ci: Compute Confidence Intervals In gmodels: Various R Programming Tools for Model Fitting

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ``` 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

 `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

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 [email protected]

`confint`, `lm`, `summary.lm`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```# 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) ```