Fit Univariate Distribution

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