How do I fit a custom distribution?
The user can also specify a custom distribution (called say dist) provided the following functions are defined:
- probability density (
ddist(x, par1, par2, log = FALSE)) - cumulative distribution (
pdist(q, par1, par2, lower.tail = TRUE, log.p = FALSE)) - inverse cumulative distribution (
qdist(p, par1, par2, lower.tail = TRUE, log.p = FALSE)) - random samples (
rdist(n, par1, par2)) - starting values (
sdist(x))
How do I fit distributions to multiple groups such taxa and/or chemicals?
An elegant approach using some tidyverse packages is demonstrated below.
library(purrr) library(tidyr) library(dplyr) boron_preds <- nest(ssdtools::boron_data, data = c(Chemical, Species, Conc, Units)) %>% mutate( Fit = map(data, ssd_fit_dists, dists = "lnorm"), Prediction = map(Fit, predict) ) %>% unnest(Prediction)
The resultant data and predictions can then be plotted as follows.
ssd_plot(boron_data, boron_preds, xlab = "Concentration (mg/L)", ci = FALSE) + facet_wrap(~Group)
How do I plot a Cullen Frey plot?
The data can be visualized using a Cullen Frey plot of the skewness and kurtosis.
set.seed(10) ssd_plot_cf(boron_data)
How do I plot model diagnostics?
A fitdists object can be plotted to display model diagnostics plots for each fit.
plot(boron_dists)
