FitUltD

The goal of FitUltD is to fit data that can't be fitted with ordinary density functions

Installation

You can install the released version of FitUltD from CRAN with:

install.packages("FitUltD")

Example

This is a basic example which shows you how to fit a multimodal random variable choosing your own distributions:

library(FitUltD)
#> Loading required package: mclust
#> Package 'mclust' version 5.4.5
#> Type 'citation("mclust")' for citing this R package in publications.
#Random Variable
set.seed(3110)

RV <- c(rnorm(73,189,12), rweibull(82,401,87), rgamma(90,40,19))

Nombres <- c("norm","weibull","gamma","exp","cauchy")

FIT1 <- FDistUlt(RV, plot = TRUE, subplot = TRUE)

One of the available options is to show the distribution functions that passed the Anderson Darling and Kolmogorov Smirnov tests, as well as their p-value and the proportion of the total distribution.

FIT1[[3]]
#>                           Distribution Dist_Prop  Dist    AD_p.v    KS_p.v
#> AD7  gamma(252.339, 293.811)*222.168+0 0.2979592 gamma 0.8859093 0.9397635
#> AD2                 norm(86.894, 0.27) 0.3346939  norm 0.5466113 0.7882263
#> AD71     gamma(51.093, 73.537)*2.999+0 0.3673469 gamma 0.4460519 0.6231112
#>      estimate1   estimate2 estimateLL1 estimateLL2 method     PV_S Obs
#> AD7  252.33913 293.8110186           0   222.16825    mge 1.825673  73
#> AD2   86.89350   0.2698051           0     1.00000    mge 1.334838  82
#> AD71  51.09258  73.5366847           0     2.99879    mge 1.069163  90
#>         Lim_inf   Lim_sup
#> AD7  162.249575 222.16825
#> AD2   85.842947  87.33807
#> AD71   1.483556   2.99879

By setting plot and subplot arguments as TRUE, is possible to visualizate each distribution which forms the most accurrate model.

Real data distribution versus fitted model.

Distributions that forms the fitted model.