| pfa.neg.log.likelihood {dmt} | R Documentation |
Likelihood functions for the implemented models.
pfa.neg.log.likelihood(Wvec, phi, X)
Wvec |
Parameter vector which is converted in the latent covariance structure used in PFA, correspond to W*t(W) in the model X = Wz + epsilon. |
phi |
Marginal covariance in the model X = Wz + epilon with epsilon ~ N(0, phi). |
X |
Data: features x samples matrix. |
Other likelihood functions will be added later.
Log-likelihood of the data, given the model parameters.
Leo Lahti leo.lahti@iki.fi
See citation("dmt").
fit.dependency.model, pfa
library(dmt)
# Generate toydata
N <- 100
xdim <- 10
zdim <- 3
toy <- generate.toydata(N = N, zDim = zdim, xDim = xdim, yDim = xdim,
marginal.covariances = "diagonal")
# Estimate model parameters
res <- pfa(toy$X, zDimension = zdim)
W <- res@W$total
phi <- res@phi$total
# wtw <- crossprod(t(W)) # is the same as W * t(W)
# Calculate negative log-likelihood for the model
L <- pfa.neg.log.likelihood(W, phi,toy$X)