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\name{mxCI}
\alias{mxCI}

\title{Create mxCI Object}

\description{
This function creates a new \link{MxCI} object, which allows estimation of likelihood-based confidence intervals in a model
(note: to estimate SEs around arbitrary objects, see \code{\link{mxSE}})
}

\usage{
mxCI(reference, interval = 0.95, type=c("both", "lower", "upper"), ..., boundAdj=TRUE)
}

\arguments{
\item{reference}{A character vector of free parameters, mxMatrices, mxMatrix elements and mxAlgebras on which confidence intervals are to be estimated, listed by name.}
\item{interval}{A scalar numeric value indicating the confidence interval to be estimated. Must be between 0 and 1. Defaults to 0.95.}
\item{type}{A character string indicating whether the upper, lower or both confidence limits are returned. Defaults to "both".}
\item{...}{Not used.  Forces remaining arguments to be specified by
  name.}
\item{boundAdj}{Whether to correct the likelihood-based confidence
  intervals for a lower or upper bound.}
}

\details{
The mxCI function creates \link{MxCI} objects, which can be used as arguments in \link{MxModel} objects. When models containing \link{MxCI} objects are optimized using \link{mxRun} with the \sQuote{intervals} argument set to TRUE, likelihood-based confidence intervals are returned. The likelihood-based confidence intervals calculated by \link{MxCI} objects are symmetric with respect to the change in likelihood in either direction, and are not necessarily symmetric around the parameter estimate. Estimation of confidence intervals requires both that an \link{MxCI} object be included in the model and that the \sQuote{intervals} argument of the \link{mxRun} function is set to TRUE. When estimated, confidence intervals can be accessed in the model output at \code{$output$confidenceIntervals} or by using \link{summary} on a fitted \link{MxModel} object. 

A typical use case is when a model includes non-linear constraints,
and hence, standard errors are not available.
In all cases, a two-sided hypothesis test is assumed.
Therefore, the upper bound will
exclude 2.5\% (for interval=0.95) even though only one bound is
requested. To obtain a one-sided CI for a one-sided hypothesis test,
interval=0.90 will obtain a 95\% confidence interval.

When a confidence interval is requested for a free parameter (not an
algebra) constrained by a lower bound or an upper bound (but not both)
and \code{boundAdj=TRUE} then the Wu & Neale (2012) correction is used.
This improves the accuracy of the confidence interval when the parameter
is estimated close to the bound. For example, this correction will be
activated when a variance with a lower bound of \eqn{10^{-6}} and no
upper bound that is estimated close to the bound. The sample size, or
more precisely effective sample size for that particular parameter, will
determine how close the variance needs to be to the bound at
\eqn{10^{-6}} to activate the correction.

The likelihood-based confidence intervals returned using \link{MxCI} are obtained by increasing or decreasing the value of each parameter until the -2 log likelihood of the model increases by an amount corresponding to the requested interval. The confidence limit specified by the \sQuote{interval} argument is transformed into a corresponding difference in the model -2 log likelihood based on the likelihood ratio test. Thus, a requested confidence interval for a parameter will first determine the corresponding quantile from the chi-squared distribution with one degree of freedom (a value of 3.841459 when a 95 percent confidence interval is requested). That quantile will be populated into either the \sQuote{lowerdelta} slot, the \sQuote{upperdelta} slot, or both in the output \link{MxCI} object. 

Estimation of likelihood-based confidence intervals begins after optimization has been completed, with each parameter moved in the direction(s) specified in the \sQuote{type} argument until the specified increase in -2 log likelihood is reached. All other free parameters are left free for this stage of optimization. This process repeats until all confidence intervals have been calculated. The calculation of likelihood-based confidence intervals can be computationally intensive, and may add a significant amount of time to model estimation when many confidence intervals are requested.

Multiple parameters, \link[=MxMatrix]{MxMatrices} and \link[=MxAlgebra]{MxAlgebras} may be listed in the \sQuote{reference} argument. Individual elements of \link[=MxMatrix]{MxMatrices} and \link[=MxAlgebra]{MxAlgebras} may be listed as well, using the syntax \dQuote{matrix[row,col]} (see \link{Extract} for more information). Only scalar numeric values for the \sQuote{interval} argument are supported. Users requesting different confidence ranges for different parameters must use separate \link{mxCI} statements. \link{MxModel} objects can hold multiple \link{MxCI} objects, but only one confidence interval may be requested per named-entity.

Confidence interval estimation may result in model non-convergence at the confidence limit. Separate optimizer messages may be passed for each confidence limit. This has no impact on the parameter estimates themselves, but may indicate a problem with the referenced confidence limit. Model non-convergence for a particular confidence limit may indicate parameter interdependence or the influence of a parameter boundary. 

These error messages and their meanings are listed in the help for \link{mxSummary}

The validity of a confidence limit can be checked by running a model with the appropriate parameter fixed at the confidence limit in question. If the confidence limit is valid, the -2 log likelihoods of these two models should differ by the specified chi-squared criterion (as set using the \sQuote{lowerdelta} or \sQuote{upperdelta} slots in the \link{MxCI} object (you can choose which of these to set via the type parameter of mxCI). 

}
    
\value{
Returns a new \link{MxCI} object. If used as an argument in an \link{MxModel} object, the parameters, \link[=MxMatrix]{MxMatrices} and \link[=MxAlgebra]{MxAlgebras} listed in the 'reference' argument must also be included prior to optimization.
}

\references{
The OpenMx User's guide can be found at http://openmx.ssri.psu.edu/documentation. Additional support for mxCI() can be found on the OpenMx wiki at http://openmx.ssri.psu.edu/wiki.

Neale, M. C. & Miller M. B. (1997). The use of likelihood based
confidence intervals in genetic models.  \emph{Behavior Genetics,
27}(2), 113-120.

Pek, J. & Wu, H. (2015). Profile likelihood-based confidence intervals
and regions for structural equation models.  \emph{Psychometrika,
80}(4), 1123-1145.

Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a
bounded parameter. \emph{Behavior genetics, 42}(6), 886-898.
}

\seealso{
  \code{\link{mxSE}} for computing SEs around arbitrary objects.
  \code{\link{mxComputeConfidenceInterval}} is the internal compute plan that implements the algorithm.
  \link{MxMatrix} and \link{mxMatrix} for free parameter specification.
  \code{\link{MxCI}} for the S4 class created by mxCI.
  More information about the OpenMx package may be found \link[=OpenMx]{here}. 
}

\examples{

library(OpenMx)

# generate data
covariance <- matrix(c(1.0, 0.5, 0.5, 1.0), 
    nrow=2, 
    dimnames=list(c("a", "b"), c("a", "b")))
    
data <- mxData(covariance, "cov", numObs=100)

# create an expected covariance matrix
expect <- mxMatrix("Symm", 2, 2,
    free=TRUE,
    values=c(1, .5, 1),
    labels=c("var1", "cov12", "var2"),
    name="expectedCov")

# request 95 percent confidence intervals   
ci <- mxCI(c("var1", "cov12", "var2"))

# specify the model
model <- mxModel(model="Confidence Interval Example",
    data, expect, ci,
    mxExpectationNormal("expectedCov", dimnames=c("a", "b")),
    mxFitFunctionML())

# run the model 
results <- mxRun(model, intervals=TRUE)

# view confidence intervals
print(summary(results)$CI)

# view all results
summary(results)

# remove a specific mxCI from a model
model <- mxModel(model, remove=TRUE, model$intervals[['cov12']])
model$intervals

# remove all mxCI from a model
model <- mxModel(model, remove=TRUE, model$intervals)
model$intervals

}
