# (1) load of data
#
data(salinity)
# (2) plot of data using Turnbull cdf plot
#
log10LC50 <- log10(salinity)
plotdistcens(log10LC50)
# (3) fit of a normal and a logistic distribution to data in log10
# (classical distributions used for species sensitivity
# distributions, SSD, in ecotoxicology))
# and visual comparison of the fits using Turnbull cdf plot
#
fln <- fitdistcens(log10LC50, "norm")
summary(fln)
fll <- fitdistcens(log10LC50, "logis")
summary(fll)
cdfcompcens(list(fln, fll),legendtext = c("normal", "logistic"),
xlab = "log10(LC50)", xlim = c(0.5, 2), lines01 = TRUE)
# (4) estimation of the 5 percent quantile value of
# the normal fitted distribution (5 percent hazardous concentration : HC5)
# with its two-sided 95 percent confidence interval calculated by
# non parametric bootstrap
# from a small number of bootstrap iterations to satisfy CRAN running times constraint.
# For practical applications, we recommend to use at least niter=501 or niter=1001.
#
# in log10(LC50)
bln <- bootdistcens(fln, niter = 101)
HC5ln <- quantile(bln, probs = 0.05)
# in LC50
10^(HC5ln$quantiles)
10^(HC5ln$quantCI)
# (5) estimation of the HC5 value
# with its one-sided 95 percent confidence interval (type "greater")
#
# in log10(LC50)
HC5lnb <- quantile(bln, probs = 0.05, CI.type = "greater")
# in LC50
10^(HC5lnb$quantiles)
10^(HC5lnb$quantCI)
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