jointRot: Resolve rotational ambiguity in samples of factor loadings...

In poworoznek/infinitefactor: Bayesian Infinite Factor Models

Description

Performs the varimax rotation on the factor loadings samples and column-based matching to resolve resultant sign and label switching. Rotates the factors along with the loadings to induce identifiability jointly. Note this method will only work on lists of factors and factor loadings that share the same constant number of factors (k) across all samples, and will likely crash the session if this is not the case.

Usage

jointRot(lambda, eta)

Arguments

lambda	list of factor loadings samples
eta	list of factor samples

Value

lambda	rotationally aligned factor loadings samples
eta	rotationally aligned factor samples

Author(s)

Evan Poworoznek

References

coming soon...

See Also

msf

Examples

k0 = 5
p = 20
n = 100

lambda = matrix(rnorm(p*k0, 0, 0.01), ncol = k0)
lambda[sample.int(p, 40, replace = TRUE) +
         p*(sample.int(k0, 40, replace = TRUE)-1)] = rnorm(40, 0, 1)
lambda[1:7, 1] = rnorm(7, 2, 0.5)
lambda[8:14, 2] = rnorm(7, -2, 0.5)
lambda[15:20, 3] = rnorm(6, 2, 0.5)
lambda[,4] = rnorm(p, 0, 0.5)
lambda[,5] = rnorm(p, 0, 0.5)
plotmat(varimax(lambda)[[1]])

X = matrix(rnorm(n*k0),n,k0)%*%t(lambda) + matrix(rnorm(n*p), n, p)

out = linearMGSP(X = X, nrun = 1000, burn = 500, adapt = FALSE)

aligned = jointRot(out$lambdaSamps, out$etaSamps)

plotmat(lmean(aligned$lambda))

poworoznek/infinitefactor documentation built on April 7, 2020, 1:10 p.m.