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> library(mbbefd)
Loading required package: fitdistrplus
Loading required package: MASS
Loading required package: survival
Loading required package: alabama
Loading required package: numDeriv
Loading required package: Rcpp
Package:  mbbefd
Version:  0.8.12
Date:     
BugReport: https://github.com/spedygiorgio/mbbefd/issues


> library(fitdistrplus)
> 
> 
> #oistpareto
> n <- 1e3
> nboot <- 1000
> nboot <- 10
> set.seed(12345)
> x <- roistpareto(n, 2, 1/6)
> 
> f1 <- fitDR(x, "oistpareto", method="mle")
> summary(f1)
Fitting of the distribution ' oistpareto ' by maximum likelihood 
Parameters : 
   estimate Std. Error
a  1.882608  0.1811552
p1 0.173000  0.3782473
Loglikelihood:  -327.1999   AIC:  658.3999   BIC:  668.2154 
Correlation matrix:
  a  
a 1 0
  0 1

> summary(fitdist(x, "oistpareto", method="mle", start=list(a=1/mean(x), p1=etl(x))))#check
Fitting of the distribution ' oistpareto ' by maximum likelihood 
Parameters : 
    estimate Std. Error
a  1.8824254 0.18115376
p1 0.1730262 0.01196233
Loglikelihood:  -327.1999   AIC:  658.3999   BIC:  668.2154 
Correlation matrix:
   a p1
a  1  0
p1 0  1

Warning message:
In fitdist(x, "oistpareto", method = "mle", start = list(a = 1/mean(x),  :
  The doistpareto function should return a vector of with NaN values when input has inconsistent values and not raise an error
> 
> b1 <- bootDR(f1, niter=nboot)
> summary(b1)
Parametric bootstrap medians and 95% percentile CI 
     Median     2.5%    97.5%
a  1.988004 1.665848 2.087465
p1 0.165500 0.148575 0.196400
> 
> plot(b1, enhance=TRUE, trueval=c(2, 1/6))
> 
> hist(b1$estim[,1])
> hist(b1$estim[,2])
> 
> f2 <- fitDR(x, "oistpareto", method="tlmme")
> summary(f2)
Parameters : 
   estimate Std. Error
a  1.908612         NA
p1 0.173000         NA
Loglikelihood:  -327.2102   AIC:  658.4205   BIC:  668.236 
Correlation matrix:
[1] NA

> 
> gofstat(list(f1, f2))
Goodness-of-fit statistics
                             1-mle-oistpareto 2-tlmme-oistpareto
Kolmogorov-Smirnov statistic         0.173000           0.173000
Cramer-von Mises statistic           1.791505           1.798982
Anderson-Darling statistic                Inf                Inf

Goodness-of-fit criteria
                               1-mle-oistpareto 2-tlmme-oistpareto
Akaike's Information Criterion         658.3999           658.4205
Bayesian Information Criterion         668.2154           668.2360
> cdfcomp(list(f1, f2), do.points=FALSE)
> ppcomp(list(f1, f2))
> 
> proc.time()
   user  system elapsed 
   1.73    0.18    1.90