# Confidence Interval for Binomial Proportions

Source:`R/AllGenerics.R`

, `R/statistics.R`

`confidence_binomial.Rd`

Computes a Wald interval for a proportion at a desired level of significance.

## Usage

```
confidence_binomial(object, ...)
# S4 method for numeric
confidence_binomial(
object,
n,
level = 0.95,
method = "wald",
corrected = FALSE
)
```

## Arguments

- object
A

`numeric`

vector giving the number of success.- ...
Currently not used.

- n
A length-one

`numeric`

vector giving the number of trials.- level
A length-one

`numeric`

vector giving the confidence level. Must be a single number between \(0\) and \(1\).- method
A

`character`

string specifying the method to be used. Any unambiguous substring can be used.- corrected
A

`logical`

scalar: should continuity correction be used? Only used if`method`

is "`wald`

".

## Value

A length-two `numeric`

vector giving the lower and upper confidence
limits.

## See also

Other summary statistics:
`confidence_mean()`

,
`confidence_multinomial()`

,
`interval_credible()`

,
`interval_hdr()`

## Examples

```
## Confidence interval for a mean
x <- seq(from = -4, to = 4, by = 0.01)
y <- dnorm(x)
confidence_mean(y, type = "student")
#> lower upper
#> 0.1151118 0.1345606
confidence_mean(y, type = "normal")
#> lower upper
#> 0.1151265 0.1345459
## Confidence interval for a propotion
confidence_binomial(118, n = 236)
#> lower upper
#> 0.4362086 0.5637914
x <- c(35, 74, 22, 69)
confidence_multinomial(x)
#> lower upper
#> [1,] 0.12234021 0.2276598
#> [2,] 0.30308797 0.4369120
#> [3,] 0.06663649 0.1533635
#> [4,] 0.27911853 0.4108815
```