AdditiveLogLikelihood: Additive Model Log Likelihood

View source: R/AdditiveLogLikelihood.R

AdditiveLogLikelihoodR Documentation

Additive Model Log Likelihood

Description

Computes the log-likelihood of a spike train under the assumptions of the additive model.

Usage

AdditiveLogLikelihood(
  param,
  f.hat,
  w0.hat.itr,
  setup.pars,
  ct,
  ct.spike.times,
  individual.spike.train
)

Arguments

param

a numeric vector containing the value of the eta and gamma parameters in the model. For a model containing K frequencies, the first K numbers are eta values and the second K numbers are gamma values. This must be the first argument as we wish to maximize the value of the log-likelihood function over the entire set of eta and gamma parameters.

f.hat

a numeric vector containing frequency estimates for a particular model.

w0.hat.itr

a numeric vector containing phase estimates for a particular model and spike train.

setup.pars

a list of additional parameters for the likelihood function, computed by the SetupLikelihoods() function.

ct

a numeric vector containing the estimated piecewise constant intensity function. The length of ct should be a whole number power of 2.

ct.spike.times

a numeric vector containing the values of ct at the specific times a spike was recorded.

individual.spike.train

a numeric vector containing the spike times for that spike train.

Value

The value of the log-likelihood function for the additive model.

References

Ramezan, R., Marriott, P., and Chenouri, S. (2014), Statistics in Medicine, 33(2), 238-256. doi: 10.1002/sim.5923.


dpwynne/mmnst documentation built on Aug. 1, 2023, 8:08 a.m.