R Under development (unstable) (2024-10-16 r87241 ucrt) -- "Unsuffered Consequences"
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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


> 
> data(lossalaefull)
> x <- lossalaefull$Loss/lossalaefull$Limit
> 
> 
> # fitDR(x, "oigbeta", method="mle", control=list(trace=1, REPORT=1))
> 
> dlist <- c("oistpareto", "oibeta", "oigbeta")
> flist <- lapply(dlist, function(d) {print(d);
+   fitDR(x, d, method="mle")})
[1] "oistpareto"
[1] "oibeta"
[1] "oigbeta"
> names(flist) <- dlist
> 
> gof1 <- gofstat(flist)
> 
> mm <- rbind(KS = gof1$ks, CvM = gof1$cvm, AD = gof1$ad, AIC = gof1$aic, BIC = gof1$bic)
> rownames(mm) <- c("Kolmogorov-Smirnov statistic", "Cramer-von Mises statistic", 
+                   "Anderson-Darling statistic", "Aikake's Information Criterion", "Bayesian Information Criterion")
> 
> 
> cdfcomp(flist, do.points=FALSE, leg=dlist, addlegend = FALSE, fitlwd=1.5, main="Emp./theo. CDFs - insurance")
> legend("topleft", col=c("red", "green", "blue"), leg=dlist, lty=1:3, lwd=2, bty="n")
> 
> 
> par(mar=c(4,4,2,1))
> cdfcomp(flist, do.points=FALSE, leg=dlist, xlogscale = TRUE, addlegend = FALSE, fitlwd=1.5, main="Emp./theo. CDFs - insurance")
> legend("topleft", col=c("red", "green", "blue"), leg=dlist, lty=1:3, lwd=2, bty="n")
> 
> eccomp(flist, leg=dlist, do.points = FALSE, addlegend = FALSE, lwd=1.5)
> legend("topleft", col=c("red", "green", "blue"), leg=dlist, lty=1:3, lwd=2, bty="n")
> 
> ppcomp(flist, leg=dlist, fitpch=".", addlegend = FALSE)
> legend("bottomright", fill=c("red", "green", "blue", "cyan", "purple"), leg=dlist)
> 
> 
> qqcomp(flist, leg=dlist, use.ppoints=TRUE)
Warning messages:
1: In qfun(p, lower.tail = TRUE, log.p = FALSE, ...) : NaNs produced
2: In qbeta(p, shape1, shape2, lower.tail = TRUE, log.p = FALSE) :
  NaNs produced
> 
> proc.time()
   user  system elapsed 
   2.35    0.26    2.62