Fit Univariate Distribution

fit_univariate(x, distribution, type = "continuous")

Arguments

x

numeric vector

distribution

character name of distribution

type

discrete or continuous data

Value

a fitted list object of d, p, q, r distribution functions and parameters, MLE for probability distributions, custom fit for empirical

Examples

# Fit Discrete Distribution set.seed(42) x <- rpois(1000, 3) fitted <- fit_univariate(x, 'pois', type = 'discrete') # density function plot(fitted$dpois(x=0:10), xlab = 'x', ylab = 'dpois')
# distribution function plot(fitted$ppois(seq(0, 10, 1)), xlab= 'x', ylab = 'ppois')
# quantile function plot(fitted$qpois, xlab= 'x', ylab = 'qpois')
# sample from theoretical distribution summary(fitted$rpois(100))
#> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 0.00 1.00 3.00 2.75 4.00 10.00
# estimated parameters from MLE fitted$parameters
#> lambda #> 2.93
set.seed(24) x <- rweibull(1000, shape = .5, scale = 2) fitted <- fit_univariate(x, 'weibull') # density function plot(fitted$dweibull, xlab = 'x', ylab = 'dweibull')
# distribution function plot(fitted$pweibull, xlab = 'x', ylab = 'pweibull')
# quantile function plot(fitted$qweibull, xlab = 'x', ylab = 'qweibull')
# sample from theoretical distribution summary(fitted$rweibull(100))
#> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 0.00001 0.18442 1.18814 4.83963 5.18201 81.99765
# estimated parameters from MLE fitted$parameters
#> shape scale #> 0.4879054 2.0564428