confint.dynrCook {dynr}

Confidence Intervals for Model Parameters

Description

Confidence Intervals for Model Parameters

Usage

## S3 method for class 'dynrCook'
confint(object, parm, level = 0.95,
  type = c("delta.method", "endpoint.transformation"),
  transformation = NULL, ...)

Arguments

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 type='endpoint.transformation' the transformation function used.

...

further named arguments. Ignored.

Details

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).

Value

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

References

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.

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 confidence intervals
# But note that they are nonsense because we set hessian_flag=FALSE !!!!
confint(cook)

## End(Not run)