Compute Confidence Intervals

Description

Compute and display confidence intervals for model estimates. Methods are provided for the mean of a numeric vector ci.default, the probability of a binomial vector ci.binom, and for lm, and lme objects are provided.

Usage

  ci(x, confidence=0.95, alpha=1 - confidence, ...)
  ## S3 method for class 'numeric'
ci(x, confidence=0.95, alpha=1-confidence, na.rm=FALSE, ...)
  ## S3 method for class 'binom'
ci(x, confidence=0.95, alpha=1-confidence, ...)
  ## S3 method for class 'lm'
ci(x, confidence=0.95, alpha=1-confidence, ...)
  ## S3 method for class 'lme'
ci(x, confidence=0.95, alpha=1-confidence, ...)
  
  ## S3 method for class 'estimable'
ci(x, confidence=0.95, alpha=1-confidence, ...)

Arguments

Details

ci.binom computes binomial confidence intervals using the Clopper-Pearson 'exact' method based on the binomial quantile function. Due to the discrete nature of the binomial distribution, this interval is conservative.

Value

vector or matrix with one row per model parameter and elements/columns Estimate, CI lower, CI upper, Std. Error, DF (for lme objects only), and p-value.

Author(s)

Gregory R. Warnes greg@warnes.net

See Also

confint, lm, 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)

# mer example
library(nlme)
Orthodont$AgeGroup <- gtools::quantcut(Orthodont$age)
fm2 <- lme(distance ~ Sex + AgeGroup, data = Orthodont,random=~1|Subject)
ci(fm2)

Results