likelihood: Likelihood for the models.
In dmt: Dependency Modeling Toolkit

Description Usage Arguments Details Value Author(s) References See Also Examples

Likelihood functions for the implemented models.

1	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)