statsmodels.stats.multivariate.confint_mvmean¶
- statsmodels.stats.multivariate.confint_mvmean(data, lin_transf=None, alpha=0.5, simult=False)[source]¶
Confidence interval for linear transformation of a multivariate mean
Either pointwise or simultaneous confidence intervals are returned.
- Parameters:
- datanumpy:array_like
data with observations in rows and variables in columns
- lin_transfnumpy:array_like or
None
The linear transformation or contrast matrix for transforming the vector of means. If this is None, then the identity matrix is used which specifies the means themselves.
- alpha
float
in
(0, 1) confidence level for the confidence interval, commonly used is alpha=0.05.
- simultbool
If
simult
is False (default), then the pointwise confidence interval is returned. Otherwise, a simultaneous confidence interval is returned. Warning: additional simultaneous confidence intervals might be added and the default for those might change.
- Returns:
- low
ndarray
lower confidence bound on the linear transformed
- upp
ndarray
upper confidence bound on the linear transformed
- values
ndarray
mean or their linear transformation, center of the confidence region
- low
Notes
Pointwise confidence interval is based on Johnson and Wichern equation (5-21) page 224.
Simultaneous confidence interval is based on Johnson and Wichern Result 5.3 page 225. This looks like Sheffe simultaneous confidence intervals.
Bonferroni corrected simultaneous confidence interval might be added in future
References
Johnson, Richard A., and Dean W. Wichern. 2007. Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River, N.J: Pearson Prentice Hall.