Bases: object
Methods
pvalue([baseline]) | Return a parametric approximation of the p-value associated |
stat([baseline]) | Return the decision statistic associated with the test of the |
summary() | Return a dictionary containing the estimated contrast effect, the associated ReML-based estimation variance, and the estimated degrees of freedom (variance of the variance). |
zscore([baseline]) | Return a parametric approximation of the z-score associated |
tiny is a numerical constant for computations.
Return a parametric approximation of the p-value associated with the null hypothesis: (H0) ‘contrast equals baseline’
Return the decision statistic associated with the test of the null hypothesis: (H0) ‘contrast equals baseline’
Return a dictionary containing the estimated contrast effect, the associated ReML-based estimation variance, and the estimated degrees of freedom (variance of the variance).
Return a parametric approximation of the z-score associated with the null hypothesis: (H0) ‘contrast equals baseline’
Bases: object
Methods
contrast(c[, type, tiny, dofmax]) | Specify and estimate a constrast |
fit(Y, X[, formula, axis, model, method, niter]) | |
save(file) | Save fit into a .npz file |
Specify and estimate a constrast
c must be a numpy.ndarray (or anything that numpy.asarray can cast to a ndarray). For a F contrast, c must be q x p where q is the number of contrast vectors and p is the total number of regressors.
Save fit into a .npz file