Using ssd4mosaic's functions in R
In ssd4mosaic: Web Application for the SSD Module of the MOSAIC Platform

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE,
  fig.width = 5,
  fig.height = 4
)

library(ssd4mosaic)

When using the MOSAIC SSD web application, a code is provided after each analysis to reproduce the same results directly in R. Here is an example of censored data species sensitivity distribution analysis using {ssd4mosaic} functions.

Defining the inputs

# 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"

Fitting to the data

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

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

Extracting information from the fit

## Model parameters
lapply(fits, summary)

## HCx values
lapply(bts, quantile, probs = c(0.05, 0.1, 0.2, 0.5))

## 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)