# Scatter Matrix and Covariance

This package implements functions for computing scatter matrix, as well as weighted covariance matrix.

`StatsBase.scattermat`

— Function`scattermat(X, [wv::AbstractWeights]; mean=nothing, dims=1)`

Compute the scatter matrix, which is an unnormalized covariance matrix. A weighting vector `wv`

can be specified to weight the estimate.

**Arguments**

`mean=nothing`

: a known mean value.`nothing`

indicates that the mean is unknown, and the function will compute the mean. Specifying`mean=0`

indicates that the data are centered and hence there's no need to subtract the mean.`dims=1`

: the dimension along which the variables are organized. When`dims = 1`

, the variables are considered columns with observations in rows; when`dims = 2`

, variables are in rows with observations in columns.

`Statistics.cov`

— Function`cov(X, w::AbstractWeights, vardim=1; mean=nothing, corrected=false)`

Compute the weighted covariance matrix. Similar to `var`

and `std`

the biased covariance matrix (`corrected=false`

) is computed by multiplying `scattermat(X, w)`

by $\frac{1}{\sum{w}}$ to normalize. However, the unbiased covariance matrix (`corrected=true`

) is dependent on the type of weights used:

`AnalyticWeights`

: $\frac{1}{\sum w - \sum {w^2} / \sum w}$`FrequencyWeights`

: $\frac{1}{\sum{w} - 1}$`ProbabilityWeights`

: $\frac{n}{(n - 1) \sum w}$ where $n$ equals`count(!iszero, w)`

`Weights`

:`ArgumentError`

(bias correction not supported)

`Statistics.cov`

— Method`cov(ce::CovarianceEstimator, x::AbstractVector; mean=nothing)`

Compute a variance estimate from the observation vector `x`

using the estimator `ce`

.

`Statistics.cov`

— Method`cov(ce::CovarianceEstimator, x::AbstractVector, y::AbstractVector)`

Compute the covariance of the vectors `x`

and `y`

using estimator `ce`

.

`Statistics.cov`

— Method`cov(ce::CovarianceEstimator, X::AbstractMatrix, [w::AbstractWeights]; mean=nothing, dims::Int=1)`

Compute the covariance matrix of the matrix `X`

along dimension `dims`

using estimator `ce`

. A weighting vector `w`

can be specified. The keyword argument `mean`

can be:

`nothing`

(default) in which case the mean is estimated and subtracted from the data`X`

,- a precalculated mean in which case it is subtracted from the data
`X`

. Assuming`size(X)`

is`(N,M)`

,`mean`

can either be:- when
`dims=1`

, an`AbstractMatrix`

of size`(1,M)`

, - when
`dims=2`

, an`AbstractVector`

of length`N`

or an`AbstractMatrix`

of size`(N,1)`

.

- when

`Statistics.cor`

— Function`cor(X, w::AbstractWeights, dims=1)`

Compute the Pearson correlation matrix of `X`

along the dimension `dims`

with a weighting `w`

.

`StatsBase.mean_and_cov`

— Function`mean_and_cov(x, [wv::AbstractWeights,] vardim=1; corrected=false) -> (mean, cov)`

Return the mean and covariance matrix as a tuple. A weighting vector `wv`

can be specified. `vardim`

that designates whether the variables are columns in the matrix (`1`

) or rows (`2`

). Finally, bias correction is applied to the covariance calculation if `corrected=true`

. See `cov`

documentation for more details.

`StatsBase.cov2cor`

— Function`cov2cor(C, s)`

Compute the correlation matrix from the covariance matrix `C`

and a vector of standard deviations `s`

. Use `StatsBase.cov2cor!`

for an in-place version.

`StatsBase.cor2cov`

— Function`cor2cov(C, s)`

Compute the covariance matrix from the correlation matrix `C`

and a vector of standard deviations `s`

. Use `StatsBase.cor2cov!`

for an in-place version.

`StatsBase.CovarianceEstimator`

— Type`CovarianceEstimator`

Abstract type for covariance estimators.

`StatsBase.SimpleCovariance`

— Type`SimpleCovariance(;corrected::Bool=false)`

Simple covariance estimator. Estimation calls `cov(x; corrected=corrected)`

, `cov(x, y; corrected=corrected)`

or `cov(X, w, dims; corrected=corrected)`

where `x`

, `y`

are vectors, `X`

is a matrix and `w`

is a weighting vector.