I have a data table with a column as concentration, and two groups of treatments, each in two replicates.

I used GraphPad Prism to calculate the nonlinear regression (curve fit) parameter with the below criteria:

- model: sigmoidal 4pl, x is the concentration model (standard curve to interpolate) and check the interpolate box
- method: fitting method as least squares
- compare: no comparisons
- constrain: Bottom = must be greater than -1.1; top= must be less than 2; IC50= no constrain; Hilslop= no constrain.
- confidence level= 95%
- Diagnostic: R square, Sy.x and the sum of squares for quantifying the goodness of fit.
- the rest criteria are default.

then I would have an output as "Table os results " including (best-fit values (including Bottom, Top, IC50, Hillsplop, logIC50, Span), 95% CI (including Bottom, Top, IC50, Hillsplop, logIC50), the goodness of fit (including Degrees of freedom, R squared, sum of squares, Sy.x) ), and one as "Interpolated X mean values" for each treatment.

But I would like to know how can I have the same results in R?

Also, the GraphPad link is, showing how to define the model: `https://www.graphpad.com/guides/prism/latest/curve-fitting/reg_dr_stim_variable_2.htm`

Input:

```
df <- data.frame(
Concentration = rep(c(1, 2, 3, 4), each = 2),
Treatment = rep(c("Group1", "Group2"), each = 4),
Replicate = rep(1:2, times = 4),
Value = rnorm(8, mean = 10, sd = 2) )
```