Fits a beta distribution based on quantiles

Fits a beta distribution based on quantiles

Usage

fitbeta_ml(point, lci, uci)

fitbeta(point, lci, uci)

Arguments

point

Point estimates corresponding to the median

lci

Lower limit (quantile 0.025)

uci

Upper limit (quantile 0.975)

Value

parameters shape1 and shape2 of a beta distribution

Functions

• fitbeta_ml(): using ML to estimate parameters

• fitbeta(): preserve the expected value

Note

This is a wrap of the fitdist to obtain the best parameters for a beta distribution based on quantiles.

When using confidence intervals (not ML), the shape parameters are obtained using the following formula:

\(varp = (p_uci-p_lci)/4^2\)

\(shape1 = p_mean * (p_mean * (1 - p_mean) / varp - 1)\)

\(shape2 =(1 - p_mean) * (p_mean * (1 - p_mean) / varp - 1) \)

See also

fitdist

Author

John J. Aponte

Examples

fitbeta_ml(0.45,0.40,0.50)
#>   shape1   shape2 
#> 170.5639 208.4047 
fitbeta(0.45,0.40,0.50)
#> shape1 shape2 
#> 177.75 217.25