if( !require('drc') ){
    install.packages('drc', dependencies=TRUE, repos='http://cran.rstudio.com/')
}
library(drc) # get the dataset G.aparine

if( !require('nlme') ){
    install.packages('nlme', dependencies=TRUE, repos='http://cran.rstudio.com/')
}
library(nlme)

source('../Chapter5/SSlogLogistic.R')

# Avoid log(0) when dose=0:
#
mask = G.aparine$dose == 0
G.aparine$dose[mask] = G.aparine$dose[mask] + 1.e-3

# Fit a model to all the data:
#
G.aparine.m0 = nls(drymatter ~ SSlogLogistic(dose, b, c, d, e), data=G.aparine)
print(summary(G.aparine.m0))

# Fit each model separately:
#
G.aparine.m1 = nlsList(drymatter ~ SSlogLogistic(dose, b, c, d, e) | treatment, data=G.aparine)
print(summary(G.aparine.m1))

nd = data.frame(dose=500, treatment=1)
print(sprintf('(dose= %.2f; treatment= %d): prediction(group independent)= %f', nd$dose, nd$treatment, predict(G.aparine.m0, newdata=nd)))
print(sprintf('(dose= %.2f; treatment= %d): prediction(group dependent)= %f', nd$dose, nd$treatment, predict(G.aparine.m1, newdata=nd)))
nd = data.frame(dose=500, treatment=2)
print(sprintf('(dose= %.2f; treatment= %d): prediction(group independent)= %f', nd$dose, nd$treatment, predict(G.aparine.m0, newdata=nd)))
print(sprintf('(dose= %.2f; treatment= %d): prediction(group dependent)= %f', nd$dose, nd$treatment, predict(G.aparine.m1, newdata=nd)))