gmodels (version 2.15.2)

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, lme, and mer objects are provided.

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

sim.mer

Logical value. If TRUE confidence intervals will be estimated using mcmcsamp. This option only takes effect for mer objects.

n.sim

Number of samples to take in mcmcsamp.

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.

synopsis

ci(x, confidence = 0.95, alpha = 1 - confidence,...) ## S3 method for class 'default': 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 'mer': ci(x, confidence = 0.95, alpha = 1 - confidence, sim.mer=TRUE, n.sim=10000, ...)

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(lme4)
fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)
ci(fm2)