coef.dynrCook function

Extract fitted parameters from a dynrCook Object

aliases coef.dynrModel coef<- coef<-.dynrModel

## S3 method for class 'dynrModel'
coef(object, ...)

coef(object) <- value

## S3 replacement method for class 'dynrModel'
coef(object) <- value

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

Arguments

• object: The dynrCook object for which the coefficients are desired
• ...: further named arguments, ignored for this method
• value: values for setting

Returns

A numeric vector of the fitted parameters.

Examples

# Create a minimal cooked model called 'cook'
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 grab the coef!
coef(cook)
## End(Not run)

See Also

Other S3 methods logLik.dynrCook