# ci: Confidence interval of probabilty, mean and incidence In epiDisplay: Epidemiological Data Display Package

## Description

Compute confidence interval(s) of variables or values input from keyboard.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```ci(x, ...) ## Default S3 method: ci(x,...) ## S3 method for class 'binomial' ci(x, size, precision, alpha = 0.05, ...) ## S3 method for class 'numeric' ci(x, n, sds, alpha = 0.05, ...) ## S3 method for class 'poisson' ci(x, person.time, precision, alpha = 0.05, ...) ```

## Arguments

 `x` a variable for 'ci', number of success for 'ci.binomial', mean(s) for 'ci.numeric', and counts for 'ci.poisson' `size` denominator for success `precision` level of precision used during computation for the confidence limits `alpha` significance level `n` sample size `sds` standard deviation `person.time` denominator for count `...` further arguments passed to or used by other methods

## Details

These functions compute confidence intervals of probability, mean and incidence from variables in a dataset or values from keyboard input.

'ci' will try to identify the nature of the variable 'x' and determine the appropriate method (between 'ci.binomial' and 'ci.numeric') for computation. 'ci' without a specified method will never call 'ci.poisson'.

The specific method, ie. 'ci.binomial', 'ci.numeric' or 'ci.poisson', should be used when the values are input from the keyboard or from an aggregated data frame with columns of variables for the arguments.

'ci.binomial' and 'ci.numeric' employ exact probability computation while 'ci.numeric' is based on the t-distribution assumption.

## Value

'ci.binomial' and 'ci.poisson' return a data frame containing the number of events, the denominator and the incidence rate. 'ci.numeric' returns means and standard deviations. All of these are followed by the standard error and the confidence limit, the level of which is determined by 'alpha'

## Author(s)

Virasakdi Chongsuvivatwong <[email protected]>

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54``` ```data(Oswego) .data <- Oswego attach(.data) # logical variable ci(ill) # numeric variable ci(age) # factor ci(sex=="M") ci(sex=="F") detach(.data) # Example of confidence interval for means library(MASS) .data <- Cars93 attach(.data) car.price <- aggregate(Price, by=list(type=Type), FUN=c("mean","length","sd")) car.price ci.numeric(x=car.price\$mean, n=car.price\$length, sds=car.price\$sd.Price ) detach(.data) rm(list=ls()) # Example of confidence interval for probabilty data(ANCdata) .data <- ANCdata attach(.data) death1 <- death=="yes" death.by.group <- aggregate.numeric(death1, by=list(anc=anc, clinic=clinic), FUN=c("sum","length")) death.by.group ci.binomial(death.by.group\$sum.death1, death.by.group\$length) detach(.data) rm(list=ls()) # Example of confidence interval for incidence data(Montana) .data <- Montana attach(.data) des(.data) age.Montana <- aggregate.data.frame(Montana[,1:2], by=list(agegr=Montana\$agegr),FUN="sum") age.Montana ci.poisson(age.Montana\$respdeath, person.time=age.Montana\$personyrs) detach(.data) rm(list=ls()) # Keyboard input # What is the 95 % CI of sensitivity of a test that gives all # positive results among 40 diseased individuals ci.binomial(40,40) # What is the 99 % CI of incidence of a disease if the number # of cases is 25 among 340,000 person-years ci.poisson(25, 340000, alpha=.01) # 4.1 to 12.0 per 100,000 person-years ```