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% $Id: ci.Rd 1329 2009-05-09 04:00:15Z warnes $
%
\name{ci}
\alias{ci}
\alias{ci.default}
\alias{ci.binom}
\alias{ci.lm}
\alias{ci.lme}
\alias{ci.mer}
\title{Compute Confidence Intervals}
\description{ Compute and display confidence intervals for model
estimates. Methods are provided for the mean of a numeric vector
\code{ci.default}, the probability of a binomial vector
\code{ci.binom}, and for \code{lm}, \code{lme}, and \code{mer} objects are
provided. }
\synopsis{
ci(x, confidence = 0.95, alpha = 1 - confidence,...)
\method{ci}{default}(x, confidence = 0.95, alpha = 1 - confidence, na.rm=FALSE)...)
\method{ci}{binom}(x, confidence = 0.95, alpha = 1 - confidence,...)
\method{ci}{lm}(x, confidence = 0.95, alpha = 1 - confidence,...)
\method{ci}{lme}(x, confidence = 0.95, alpha = 1 - confidence,...)
\method{ci}{mer}(x, confidence = 0.95, alpha = 1 - confidence,
sim.mer=TRUE, n.sim=1000, ...)
}
\arguments{
\item{x}{ object from which to compute confidence intervals. }
\item{confidence}{ confidence level. Defaults to 0.95. }
\item{alpha}{type one error rate. Defaults to 1.0-\code{confidence} }
\item{na.rm}{boolean indicating whether missing values should be
removed. Defaults to \code{FALSE}.}
\item{\dots}{Arguments for methods}
\item{sim.mer}{Logical value. If TRUE confidence
intervals will be estimated using \code{\Link[Matrix]{mcmcsamp}}. This option only takes effect for mer
objects.}
\item{n.sim}{Number of samples to take in \code{\Link[Matrix]{mcmcsamp}}.}
}
%\details{
% ~~ If necessary, more details than the __description__ above ~~
%}
\value{
vector or matrix with one row per model parameter and elements/columns
\code{Estimate}, \code{CI lower}, \code{CI upper}, \code{Std. Error},
\code{DF} (for lme objects only), and \code{p-value}.
}
\author{ Gregory R. Warnes \email{greg@random-technologies-llc.com}
}
\seealso{
\code{\link[stats]{confint}},
\code{\link[stats]{lm}},
\code{\link[stats]{summary.lm}}
}
\examples{
# mean and confidence interval
ci( rnorm(10) )
# binomial proportion and exact confidence interval
b <- rbinom( prob=0.75, size=1, n=20 )
ci.binom(b) # direct call
class(b) <- 'binom'
ci(b) # indirect call
# confidence intervals for regression parameteres
data(state)
reg <- lm(Area ~ Population, data=as.data.frame(state.x77))
ci(reg)
\dontrun{
# mer example
library(lme4)
fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)
ci(fm2)
}
}
\keyword{ regression }
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