basiscor: Correlations in NMF Models
In renozao/NMF: Algorithms and Framework for Nonnegative Matrix Factorization (NMF)

Description Usage Arguments Details Methods (by generic) Examples

basiscor computes the correlation matrix between basis vectors, i.e. the columns of its basis matrix – which is the model's first matrix factor.

profcor computes the correlation matrix between basis profiles, i.e. the rows of the coefficient matrix – which is the model's second matrix factor.

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basiscor(x, y, ...)

profcor(x, y, ...)

`x`	a matrix or an object with suitable methods `basis` or `coef`.
`y`	a matrix or an object with suitable methods `basis` or `coef`, and dimensions compatible with `x`. If missing the correlations are computed between `x` and `y=x`.
`...`	extra arguments passed to `cor`.

Each generic has methods defined for computing correlations between NMF models and/or compatible matrices. The computation is performed by the base function cor.

basiscor:

basiscor(x = NMF,y = matrix): Computes the correlations between the basis vectors of x and the columns of y.
basiscor(x = matrix,y = NMF): Computes the correlations between the columns of x and the the basis vectors of y.
basiscor(x = NMF,y = NMF): Computes the correlations between the basis vectors of x and y.
basiscor(x = NMF,y = missing): Computes the correlations between the basis vectors of x.

profcor:

profcor(x = NMF,y = matrix): Computes the correlations between the basis profiles of x and the rows of y.
profcor(x = matrix,y = NMF): Computes the correlations between the rows of x and the basis profiles of y.
profcor(x = NMF,y = NMF): Computes the correlations between the basis profiles of x and y.
profcor(x = NMF,y = missing): Computes the correlations between the basis profiles of x.

# generate two random NMF models
a <- rnmf(3, 100, 20)
b <- rnmf(3, 100, 20)

# Compute auto-correlations
basiscor(a)
profcor(a)
# Compute correlations with b
basiscor(a, b)
profcor(a, b)

# try to recover the underlying NMF model 'a' from noisy data 
res <- nmf(fitted(a) + rmatrix(a), 3)

# Compute correlations with the true model
basiscor(a, res)
profcor(a, res)

# Compute correlations with a random compatible matrix
W <- rmatrix(basis(a))
basiscor(a, W)
identical(basiscor(a, W), basiscor(W, a))

H <- rmatrix(coef(a))
profcor(a, H)
identical(profcor(a, H), profcor(H, a))