library.anova Submodule

Module Summary

Interfaces for the NAG Mark 29.2 anova Chapter.

anova - Analysis of Variance

This module is concerned with methods for analysing the results of designed experiments. The range of experiments covered include:

single factor designs with equal sized blocks such as randomized complete block and balanced incomplete block designs,

row and column designs such as Latin squares, and

complete factorial designs.

Further designs may be analysed by combining the analyses provided by multiple calls to functions or by using general linear model functions provided in submodule correg.

See Also

naginterfaces.library.examples.anova :

This subpackage contains examples for the anova module. See also the Examples subsection.

Functionality Index

Analysis of variance for

complete factorial design: factorial()

general block design or completely randomized design: random()

two-way hierarchical classification, subgroups of unequal size: hier2()

General linear model

generate dummy variables and orthogonal polynomials: dummyvars()

Inferences on means

simultaneous confidence intervals: confidence()

sum of squares for contrast between means: contrasts()

Rater Reliability

intraclass correlation (ICC): icc()

For full information please refer to the NAG Library document



Example for naginterfaces.library.anova.icc().

Intraclass correlation (ICC) for assessing rater reliability.

>>> main()
naginterfaces.library.anova.icc Python Example Results.
Interrater reliability for a one-factor design.
Intraclass Correlation   :  0.17
Lower Limit for 95.0% CI : -0.13
Upper Limit for 95.0% CI :  0.72
F statistic              :  1.79
Degrees of Freedom 1     :   5.0
Degrees of Freedom 2     :  18.0
p-value                  : 0.165