FitAdditiveModel: Fit additive model

In dpwynne/mmnst: Multiscale Modeling of Neural Spike Trains

Fit additive model

Description

Fits the additive multiscale model.

Usage

FitAdditiveModel(spikes, f.hat, w0.hat, setup.pars, terminal.points, ct)

Arguments

spikes	a list of spike trains.
f.hat	a numeric vector containing frequency estimates for a particular model.
w0.hat	a matrix containing phase estimates for each spike train for a particular model.
setup.pars	a list of additional parameters for the likelihood function, computed by the SetupLikelihoods() function.
terminal.points	a numeric vector containing the time points at which c(t) changes.
ct	a numeric vector containing the estimated piecewise constant intensity function c(t). The length of ct should be a whole number power of 2.

Value

A list of length 3. The first item in the list is a matrix whose rows each contain the MLEs of eta for a single spike train. The second item in the list is a matrix whose rows each contain the MLEs of gamma for a single spike train. The third item in the list is a matrix whose rows each contain the AIC, AICc, BIC, and log-likelihood for the model for a single spike train

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.