coef.dynrModel | R Documentation |
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, ...)
object |
The dynrCook object for which the coefficients are desired |
... |
further named arguments, ignored for this method |
value |
values for setting |
A numeric vector of the fitted parameters.
Other S3 methods logLik.dynrCook
# 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)
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