Extract the log likelihood from a dynrCook Object
Extract the log likelihood from a dynrCook Object
## S3 method for class 'dynrCook' logLik(object, ...) ## S3 method for class 'dynrCook' deviance(object, ...)
object |
The dynrCook object for which the log likelihood is desired |
... |
further named arguments, ignored for this method |
The 'df' attribute for this object is the number of freely estimated parameters. The 'nobs' attribute is the total number of rows of data, adding up the number of time points for each person.
The deviance method returns minus two times the log likelihood.
In the case of logLik, an object of class logLik.
Other S3 methods coef.dynrCook
# Minimal model
require(dynr)
meas <- prep.measurement(
values.load=matrix(c(1, 0), 1, 2),
params.load=matrix(c('fixed', 'fixed'), 1, 2),
state.names=c("Position","Velocity"),
obs.names=c("y1"))
ecov <- prep.noise(
values.latent=diag(c(0, 1), 2),
params.latent=diag(c('fixed', 'dnoise'), 2),
values.observed=diag(1.5, 1),
params.observed=diag('mnoise', 1))
initial <- prep.initial(
values.inistate=c(0, 1),
params.inistate=c('inipos', 'fixed'),
values.inicov=diag(1, 2),
params.inicov=diag('fixed', 2))
dynamics <- prep.matrixDynamics(
values.dyn=matrix(c(0, -0.1, 1, -0.2), 2, 2),
params.dyn=matrix(c('fixed', 'spring', 'fixed', 'friction'), 2, 2),
isContinuousTime=TRUE)
data(Oscillator)
data <- dynr.data(Oscillator, id="id", time="times", observed="y1")
model <- dynr.model(dynamics=dynamics, measurement=meas,
noise=ecov, initial=initial, data=data)
cook <- dynr.cook(model,
verbose=FALSE, optimization_flag=FALSE, hessian_flag=FALSE)
# Now get the log likelihood!
logLik(cook)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.