mvnorm.test {energy} | R Documentation |

Performs the E-statistic (energy) test of multivariate or univariate normality.

```
mvnorm.test(x, R)
mvnorm.etest(x, R)
mvnorm.e(x)
```

`x` |
data matrix of multivariate sample, or univariate data vector |

`R` |
number of bootstrap replicates |

If `x`

is a matrix, each row is a multivariate observation. The
data will be standardized to zero mean and identity covariance matrix
using the sample mean vector and sample covariance matrix. If `x`

is a vector, `mvnorm.e`

returns the univariate statistic
`normal.e(x)`

.
If the data contains missing values or the sample covariance matrix is
singular, `mvnorm.e`

returns NA.

The `\mathcal{E}`

-test of multivariate normality was proposed
and implemented by Szekely and Rizzo (2005). The test statistic for
d-variate normality is given by

```
\mathcal{E} = n (\frac{2}{n} \sum_{i=1}^n E\|y_i-Z\| -
E\|Z-Z'\| - \frac{1}{n^2} \sum_{i=1}^n \sum_{j=1}^n \|y_i-y_j\|),
```

where `y_1,\ldots,y_n`

is the standardized sample,
`Z, Z'`

are iid standard d-variate normal, and
`\| \cdot \|`

denotes Euclidean norm.

The `\mathcal{E}`

-test of multivariate (univariate) normality
is implemented by parametric bootstrap with `R`

replicates.

The value of the `\mathcal{E}`

-statistic for multivariate
normality is returned by `mvnorm.e`

.

`mvnorm.test`

returns a list with class `htest`

containing

`method` |
description of test |

`statistic` |
observed value of the test statistic |

`p.value` |
approximate p-value of the test |

`data.name` |
description of data |

`mvnorm.etest`

is replaced by `mvnorm.test`

.

If the data is univariate, the test statistic is formally
the same as the multivariate case, but a more efficient computational
formula is applied in `normal.e`

.

`normal.test`

also provides an optional method for the
test based on the asymptotic sampling distribution of the test
statistic.

Maria L. Rizzo mrizzo@bgsu.edu and Gabor J. Szekely

Szekely, G. J. and Rizzo, M. L. (2005) A New Test for
Multivariate Normality, *Journal of Multivariate Analysis*,
93/1, 58-80,
doi: 10.1016/j.jmva.2003.12.002.

Mori, T. F., Szekely, G. J. and Rizzo, M. L. "On energy tests of normality." Journal of Statistical Planning and Inference 213 (2021): 1-15.

Rizzo, M. L. (2002). A New Rotation Invariant Goodness-of-Fit Test, Ph.D. dissertation, Bowling Green State University.

Szekely, G. J. (1989) Potential and Kinetic Energy in Statistics, Lecture Notes, Budapest Institute of Technology (Technical University).

`normal.test`

for the energy test of univariate
normality and `normal.e`

for the statistic.

```
## compute normality test statistic for iris Setosa data
data(iris)
mvnorm.e(iris[1:50, 1:4])
## test if the iris Setosa data has multivariate normal distribution
mvnorm.test(iris[1:50,1:4], R = 199)
```

[Package *energy* version 1.7-10 Index]