pingouin.ancova¶

pingouin.
ancova
(data=None, dv=None, between=None, covar=None, effsize='np2')¶ ANCOVA with one or more covariate(s).
 Parameters
 data
pandas.DataFrame
DataFrame. Note that this function can also directly be used as a Pandas method, in which case this argument is no longer needed.
 dvstring
Name of column in data with the dependent variable.
 betweenstring
Name of column in data with the between factor.
 covarstring or list
Name(s) of column(s) in data with the covariate.
 effsizestr
Effect size. Must be ‘np2’ (partial etasquared) or ‘n2’ (etasquared).
 data
 Returns
 aov
pandas.DataFrame
ANCOVA summary:
'Source'
: Names of the factor considered'SS'
: Sums of squares'DF'
: Degrees of freedom'F'
: Fvalues'punc'
: Uncorrected pvalues'np2'
: Partial etasquared
 aov
See also
anova
Oneway and Nway ANOVA
Notes
Analysis of covariance (ANCOVA) is a general linear model which blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable (dv) are equal across levels of a categorical independent variable (between) often called a treatment, while statistically controlling for the effects of other continuous variables that are not of primary interest, known as covariates or nuisance variables (covar).
Pingouin uses
statsmodels.regression.linear_model.OLS
to compute the ANCOVA.Important
Rows with missing values are automatically removed (listwise deletion).
Examples
1. Evaluate the reading scores of students with different teaching method and family income as a covariate.
>>> from pingouin import ancova, read_dataset >>> df = read_dataset('ancova') >>> ancova(data=df, dv='Scores', covar='Income', between='Method') Source SS DF F punc np2 0 Method 571.029883 3 3.336482 0.031940 0.244077 1 Income 1678.352687 1 29.419438 0.000006 0.486920 2 Residual 1768.522313 31 NaN NaN NaN
2. Evaluate the reading scores of students with different teaching method and family income + BMI as a covariate.
>>> ancova(data=df, dv='Scores', covar=['Income', 'BMI'], between='Method', ... effsize="n2") Source SS DF F punc n2 0 Method 552.284043 3 3.232550 0.036113 0.141802 1 Income 1573.952434 1 27.637304 0.000011 0.404121 2 BMI 60.013656 1 1.053790 0.312842 0.015409 3 Residual 1708.508657 30 NaN NaN NaN