How to get the principal components and variances from MultivariateStats?

Hi all,

I have run PCA and got the model, but I have not find any function to get the principal components and variances.

any hints would be appreciated.
thanks in advance!

Have you tried

transform(M, x)
Transform observations x into principal components.


The variances of principal components.

I haven’t actually used the package myself but they sound like they do what you’re after?

1 Like

thanks, should be that function. did not noticed that …

actually, transform is not what I want. transform is reducing the data to lower dimension data, rather than getting the principal directional vector (elgenvector of covariance matrix).

I have made my own function to do it based on svd:

perform PCA using SVD
    - data: M x N matrix of input data. (M dimensions, N trials)
    - PC: each column is a principle component
    - V: M x 1 matrix of variances
function pca(data::Array{T,2}) where T
    X = data .- mean(data, dims=2)
    Y = X' ./ sqrt(T(size(X,2)-1))
    U,S,PC = svd(Y)
    S = diagm(0=>S)
    V = S .* S
    # find the least variance vector
    indexList = sortperm(diag(V); rev=true)

    PCs = map(x->PC[:,x], indexList)
    return PCs, diag(V)[indexList]

I have data.
A need to do all step by step , without function.
What is ‘T’ in line Y = X’ ./ sqrt(T(size(X,2)-1)) ?