# covw: Weighted means, covariance and scattering matrices... In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

## Description

Compute efficiently (via Fortran code) the means, covariance and scattering matrices conditioning on a weighted or indicator matrix

## Usage

 `1` ```covw(X, Z, normalize = TRUE) ```

## Arguments

 `X` A (n x p) data matrix, with n observations on p variables. `Z` A (n x G) matrix of weights, with G number of groups. `normalize` A logical indicating if rows of `Z` should be normalized to sum to one.

## Value

A list with the following components:

 `mean` A (p x G) matrix of weighted means. `S` A (p x p x G) array of weighted covariance matrices. `W` A (p x p x G) array of weighted scattering matrices.

## Author(s)

M. Fop and L. Scrucca

## Examples

 ```1 2 3 4 5 6 7``` ```# Z as an indicator matrix X <- iris[,1:4] Z <- unmap(iris\$Species) str(covw(X, Z)) # Z as a matrix of weights mod <- Mclust(X, G = 3, modelNames = "VVV") str(covw(X, mod\$z)) ```

### Example output

```Package 'mclust' version 5.4.7
Type 'citation("mclust")' for citing this R package in publications.
List of 3
\$ mean: num [1:4, 1:3] 5.006 3.428 1.462 0.246 5.936 ...
\$ S   : num [1:4, 1:4, 1:3] 0.1218 0.0972 0.016 0.0101 0.0972 ...
\$ W   : num [1:4, 1:4, 1:3] 6.088 4.862 0.801 0.506 4.862 ...
List of 3
\$ mean: num [1:4, 1:3] 5.006 3.428 1.462 0.246 5.915 ...
\$ S   : num [1:4, 1:4, 1:3] 0.1218 0.0972 0.016 0.0101 0.0972 ...
\$ W   : num [1:4, 1:4, 1:3] 6.088 4.862 0.801 0.506 4.862 ...
```

mclust documentation built on Nov. 5, 2021, 5:07 p.m.