Running 't-CIcdfplot.R' [14s]
  Running 't-Surv2fitdistcens.R' [2s]
  Running 't-bootdist.R' [5s]
  Running 't-bootdistcens.R' [2s]
  Running 't-cdfcomp.R' [3s]
  Running 't-cdfcompcens.R' [4s]
  Running 't-cvg-algo.R' [0s]
  Running 't-denscomp.R' [4s]
  Running 't-descdist.R' [2s]
  Running 't-detectbound.R' [2s]
  Running 't-fitbench.R' [0s]
  Running 't-fitdist-customoptim.R' [2s]
  Running 't-fitdist.R' [3s]
  Running 't-fitdistcens.R' [4s]
  Running 't-gen-max-spacing-estim.R' [2s]
  Running 't-getparam.R' [1s]
  Running 't-gofstat.R' [2s]
  Running 't-init-actuar.R' [0s]
  Running 't-llplot.R' [3s]
  Running 't-lnL-surf.R' [2s]
  Running 't-logLik-vcov-coef.R' [2s]
  Running 't-manageparam.R' [2s]
  Running 't-mgedist.R' [3s]
  Running 't-mledist-cens.R' [2s]
  Running 't-mledist-nocens.R' [3s]
  Running 't-mledist-paramsupport.R' [3s]
  Running 't-mmedist.R' [4s]
  Running 't-msedist.R' [3s]
  Running 't-parallel.R' [0s]
  Running 't-plotdist.R' [2s]
  Running 't-plotdistcens.R' [2s]
  Running 't-ppcomp.R' [3s]
  Running 't-ppcompcens.R' [3s]
  Running 't-prefit.R' [2s]
  Running 't-qme-discrete.R' [7s]
  Running 't-qmedist.R' [5s]
  Running 't-qqcomp.R' [4s]
  Running 't-qqcompcens.R' [4s]
  Running 't-quantiledist.R' [2s]
  Running 't-startfixarg-overall.R' [3s]
  Running 't-startingvalues.R' [2s]
  Running 't-util-npmle.R' [2s]
  Running 't-util-npsurv-mainfunction.R' [1s]
  Running 't-util-testdensity.R' [2s]
  Running 't-weird-ppcomp-cens.R' [2s]
  Running 't-weird-qqcomp-cens.R' [2s]
Running the tests in 'tests/t-CIcdfplot.R' failed.
Complete output:
  > library(fitdistrplus)
  Loading required package: MASS
  Loading required package: survival
  > 
  > nbboot <- 201
  > nbboot <- 10
  > ggplotEx <- requireNamespace("ggplot2", quietly = TRUE)
  > 
  > # (1) Fit of a gamma distribution 
  > #
  > 
  > set.seed(123)
  > s1 <- rgamma(20, 3, 2)
  > f1 <- fitdist(s1, "gamma")
  > b1 <- bootdist(f1, niter=nbboot, silent=TRUE)
  > 
  > plot(b1)
  > quantile(b1)
  (original) estimated quantiles for each specified probability (non-censored data)
               p=0.1    p=0.2     p=0.3    p=0.4   p=0.5    p=0.6    p=0.7
  estimate 0.4742226 0.672897 0.8486169 1.022154 1.20499 1.408707 1.650739
              p=0.8    p=0.9
  estimate 1.966843 2.466269
  Median of bootstrap estimates
               p=0.1     p=0.2     p=0.3    p=0.4    p=0.5    p=0.6    p=0.7
  estimate 0.5083059 0.6866909 0.8522568 1.018742 1.200786 1.384358 1.577513
              p=0.8    p=0.9
  estimate 1.797666 2.213844
  
  two-sided 95 % CI of each quantile
             p=0.1     p=0.2     p=0.3     p=0.4     p=0.5    p=0.6    p=0.7
  2.5 %  0.3930866 0.5545204 0.6866101 0.8156534 0.9504778 1.099636 1.275705
  97.5 % 0.7636623 0.9342928 1.0731198 1.2027355 1.3333688 1.479356 1.762055
            p=0.8    p=0.9
  2.5 %  1.498104 1.827576
  97.5 % 2.151721 2.778154
  > 
  > par(mfrow=c(1,2))
  > CIcdfplot(b1, CI.level=95/100, CI.output = "probability", CI.fill="grey80", CI.col="black")
  > CIcdfplot(b1, CI.level=95/100, CI.output = "quantile", datacol="blue")
  > if(ggplotEx) CIcdfplot(b1, CI.level=95/100, CI.output = "probability", CI.fill="grey80", CI.col="black", plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=95/100, CI.output = "quantile", datacol="blue", plotstyle = "ggplot")
  >   
  > par(mfrow=c(1,2))
  > CIcdfplot(b1, CI.level=85/100, CI.output = "probability")
  > CIcdfplot(b1, CI.level=85/100, CI.output = "quantile")
  > if(ggplotEx) CIcdfplot(b1, CI.level=85/100, CI.output = "probability", plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=85/100, CI.output = "quantile", plotstyle = "ggplot")
  > 
  > par(mfrow=c(1,2))
  > CIcdfplot(b1, CI.level=90/100, CI.output = "probability")
  > CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="black", CI.type = "less", 
  +           CI.fill="grey85", verticals=TRUE, datacol="blue", do.points=FALSE)
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "probability", plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="black", CI.type = "less", 
  +           CI.fill="grey85", verticals=TRUE, datacol="blue", do.points=FALSE, plotstyle = "ggplot")
  > 
  > par(mfrow=c(1,2))
  > CIcdfplot(b1, CI.level=90/100, CI.output = "probability", CI.type = "greater")
  > CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="black", CI.type = "greater", 
