Using ssd4mosaic’s functions in R

# Data creation
# Most often, you would archive the same result by reading a table file with a
# function akin to utils::read.delim()
data <- ssd4mosaic::fluazinam

# Which distribution to fit to the data.
# See get_fits function documentation for possible options
distributions <- list("lnorm")
# Whether to display the results plots with a logscale x-axis
logscale <- TRUE
# Concentration unit for plots labels
unit <- "\u03bcg/L"

## model fitting
fits <- ssd4mosaic::get_fits(data, distributions, TRUE)

## bootstrapping
bts <- ssd4mosaic::get_bootstrap(fits)[[1]]

## Model parameters
lapply(fits, summary)
#> [[1]]
#> Fitting of the distribution ' lnorm ' By maximum likelihood on censored data 
#> Parameters
#>         estimate Std. Error
#> meanlog 4.976920  0.7422075
#> sdlog   2.687785  0.6056713
#> Loglikelihood:  -72.81266   AIC:  149.6253   BIC:  150.9034 
#> Correlation matrix:
#>           meanlog     sdlog
#> meanlog 1.0000000 0.1350239
#> sdlog   0.1350239 1.0000000

## HCx values
lapply(bts, quantile, probs = c(0.05, 0.1, 0.2, 0.5))
#> [[1]]
#> (original) estimated quantiles for each specified probability (censored data)
#>            p=0.05    p=0.1    p=0.2    p=0.5
#> estimate 1.743522 4.629205 15.10194 145.0271
#> Median of bootstrap estimates
#>           p=0.05    p=0.1    p=0.2    p=0.5
#> estimate 1.95653 5.119783 16.38549 147.8894
#> 
#> two-sided 95 % CI of each quantile
#>            p=0.05     p=0.1     p=0.2     p=0.5
#> 2.5 %   0.3239024  1.074277  4.054411  36.17055
#> 97.5 % 17.5121757 33.826698 83.260100 784.80803

## CDF plot with confidence intervals
p <- ssd4mosaic::base_cdf(fits, unit = unit, logscale = logscale)
ssd4mosaic::add_CI_plot(p, bts, logscale)
## CDF plot with species names
ssd4mosaic::options_plot(fits, unit, logscale, data, use_names = TRUE)
## CDF plot colored by group
ssd4mosaic::options_plot(fits, unit, logscale, data, use_groups = TRUE)