Confidence Intervals for Model Parameters
Confidence Intervals for Model Parameters
## S3 method for class 'dynrCook'
confint(object, parm, level = 0.95,
type = c("delta.method", "endpoint.transformation"),
transformation = NULL, ...)object |
a fitted model object |
parm |
which parameters are to be given confidence intervals |
level |
the confidence level |
type |
The type of confidence interval to compute. See details. Partial name matching is used. |
transformation |
For |
... |
further named arguments. Ignored. |
The parm argument can be a numeric vector or a vector of names. If it is missing then it defaults to using all the parameters.
These are Wald-type confidence intervals based on the standard errors of the (transformed) parameters. Wald-type confidence intervals are known to be inaccurate for variance parameters, particularly when the variance is near zero (See references for issues with Wald-type confidence intervals).
A matrix with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 as a percentage (e.g. by default 2.5
Pritikin, J.N., Rappaport, L.M. & Neale, M.C. (In Press). Likelihood-Based Confidence Intervals for a Parameter With an Upper or Lower Bound. Structural Equation Modeling. DOI: 10.1080/10705511.2016.1275969
Neale, M. C. & Miller M. B. (1997). The use of likelihood based confidence intervals in genetic models. Behavior Genetics, 27(2), 113-120.
Pek, J. & Wu, H. (2015). Profile likelihood-based confidence intervals and regions for structural equation models. Psychometrica, 80(4), 1123-1145.
Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior genetics, 42(6), 886-898.
# 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 confidence intervals
# But note that they are nonsense because we set hessian_flag=FALSE !!!!
confint(cook)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.