if( !require('investr') ){
    install.packages('investr', dependencies=TRUE, repos='http://cran.rstudio.com/')
}
library(investr) # has the function plotFit

load(file='../../BookCode/nlrwr/data/L.minor.rda')

# Plot the data to get an idea of what it looks like:
#
plot(L.minor$conc, L.minor$rate, xlab='conc', ylab='rate')
grid()


# Build a self-starter function for this model:
#
MMentenModel = function(predictor, K, Vm){
    Vm * predictor / (K + predictor)
}

MMentenModelInit = function(mCall, LHS, data){
    xy = sortedXyData(mCall[['predictor']], LHS, data)
    oneOverX = 1/xy[,'x']
    oneOverY = 1/xy[,'y']
    lmFit = lm(oneOverY ~ oneOverX)
    coefs = coef(lmFit)
    Vm = 1/coefs[1]
    K = coefs[2] * Vm
    value = c(K, Vm)
    names(value) = mCall[c('K','Vm')]
    value
}

MMentenModelSS = selfStart(MMentenModel, MMentenModelInit, c('K', 'Vm'))

# Fit our model to the given data:
#
L.minor.m1 = nls(rate ~ MMentenModelSS(conc, K, Vm), data=L.minor, trace=FALSE)

plotFit(L.minor.m1, main='L.minor data and fit')
grid()

print(summary(L.minor.m1))