linearDL: Sample Bayesian linear infinite factor models with the...

In infinitefactor: Bayesian Infinite Factor Models

Description

Perform Bayesian factor analysis by sampling the posterior distribution of parameters in a factor model with the Dirichlet-Laplace shrinkage prior of Bhattacharya et al.

Usage

linearDL(X, nrun, burn, thin = 1, prop = 1, 
epsilon = 1e-3, k = NULL,
output = c("covMean", "covSamples", "factSamples",
"sigSamples"), verbose = TRUE, dump = FALSE,
filename = "samps.Rds", buffer = 10000,
augment = NULL)

Arguments

X	Data matrix (n x p)
nrun	number of iterations
burn	burn-in period
thin	thinning interval
prop	proportion of elements in each column less than epsilon in magnitude cutoff
epsilon	tolerance
k	Number of factors
output	output type, a vector including some of: c("covMean", "covSamples", "factSamples", "sigSamples")
verbose	logical. Show progress bar?
dump	logical. Save output object during sampling?
filename	if dump, filename for output
buffer	if dump, frequency of saving
augment	additional sampling steps as an expression

Value

some of:

covMean	X covariance posterior mean
omegaSamps	X covariance posterior samples
lambdaSamps	Posterior factor loadings samples (rotationally ambiguous)
etaSamps	Posterior factor samples (rotationally ambiguous)
sigmaSamps	Posterior marginal variance samples (see notation in Bhattacharya and Dunson (2011))
numFacts	Number of factors for each iteration

Author(s)

Evan Poworoznek

References

Bhattacharya, Anirban, et al. "Dirichlet-Laplace priors for optimal shrinkage." Journal of the American Statistical Association 110.512 (2015): 1479-1490.

See Also

linearDL

Examples

k0 = 5
p = 20
n = 50

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)

infinitefactor documentation built on April 3, 2020, 5:09 p.m.