pingouin.mediation_analysis

pingouin.
mediation_analysis
(data=None, x=None, m=None, y=None, covar=None, alpha=0.05, n_boot=500, seed=None, return_dist=False)[source] Mediation analysis using a biascorrect nonparametric bootstrap method.
 Parameters
 datapd.DataFrame
Dataframe.
 xstr
Column name in data containing the predictor variable. The predictor variable must be continuous.
 mstr or list of str
Column name(s) in data containing the mediator variable(s). The mediator(s) can be continuous or binary (e.g. 0 or 1). This function supports multiple parallel mediators.
 ystr
Column name in data containing the outcome variable. The outcome variable must be continuous.
 covarNone, str, or list
Covariate(s). If not None, the specified covariate(s) will be included in all regressions.
 alphafloat
Significance threshold. Used to determine the confidence interval, CI = [ alpha / 2 ; 1  alpha / 2]
 n_bootint
Number of bootstrap iterations for confidence intervals and pvalues estimation. The greater, the slower.
 seedint or None
Random state seed.
 return_distbool
If True, the function also returns the indirect bootstrapped beta samples (size = n_boot). Can be plotted for instance using
seaborn.distplot()
orseaborn.kdeplot()
functions.
 Returns
 statspd.DataFrame
Mediation summary:
'path' : regression model 'coef' : regression estimates 'se' : standard error 'CI[2.5%]' : lower confidence interval 'CI[97.5%]' : upper confidence interval 'pval' : twosided pvalues 'sig' : statistical significance
Notes
Mediation analysis is a “statistical procedure to test whether the effect of an independent variable X on a dependent variable Y (i.e., X → Y) is at least partly explained by a chain of effects of the independent variable on an intervening mediator variable M and of the intervening variable on the dependent variable (i.e., X → M → Y)” (from Fiedler et al. 2011).
The indirect effect (also referred to as average causal mediation effect or ACME) of X on Y through mediator M quantifies the estimated difference in Y resulting from a oneunit change in X through a sequence of causal steps in which X affects M, which in turn affects Y. It is considered significant if the specified confidence interval does not include 0. The path ‘X –> Y’ is the sum of both the indirect and direct effect. It is sometimes referred to as total effect. For more details, please refer to Fiedler et al 2011 or Hayes and Rockwood 2017.
A linear regression is used if the mediator variable is continuous and a logistic regression if the mediator variable is dichotomous (binary). Note that this function also supports parallel multiple mediators: “in such models, mediators may be and often are correlated, but nothing in the model allows one mediator to causally influence another.” (Hayes and Rockwood 2017)
This function wll only work well if the outcome variable is continuous. It does not support binary or ordinal outcome variable. For more advanced mediation models, please refer to the lavaan or mediation R packages, or the PROCESS macro for SPSS.
The twosided pvalue of the indirect effect is computed using the bootstrap distribution, as in the mediation R package. However, the pvalue should be interpreted with caution since it is a) not constructed conditioned on a true null hypothesis (see Hayes and Rockwood 2017) and b) varies depending on the number of bootstrap samples and the random seed.
Note that rows with NaN are automatically removed.
Results have been tested against the R mediation package and this tutorial https://data.library.virginia.edu/introductiontomediationanalysis/
References
 1
Baron, R. M. & Kenny, D. A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986).
 2
Fiedler, K., Schott, M. & Meiser, T. What mediation analysis can (not) do. J. Exp. Soc. Psychol. 47, 1231–1236 (2011).
 3
Hayes, A. F. & Rockwood, N. J. Regressionbased statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. Behav. Res. Ther. 98, 39–57 (2017).
 4
https://cran.rproject.org/web/packages/mediation/mediation.pdf
 5
 6
Examples
Simple mediation analysis
>>> from pingouin import mediation_analysis, read_dataset >>> df = read_dataset('mediation') >>> mediation_analysis(data=df, x='X', m='M', y='Y', alpha=0.05, seed=42) path coef se pval CI[2.5%] CI[97.5%] sig 0 M ~ X 0.5610 0.0945 4.391362e08 0.3735 0.7485 Yes 1 Y ~ M 0.6542 0.0858 1.612674e11 0.4838 0.8245 Yes 2 Total 0.3961 0.1112 5.671128e04 0.1755 0.6167 Yes 3 Direct 0.0396 0.1096 7.187429e01 0.1780 0.2572 No 4 Indirect 0.3565 0.0833 0.000000e+00 0.2198 0.5377 Yes
Return the indirect bootstrapped beta coefficients
>>> stats, dist = mediation_analysis(data=df, x='X', m='M', y='Y', ... return_dist=True) >>> print(dist.shape) (500,)
Mediation analysis with a binary mediator variable
>>> mediation_analysis(data=df, x='X', m='Mbin', y='Y', seed=42) path coef se pval CI[2.5%] CI[97.5%] sig 0 Mbin ~ X 0.0205 0.1159 0.859392 0.2476 0.2066 No 1 Y ~ Mbin 0.1354 0.4118 0.743076 0.9525 0.6818 No 2 Total 0.3961 0.1112 0.000567 0.1755 0.6167 Yes 3 Direct 0.3956 0.1117 0.000614 0.1739 0.6173 Yes 4 Indirect 0.0023 0.0495 0.960000 0.0715 0.1441 No
Mediation analysis with covariates
>>> mediation_analysis(data=df, x='X', m='M', y='Y', ... covar=['Mbin', 'Ybin'], seed=42) path coef se pval CI[2.5%] CI[97.5%] sig 0 M ~ X 0.5594 0.0968 9.394635e08 0.3672 0.7516 Yes 1 Y ~ M 0.6660 0.0861 1.017261e11 0.4951 0.8368 Yes 2 Total 0.4204 0.1129 3.324252e04 0.1962 0.6446 Yes 3 Direct 0.0645 0.1104 5.608583e01 0.1548 0.2837 No 4 Indirect 0.3559 0.0865 0.000000e+00 0.2093 0.5530 Yes
Mediation analysis with multiple parallel mediators
>>> mediation_analysis(data=df, x='X', m=['M', 'Mbin'], y='Y', seed=42) path coef se pval CI[2.5%] CI[97.5%] sig 0 M ~ X 0.5610 0.0945 4.391362e08 0.3735 0.7485 Yes 1 Mbin ~ X 0.0051 0.0290 8.592408e01 0.0626 0.0523 No 2 Y ~ M 0.6537 0.0863 2.118163e11 0.4824 0.8250 Yes 3 Y ~ Mbin 0.0640 0.3282 8.456998e01 0.7154 0.5873 No 4 Total 0.3961 0.1112 5.671128e04 0.1755 0.6167 Yes 5 Direct 0.0395 0.1102 7.206301e01 0.1792 0.2583 No 6 Indirect M 0.3563 0.0845 0.000000e+00 0.2148 0.5385 Yes 7 Indirect Mbin 0.0003 0.0097 9.520000e01 0.0172 0.0252 No