R: Likelihood for the models.

pfa.neg.log.likelihood {dmt} | R Documentation

## Likelihood for the models.

### Description

Likelihood functions for the implemented models.

### Usage

    pfa.neg.log.likelihood(Wvec, phi, X)

### Arguments

`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.

### Details

Other likelihood functions will be added later.

### Value

Log-likelihood of the data, given the model parameters.

### Author(s)

Leo Lahti leo.lahti@iki.fi

### References

See citation("dmt").

### See Also

fit.dependency.model, pfa

### Examples

    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)

[Package dmt version 0.8.20 Index]