gmodels (version 2.16.2)

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

Arguments

x

object from which to compute confidence intervals.

confidence

confidence level. Defaults to 0.95.

alpha

type one error rate. Defaults to 1.0-confidence

na.rm

boolean indicating whether missing values should be removed. Defaults to FALSE.

…

Arguments for methods

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.

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.

See Also

confint, lm, summary.lm

Examples

# NOT RUN {
# 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)



# }