv, within, subject, between, …]) |
Mixed-design (split-plot) ANOVA. |
ancova([dv, covar, between, data, …]) |
ANCOVA with one or more covariate(s). |
ttest(x, y[, paired, tail, correction, r]) |
T-test. |
bayesfactor_ttest(t, nx[, ny, paired, tail, r]) |
Bayes Factor of a T-test. |
bayesfactor_pearson(r, n) |
Bayes Factor of a Pearson correlation. |
circ_axial(alpha, n) |
Transforms n-axial data to a common scale. |
circ_corrcc(x, y[, tail]) |
Correlation coefficient between two circular variables. |
circ_corrcl(x, y[, tail]) |
Correlation coefficient between one circular and one linear variable random variables. |
circ_mean(alpha[, w, axis]) |
Mean direction for circular data. |
circ_r(alpha[, w, d, axis]) |
Mean resultant vector length for circular data. |
circ_rayleigh(alpha[, w, d]) |
Rayleigh test for non-uniformity of circular data. |
circ_vtest(alpha[, dir, w, d]) |
V test for non-uniformity of circular data with a specified mean direction. |
corr(x, y[, tail, method]) |
(Robust) correlation between two variables. |
pairwise_corr(data[, columns, tail, method, …]) |
Pairwise correlations between columns of a pandas dataframe. |
partial_corr([data, x, y, covar, tail, method]) |
Partial correlation. |
rm_corr([data, x, y, subject, tail]) |
Repeated measures correlation. |
intraclass_corr([data, groups, raters, …]) |
Intra-class correlation coefficient. |
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, alpha, …]) |
Mediation analysis using a bias-correct non-parametric bootstrap method. |
anderson(*args[, dist]) |
Anderson-Darling test of distribution. |
gzscore(x) |
Geometric standard (Z) score. |
homoscedasticity(*args[, alpha]) |
Test equality of variance. |
normality(*args[, alpha]) |
Shapiro-Wilk univariate normality test. |
multivariate_normality(X[, alpha]) |
Henze-Zirkler multivariate normality test. |
sphericity(data[, method, alpha]) |
Mauchly and JNS test for sphericity. |
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, 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. |
pairwise_ttests([dv, between, within, …]) |
Pairwise T-tests. |
pairwise_tukey([dv, between, data, alpha, …]) |
Pairwise Tukey-HSD post-hoc test. |
pairwise_gameshowell([dv, between, data, …]) |
Pairwise Games-Howell post-hoc test. |
multicomp(pvals[, alpha, method]) |
P-values correction for multiple tests. |
bonf(pvals[, alpha]) |
P-values correction with Bonferroni method. |
holm(pvals[, alpha]) |
P-values correction with Holm method. |
fdr(pvals[, alpha, method]) |
P-values correction with False Discovery Rate (FDR). |
cochran([dv, within, subject, data, …]) |
Cochran Q test. |
friedman([dv, within, subject, data, …]) |
Friedman test for repeated measurements. |
kruskal([dv, between, data, detailed, …]) |
Kruskal-Wallis H-test for independent samples. |
mad(a[, normalize, axis]) |
Median Absolute Deviation along given axis of an array. |
madmedianrule(a) |
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. |
print_table(df[, floatfmt, tablefmt]) |
Pretty display of table. |
pingouin.datasets.read_dataset(dname) |
Read example datasets. |
pingouin.datasets.list_dataset() |
List available example datasets. |
plot_blandaltman(x, y[, agreement, …]) |
Generate a Bland-Altman plot to compare two sets of measurements. |
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_anova([eta, k, n, power, alpha]) |
Evaluate power, sample size, effect size or significance level of a one-way balanced ANOVA. |
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. |
