Calculation and presentation of decision-invariant bias adjustment thresholds and intervals for Network Meta-Analysis, as described by Phillippo et al. (2017) <doi:10.1111/rssa.12341>. These describe the smallest changes to the data that would result in a change of decision.
| Version: | 0.1.3 |
| Depends: | R (≥ 3.1.0) |
| Imports: | nnls, grid, gridExtra, gtable, Matrix, ggplot2 |
| Suggests: | knitr, rmarkdown, coda |
| Published: | 2018-03-29 |
| Author: | David Phillippo [aut, cre] |
| Maintainer: | David Phillippo <david.phillippo at bristol.ac.uk> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | nmathresh citation info |
| Materials: | README NEWS |
| In views: | MetaAnalysis |
| CRAN checks: | nmathresh results |
| Reference manual: | nmathresh.pdf |
| Vignettes: |
"Examples: bias adjustment thresholds for network meta-analysis" |
| Package source: | nmathresh_0.1.3.tar.gz |
| Windows binaries: | r-devel: nmathresh_0.1.3.zip, r-release: nmathresh_0.1.3.zip, r-oldrel: nmathresh_0.1.3.zip |
| OS X binaries: | r-release: nmathresh_0.1.3.tgz, r-oldrel: nmathresh_0.1.3.tgz |
| Old sources: | nmathresh archive |
Please use the canonical form https://CRAN.R-project.org/package=nmathresh to link to this page.


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---
layout: post
title: lemon basil gelato a little more like sorbet than
date: '2011-08-30T10:01:00+02:00'
tags:
- kitchen
- gelato
tumblr_url: http://dotmr.tumblr.com/post/9586547033/lemon-basil-gelato-a-little-more-like-sorbet-than
---
Lemon Basil Gelato
A little more like sorbet than I hoped, but the flavor is refreshing. Not sure if the consistency is due to freezer temp or cornstarch rather than eggs.


pingouin.multivariate_normality¶
-
pingouin.
multivariate_normality
(X, alpha=0.05)¶ Henze-Zirkler multivariate normality test.
- Parameters
- Xnp.array
Data matrix of shape (n_samples, n_features).
- alphafloat
Significance level.
- Returns
- hzfloat
The Henze-Zirkler test statistic.
- pvalfloat
P-value.
- normalboolean
True if X comes from a multivariate normal distribution.
See also
normality
Test the univariate normality of one or more variables.
homoscedasticity
Test equality of variance.
sphericity
Mauchly’s test for sphericity.
Notes
The Henze-Zirkler test [1] has a good overall power against alternatives to normality and works for any dimension and sample size.
Adapted to Python from a Matlab code [2] by Antonio Trujillo-Ortiz and tested against the MVN R package.
Rows with missing values are automatically removed.
References
- 1
Henze, N., & Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics-Theory and Methods, 19(10), 3595-3617.
- 2
Trujillo-Ortiz, A., R. Hernandez-Walls, K. Barba-Rojo and L. Cupul-Magana. (2007). HZmvntest: Henze-Zirkler’s Multivariate Normality Test. A MATLAB file.
Examples
>>> import pingouin as pg >>> data = pg.read_dataset('multivariate') >>> X = data[['Fever', 'Pressure', 'Aches']] >>> pg.multivariate_normality(X, alpha=.05) HZResults(hz=0.540086101851555, pval=0.7173686509622385, normal=True)


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