A pre-loaded example dataset in R

Main page: https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/Puromycin

Puromycin
##    conc rate     state
## 1  0.02   76   treated
## 2  0.02   47   treated
## 3  0.06   97   treated
## 4  0.06  107   treated
## 5  0.11  123   treated
## 6  0.11  139   treated
## 7  0.22  159   treated
## 8  0.22  152   treated
## 9  0.56  191   treated
## 10 0.56  201   treated
## 11 1.10  207   treated
## 12 1.10  200   treated
## 13 0.02   67 untreated
## 14 0.02   51 untreated
## 15 0.06   84 untreated
## 16 0.06   86 untreated
## 17 0.11   98 untreated
## 18 0.11  115 untreated
## 19 0.22  131 untreated
## 20 0.22  124 untreated
## 21 0.56  144 untreated
## 22 0.56  158 untreated
## 23 1.10  160 untreated
require(stats); require(graphics)
plot(rate ~ conc, data = Puromycin, las = 1,
     xlab = "Substrate concentration (ppm)",
     ylab = "Reaction velocity (counts/min/min)",
     pch = as.integer(Puromycin$state),
     col = as.integer(Puromycin$state),
     main = "Puromycin data and fitted Michaelis-Menten curves")
## simplest form of fitting the Michaelis-Menten model to these data
fm1 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
           subset = state == "treated",
           start = c(Vm = 200, K = 0.05))
fm2 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
           subset = state == "untreated",
           start = c(Vm = 160, K = 0.05))
summary(fm1)
## 
## Formula: rate ~ Vm * conc/(K + conc)
## 
## Parameters:
##     Estimate Std. Error t value Pr(>|t|)    
## Vm 2.127e+02  6.947e+00  30.615 3.24e-11 ***
## K  6.412e-02  8.281e-03   7.743 1.57e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.93 on 10 degrees of freedom
## 
## Number of iterations to convergence: 5 
## Achieved convergence tolerance: 8.824e-06
summary(fm2)
## 
## Formula: rate ~ Vm * conc/(K + conc)
## 
## Parameters:
##     Estimate Std. Error t value Pr(>|t|)    
## Vm 1.603e+02  6.480e+00  24.734 1.38e-09 ***
## K  4.771e-02  7.782e-03   6.131 0.000173 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.773 on 9 degrees of freedom
## 
## Number of iterations to convergence: 5 
## Achieved convergence tolerance: 4.473e-06
## add fitted lines to the plot
conc <- seq(0, 1.2, length.out = 101)
lines(conc, predict(fm1, list(conc = conc)), lty = 1, col = 1)
lines(conc, predict(fm2, list(conc = conc)), lty = 2, col = 2)
legend(0.8, 120, levels(Puromycin$state),
       col = 1:2, lty = 1:2, pch = 1:2)

## using partial linearity
fm3 <- nls(rate ~ conc/(K + conc), data = Puromycin,
           subset = state == "treated", start = c(K = 0.05),
           algorithm = "plinear")