pca

score, coeff, latent, explained, mu, ems = pca(X; DT::DataType=Float32, npc=0)

• X: A n-by-p data matrix X. Rows of X correspond to observations and columns correspond to variables. Must be a Float type (either 32 or 64).

• npc: The number of eigenvectors used to construct the solution. Its value must be in the range [1, npc]. The default npc=0 means we use the full solution, that is npc = p. Use this option when X is big and you want to save some resources (time, memory) by not computing components that will have a very small explained variance.

• DT: The Data Type. Internally, the algorithm makes a copy of the input X matrix because it will be modified. DT controls what type that copy will assume. Float32 or Float64? By default, we use the same data type as in X, but for big matrices it may be desirable to use Float32 if that saves memory. Note: the default is different in the methods referred below, where it defaults to Float32 because image data is almost always UInt8 or UInt16 and grids are Float32.

Returns

• score: The principal components.

• coeff: The principal component coefficients for the matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is npc-by-n. Each column of coeff contains coefficients for one principal component, and the columns are in descending order of component variance.

• latent: The principal component variances. (The eigenvalues of cov(X))

• explained: The percentage of the total variance explained by each principal component.

• mu: The mean of each variable in X.

• ems: The mean square error incurred in using only the npc eigenvectors corresponding to the largest eigenvalues. ems is 0 if npc = n (the default).

Ipca = pca(I::GMTimage; DT::DataType=Float32, npc=0) -> GMTimage{UInt8}

This method takes a GMTimage cube, normally satellite data of UInt16 type created with the RemoteS package, and returns a GMTimage cube of UInt8 of the principal components in decreasing order of explained variance. The truecolor(Ipca) (from RemoteS) will show a false color image made of the three largest components.

Gpca = pca(G::GMTgrid; DT::DataType=Float32, npc=0) -> GMTgrid{DT}

This method takes a GMTgrid cube and returns another grid, of type DT (Float32 by default), with principal components in decreasing order of explained variance.

Source Code

This function has multiple methods:

• pca(G::GMTgrid; DT, npc) - pca.jl:140
• pca(I::GMTimage; DT, npc) - pca.jl:113
• pca(X::AbstractArray{T}; DT, npc) where T<:Real - pca.jl:108