differing number of rows when running nls regression

I have some data (y=Response, x=Dose) that is distributed roughly as a
curve. I am trying to fit a non-linear regression model on the data
using negative logistic function (even if the data show a rise in the
final part of the curve that the logistic won't accommodate for).
I used a rough graphical approach to guess the starting values then
the package `nlstools` for a better overview of the data. However, I
get the error "differing number of rows".
If I run `nls` directly, I get the error" "Error in 1 + exp :
non-numeric argument to binary operator"
Why is that happening?
How can I set the model?
Is there a better curve function to fit the data?
Thank you

```
df = data.frame(Response =    c(890.72,    895.46,    895.63,
894.72,    895.49,    893.59,
                892.53,    895.06,    897.21,    889.27,    876.05,
857.96,    862.02,    858.36,
                890.94,    890.8,    887.22,    888.91,    890.83,
889.92,    891.76,    890.32,
                886.35,    878.11,    866.57,    859.04,    863.64,
880.16,    884.15,    879.57,
                878.89,    882.27,    881.59,    880.98,    881.45,
876.19,    868.32,    859.16,
                850.53,    853.21,    859.34,    859.73,    861.19),
                Dose =    c(0.0000000015,    0.000000003,
0.000000006,    0.000000012,
                       0.000000024,    0.000000048,    0.000000095,
0.00000018,
                       0.00000038,    0.00000078,    0.0000015,
0.000013,    0.000025,
                       0.00005,    0.0000000015,    0.000000003,
0.000000006,
                       0.000000012,    0.000000024,    0.000000048,
0.000000095,
                       0.00000018,    0.00000038,    0.00000078,
0.0000015,    0.000025,
                       0.00005,    0.0000000015,    0.000000003,
0.000000006,
                       0.000000012,    0.000000024,    0.000000048,
0.000000095,
                       0.00000018,    0.00000038,    0.00000078,
0.0000015,    0.000003,
                       0.000006,    0.000013,    0.000025,    0.00005)
)
plot(Response~log10(Dose), df)
abline(a=500, b=-60)
abline(v=log10(0.3e-6))
A = 0.3e-6     # plateau
B = -60        # slope
library(nlstools)   # NL regression
form = as.formula(Response ~ ( (exp(a+b*Dose)) / (1+exp-(a+b*Dose)) ) )
preview(form, data = x_25_2024, start=list(a=A, b=B))
nls(Response ~ ( (exp(a+b*Dose)) / (1+exp-(a+b*Dose)) ),
    data=df, start=list(a=A, b=B))
```