`pca.Rd`

This method performs principal components analysis and returns the requested number of PC axes (components).

```
pca(
tasObj,
useCovariance = TRUE,
limitBy = c("number_of_components", "min_eigenvalue", "total_variance"),
nComponents = 5,
minEigenval = 0,
totalVar = 0.5,
reportEigenvalues = TRUE,
reportEigenvectors = TRUE
)
```

- tasObj
an rTASSEL

`TasselGenotypePhenotype`

object.- useCovariance
If

`TRUE`

, analysis will do an eigenvalue decomposition of the covariance matrix. If`FALSE`

, it will use a correlation matrix. NOTE: Using the covariance matrix is recommended for genotypes while the correlation matrix is often used for phenotypes. Defaults to`TRUE`

.- limitBy
This parameter determines the type of value that will be used to limit the number of principal components (axes) returned. The possible choices are

`number_of_components`

,`min_eigenvalue`

, and`total_variance`

.- nComponents
The analysis will return this many principal components up to the number of taxa.

- minEigenval
All principal components with an eigenvalue greater than or equal to this value will be returned. NOTE: works only if

`min_eigenvalue`

is set in the`limitBy`

parameter.- totalVar
The first principal components that together explain this proportion of the total variance will be returned. NOTE: works only if

`total_variance`

is set in the`limitBy`

parameter.- reportEigenvalues
Returns a list of eigenvalues sorted high to low.

- reportEigenvectors
Returns the eigenvectors calculated from a Singular Value Decomposition of the data. The resulting table can be quite large if the number of variants and taxa are big.

A `DataFrame`

object.