logLik.dynrCook function

Extract the log likelihood from a dynrCook Object

## S3 method for class 'dynrCook'
logLik(object, ...)

## S3 method for class 'dynrCook'
deviance(object, ...)

Arguments

• object: The dynrCook object for which the log likelihood is desired
• ...: further named arguments, ignored for this method

Returns

In the case of logLik, an object of class logLik.

Details

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.

Examples

# 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)

## Not run:

cook <- dynr.cook(model,
        verbose=FALSE, optimization_flag=FALSE, hessian_flag=FALSE)

# Now get the log likelihood!
logLik(cook)
## End(Not run)

See Also

Other S3 methods coef.dynrCook