Apply a confidence interval to values in a dataframe based on the beta distribution.

Apply a confidence interval to values in a dataframe based on the beta distribution.

Usage

beta_interval(.data, alpha, beta, conf = 0.95)

Arguments

.data

Data frame; a confidence interval will be applied to each row in the data frame.

alpha

First shape parameter of the beta distribution, must be greater than 0

beta

Second shape parameter of the beta distribution, must be greater than 0

conf

Confidence interval, must be between [0, 1]. Defaults to 0.95.

See also

Other interval verbs: normal_interval()

Examples

# create example df
alpha <- rnorm(10, 100, 20)
beta <- rnorm(10, 80, 10)
example <- dplyr::bind_cols(alpha, beta)
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
colnames(example) <- c("alpha", "beta")

# apply the default confidence interval of 0.95
beta_interval(example, alpha, beta)
#> # A tibble: 10 × 4
#>    alpha  beta ci_lower ci_upper
#>    <dbl> <dbl>    <dbl>    <dbl>
#>  1  72.0  74.5    0.411    0.572
#>  2 105.   86.3    0.478    0.619
#>  3  51.3 101.     0.265    0.414
#>  4  99.9  63.7    0.535    0.684
#>  5 112.   85.1    0.500    0.637
#>  6 123.   61.4    0.598    0.733
#>  7  63.6  74.8    0.377    0.543
#>  8  95.1  79.5    0.471    0.618
#>  9  95.1  85.4    0.454    0.599
#> 10  94.3  70.9    0.495    0.645

# apply a custom confidence interval
beta_interval(example, alpha, beta, conf = 0.99)
#> # A tibble: 10 × 4
#>    alpha  beta ci_lower ci_upper
#>    <dbl> <dbl>    <dbl>    <dbl>
#>  1  72.0  74.5    0.386    0.597
#>  2 105.   86.3    0.456    0.640
#>  3  51.3 101.     0.244    0.439
#>  4  99.9  63.7    0.511    0.705
#>  5 112.   85.1    0.478    0.658
#>  6 123.   61.4    0.575    0.752
#>  7  63.6  74.8    0.353    0.569
#>  8  95.1  79.5    0.447    0.640
#>  9  95.1  85.4    0.431    0.621
#> 10  94.3  70.9    0.471    0.668