Ratio of polynomials approximation - r

Question

I am trying to fit a polynomial to my dataset, which looks like that (full dataset is at the end of the post):
The theory predicts that the formulation of the curve is:
which looks like this (for x between 0 and 1):
When I try to make a linear model in R by doing:
mod <- lm(y ~ poly(x, 2, raw=TRUE)/poly(x, 2))
I get the following curve:
Which is much different from what I would expect. Have you got any idea how to fit a new curve from this data so that it would be similar to the one, which theory predicts? Also, it should have only one minimum.
Full dataset:
Vector of x values:
x <- c(0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12,
0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25,
0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38,
0.39, 0.40, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.50, 0.51,
0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.60, 0.61, 0.62, 0.63, 0.64,
0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77,
0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90,
0.91, 0.92, 0.93, 0.94, 0.95)
Vector of y values:
y <- c(4.104, 4.444, 4.432, 4.334, 4.285, 4.058, 3.901, 4.382,
4.258, 4.158, 3.688, 3.826, 3.724, 3.867, 3.811, 3.550, 3.736, 3.591,
3.566, 3.566, 3.518, 3.581, 3.505, 3.454, 3.529, 3.444, 3.501, 3.493,
3.362, 3.504, 3.365, 3.348, 3.371, 3.389, 3.506, 3.310, 3.578, 3.497,
3.302, 3.530, 3.593, 3.630, 3.420, 3.467, 3.656, 3.644, 3.715, 3.698,
3.807, 3.836, 3.826, 4.017, 3.942, 4.208, 3.959, 3.856, 4.157, 4.312,
4.349, 4.286, 4.483, 4.599, 4.395, 4.811, 4.887, 4.885, 5.286, 5.422,
5.527, 5.467, 5.749, 5.980, 6.242, 6.314, 6.587, 6.790, 7.183, 7.450,
7.487, 8.566, 7.946, 9.078, 9.308, 10.267, 10.738, 11.922, 12.178, 13.243,
15.627, 16.308, 19.246, 22.022, 25.223, 29.752)

Answer 1

Use nls to fit a nonlinear model. Note that the model formula is not uniquely defined as displayed in the question since if we multiply all the coefficients by any number the result will still give the same predictions. To avoid this we need to fix one coefficient. A first try used the coefficients shown in the question as starting values (except fixing one) but that failed so dropping C was tried and the resulting coefficients fed into a second fit with C = 1.
st <- list(a = 43, b = -14, c = 25, B = 18)
fm <- nls(y ~ (a + b * x + c * x^2) / (9 + B * x), start = st)
fm2 <- nls(y ~ (a + b * x + c * x^2) / (9 + B * x + C * x^2), start = c(coef(fm), C = 1))
plot(y ~ x)
lines(fitted(fm2) ~ x, col = "red")
(continued after chart)
Note: Here is an example of using nls2 to get starting values with random search. We assume that the coefficients each lie between -50 and 50.
library(nls2)
set.seed(123) # for reproducibility
v <- c(a = 50, b = 50, c = 50, B = 50, C = 50)
st0 <- as.data.frame(rbind(-v, v))
fm0 <- nls2(y ~ (a + b * x + c * x^2) / (9 + B * x + C * x^2), start = st0,
alg = "random", control = list(maxiter = 1000))
fm3 <- nls(y ~ (a + b * x + c * x^2) / (9 + B * x + C * x^2), st = coef(fm0))

Answer 2

Since you already have a theoretic prediction, you don't seem in need of a new model, and it's really only a plotting task:
png(); plot(y~x)
lines(x,mod,col="blue")
dev.off()
You cannot expect lm to produce a good approximation to a non-linear problem. The denominator involving x in that theoretic expression makes this inherently nonlinear.