  +           CI.fill="grey90", datacol="blue", datapch=21)
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "probability", CI.type = "greater", plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="black", CI.type = "greater", 
  +           CI.fill="grey90", datacol="blue", datapch=21, plotstyle = "ggplot")
  > 
  > par(mfrow=c(1,1))
  > CIcdfplot(b1, CI.level=90/100, CI.output = "probability", CI.col="black", CI.type = "less", CI.fill="grey90")
  > CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="black", CI.type = "less", CI.fill="grey90", 
  +           verticals=TRUE, datacol="blue", do.points=FALSE)
  > CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="grey90", CI.type = "less", CI.fill="grey90", 
  +           verticals=TRUE, datacol="blue", do.points=FALSE, CI.only=TRUE)
  > CIcdfplot(b1, CI.level=90/100, CI.output = "probability", CI.col="grey85", CI.type = "less", CI.fill="grey90", 
  +           CI.only = TRUE)
  > CIcdfplot(b1, CI.output = "probability", fitlty=3, fitlwd=4)
  > 
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "probability", CI.col="black", CI.type = "less", CI.fill="grey90", plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="black", CI.type = "less", CI.fill="grey90", 
  +           verticals=TRUE, datacol="blue", do.points=FALSE, plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "quantile", CI.col="grey90", CI.type = "less", CI.fill="grey90", 
  +           verticals=TRUE, datacol="blue", do.points=FALSE, CI.only=TRUE, plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.level=90/100, CI.output = "probability", CI.col="grey85", CI.type = "less", CI.fill="grey90", 
  +           CI.only = TRUE, plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b1, CI.output = "probability", fitlty=3, fitlwd=4, plotstyle = "ggplot")
  > 
  > # (2) an example from ecotoxicology
  > # with censored data
  > #
  > data(salinity)
  > log10LC50 <-log10(salinity)
  > fln <- fitdistcens(log10LC50,"norm")
  > bln <- bootdistcens(fln, niter=nbboot)
  > (HC5ln <- quantile(bln,probs = 0.05))
  (original) estimated quantiles for each specified probability (censored data)
            p=0.05
  estimate 1.11584
  Median of bootstrap estimates
             p=0.05
  estimate 1.110188
  
  two-sided 95 % CI of each quantile
           p=0.05
  2.5 %  1.032713
  97.5 % 1.153806
  > CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
  +           xlab = "log10(LC50)",xlim=c(0.5,2),lines01 = TRUE)
  > if(ggplotEx) CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
  +           xlab = "log10(LC50)",xlim=c(0.5,2),lines01 = TRUE, plotstyle = "ggplot")
  > 
  > # zoom around the HC5 with CI on quantiles 
  > CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
  +           xlab = "log10(LC50)", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1))
  > abline(h = 0.05, lty = 1)
  > if(ggplotEx) CIcdfplot(bln, CI.output = "quantile", CI.fill = "lightblue", CI.col = "blue",
  +           xlab = "log10(LC50)", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1), plotstyle = "ggplot") + 
  +   ggplot2::geom_hline(yintercept = 0.05)
  > 
  > # zoom around the HC5 with CI on probabilities 
  > CIcdfplot(bln, CI.output = "probability", CI.fill = "lightblue", CI.col = "blue",
  +           xlab = "log10(LC50)", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1))
  > abline(h = 0.05, lty = 1)
  > if(ggplotEx) CIcdfplot(bln, CI.output = "probability", CI.fill = "lightblue", CI.col = "blue",
  +           xlab = "log10(LC50)", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1), plotstyle = "ggplot") + 
  +   ggplot2::geom_hline(yintercept = 0.05)
  > 
  >   
  > # (3) An example where the difference between "probability"
  > #     and "quantile" is clear on the plot
  > #
  > 
  > set.seed(123)
  > s3 <- rgamma(5, 3, 10)
  > f3 <- fitdist(s3, "norm")
  > b3 <- bootdist(f3, niter=nbboot, silent=TRUE)
  > 
  > par(mfrow=c(1,2))
  > CIcdfplot(b3, CI.level=90/100, CI.output = "probability")
  > CIcdfplot(b3, CI.level=90/100, CI.output = "quantile")
  > 
  > if(ggplotEx) CIcdfplot(b3, CI.level=90/100, CI.output = "probability", plotstyle = "ggplot")
  > if(ggplotEx) CIcdfplot(b3, CI.level=90/100, CI.output = "quantile", plotstyle = "ggplot")
  > 
  > #some ideas from http://edild.github.io/ssd/
  > 
  > proc.time()
     user  system elapsed 
    12.96    0.51   13.46