# stepwisecpc: Stepwise common principal components (CPC) In tpepler/cpc: Common principal component (CPC) analysis and applications

## Description

Estimates stepwise common principal components to simultaneously diagonalise several covariance matrices.

## Usage

 `1` ```stepwisecpc(covmats, nvec) ```

## Arguments

 `covmats ` Array containing the covariance matrices for the groups, created with a command such as covmats <- array(NA, dim = c(p, p, k)), where p refers to the number of rows/columns of each covariance matrix, and k is the number of groups (or covariance matrices). `nvec ` Vector containing the sample sizes of the k groups.

## Value

Returns a list with the values:

 `B ` Orthogonal matrix containing the estimated common eigenvectors in the columns. `eigenvals ` Matrix of which the columns contain the estimated eigenvalues of the k covariance matrices under the CPC model.

## Author(s)

Sarel Steel, Theo Pepler

## References

Trendafilov, N. T. (2010). Stepwise estimation of common principal components. Computational Statistics and Data Analysis, 54(12): 3446-3457.

`FG`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# Estimate the modal matrix for the versicolor and virginica groups # under the common eigenvector assumption. data(iris) versicolor <- iris[51:100, 1:4] virginica <- iris[101:150, 1:4] # Create array containing the covariance matrices S <- array(NA, dim = c(4, 4, 2)) S[, , 1] <- cov(versicolor) S[, , 2] <- cov(virginica) stepwisecpc(covmats = S, nvec = c(nrow(versicolor), nrow(virginica))) ```