A pre-loaded example dataset in R

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

head(DNase)
##   Run       conc density
## 1   1 0.04882812   0.017
## 2   1 0.04882812   0.018
## 3   1 0.19531250   0.121
## 4   1 0.19531250   0.124
## 5   1 0.39062500   0.206
## 6   1 0.39062500   0.215
require(stats); require(graphics)
coplot(density ~ conc | Run, data = DNase,
       show.given = FALSE, type = "b")

coplot(density ~ log(conc) | Run, data = DNase,
       show.given = FALSE, type = "b")

## fit a representative run
fm1 <- nls(density ~ SSlogis( log(conc), Asym, xmid, scal ),
    data = DNase, subset = Run == 1)
## compare with a four-parameter logistic
fm2 <- nls(density ~ SSfpl( log(conc), A, B, xmid, scal ),
    data = DNase, subset = Run == 1)
summary(fm2)
## 
## Formula: density ~ SSfpl(log(conc), A, B, xmid, scal)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## A    -0.007897   0.017200  -0.459    0.654    
## B     2.377239   0.109516  21.707 5.35e-11 ***
## xmid  1.507403   0.102080  14.767 4.65e-09 ***
## scal  1.062579   0.056996  18.643 3.16e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01981 on 12 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 2.517e-07
anova(fm1, fm2)
## Analysis of Variance Table
## 
## Model 1: density ~ SSlogis(log(conc), Asym, xmid, scal)
## Model 2: density ~ SSfpl(log(conc), A, B, xmid, scal)
##   Res.Df Res.Sum Sq Df     Sum Sq F value Pr(>F)
## 1     13  0.0047896                             
## 2     12  0.0047073  1 8.2314e-05  0.2098 0.6551