## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ----create_S------------------------------------------------------------
S <- c(0,1,2,5,8,12,30,50)

## ----create_v------------------------------------------------------------
v <- c(0,11.1,25.4,44.8,54.5,58.2,72.0,60.1)

## ----combine_data--------------------------------------------------------
# Combine data in a dataframe:
kinData <- data.frame(S,v)

# Print the data:
kinData

## ----first_plot----------------------------------------------------------
plot (S,v)

## ----theoretical_curve_formula-------------------------------------------
# "velocity = Vmax times S divided by (Km plus S)", stored in MMcurve
MMcurve <- formula(v ~ Vmax*S/(Km+S))

## ------------------------------------------------------------------------
bestfit <- nls(MMcurve, kinData, start=list(Vmax=50,Km=2))
# print the results
bestfit

## ----theoretical_curve_points--------------------------------------------
# Hypothetical oncentration ranges for S:
SconcRange <- seq(0,50,0.1)

# Calculate the predicted velocities using the predict() function:
theorLine <- predict(bestfit,list(S=SconcRange))


## ----replot_final--------------------------------------------------------
# Now plot the data, the best fit line, and put the best fit coefficients in the plot
plot (kinData, 
      xlab="Subtrate (mM)", 
      ylab="Velocity (nmol/s)", 
      title(main="Fitted MM data"), 
      pch=17, col="blue", cex=1.5)

# Now add the theoretical line with the lines() function:
lines(SconcRange,theorLine,col="red")

# Add text with the values:
text(28,30, "Vmax = ")
text(35,30,round(coef(bestfit)[1],2))
text(29,25, "Km = ")
text(36,25,round(coef(bestfit)[2],2))

## ----sessionInfo---------------------------------------------------------
sessionInfo()