Eduard Szöcs

Data in Environmental Science and Eco(toxico-)logy

Quantitative Ecotoxicology, Page 147, Example 4.3, LC50

This is about example 4.3 on page 147 of Quantitative Ecotoxicology. This example is about Calculations of $LC_{50}$ values. In this post I won’t reproduce the SAS-Code since I do not have any experience with SAS PROC PROBIT and I do not fully understand whats happening there.

Instead I will fit Dose-Response-Models using the drc-package to this data.

Get the data from here and read it into R:

require(RCurl)
url <- getURL("https://raw.github.com/EDiLD/r-ed/master/quantitative_ecotoxicology/data/p147.csv",
ssl.verifypeer = FALSE, .opts=curlOptions(followlocation=TRUE))
salt <- read.table(text = url, header = TRUE, sep = ";")
head(salt)
##   DEAD TOTAL CONC
## 1   16    76 10.3
## 2   22    79 10.8
## 3   40    77 11.6
## 4   69    76 13.2
## 5   78    78 15.8
## 6   77    77 20.1

So the data consists of number of dead animals (DEAD) from all animals (TOTAL) exposed to a concentration (CONC). First we create a new column with the proportion of dead animals:

salt$prop <- salt$DEAD / salt$TOTAL

Lets have a look at the raw data (note that I use a logarithmic scale for the x-axis):

plot(salt$CONC, salt$prop, xlab = 'Concentration', ylab = 'Proportion dead', log='x')

plot of chunk p147_plot_raw

I will use the drc-package of Christian Ritz and Jens Strebig to fit dose-response-curves to this data. The main function of this package is drm:

Here I fit a two-parameter log-logistic model to the data (see Ritz (2010) for a review of dose-response-models used in ecotoxicology):

require(drc)
mod <- drm(DEAD/TOTAL ~ CONC, weights = TOTAL, data = salt, fct =  LL.2(), type = 'binomial')

So the usage is similar to lm() or nls() with some addtions: The fct argument defines the model that is fitted to the data here a 2-parameter log-logistic. weights specifies the sample size for each group and type = 'binomial' specifies that we have a binomial response (default is continuous for normally distributed errors).

We can compare this model with other models using the AIC (the smaller the better).

Here I compare the 2-parameter log-logistic model with a two-parameter Weibull and a 2-parameter Gompertz model.

drc has for this purpose the mselect() function that shows some diagnostics for every model:

  • likelihood (the higher the better; however use this only when your models are nested)
  • AIC (the lower the better)
  • residual variance (the lower the better)
mselect(mod, fctList=list(W1.2(),G.2()))
##       logLik     IC Lack of fit
## LL.2 -10.045 24.091     0.72632
## G.2  -11.340 26.681     0.35608
## W1.2 -12.780 29.561     0.15027

The LL.2-model has the lowest AIC so I will keep this. Lets see how the model looks like:

# raw data
plot(prop ~ CONC, data = salt, xlim = c(9,21), ylim = c(0,1), ylab = 'Proportion dead', xlab = 'Concentration')
 
conc_pred <- seq(9, 21, 0.1)
lines(conc_pred, predict(mod, newdata = data.frame(CONC = conc_pred)))

plot of chunk p147_plot_mod

We can get the $LC_{50}$ with confidence interval from the model using the ED() function:

ED(mod, 50, interval = 'delta')
## 
## Estimated effective doses
## (Delta method-based confidence interval(s))
## 
##      Estimate Std. Error  Lower Upper
## 1:50   11.430      0.103 11.227  11.6

Code and data is available on my github-repo under file name ‘p147’.

References

[1] C. Ritz. “Toward a unified approach to dose-response modeling in ecotoxicology”. In: Environmental Toxicology and Chemistry 29.1 (Jan. 2010), pp. 220-229. DOI: 10.1002/etc.7. <URL: http://dx.doi.org/10.1002/etc.7>.

Written on February 25, 2013