I have a question, which is probably more of a statistical query than a question directly related to r, however, maybe I'm just calling package r incorrectly, so I will post this question here. I have the following dataset:
x<-c(1e-08, 1.1e-08, 1.2e-08, 1.3e-08, 1.4e-08, 1.6e-08, 1.7e-08, 1.9e-08, 2.1e-08, 2.3e-08, 2.6e-08, 2.8e-08, 3.1e-08, 3.5e-08, 4.2e-08, 4.7e-08, 5.2e-08, 5.8e-08, 6.4e-08, 7.1e-08, 7.9e-08, 8.8e-08, 9.8e-08, 1.1e-07, 1.23e-07, 1.38e-07, 1.55e-07, 1.76e-07, 1.98e-07, 2.26e-07, 2.58e-07, 2.95e-07, 3.25e-07, 3.75e-07, 4.25e-07, 4.75e-07, 5.4e-07, 6.15e-07, 6.75e-07, 7.5e-07, 9e-07, 1.15e-06, 1.45e-06, 1.8e-06, 2.25e-06, 2.75e-06, 3.25e-06, 3.75e-06, 4.5e-06, 5.75e-06, 7e-06, 8e-06, 9.25e-06, 1.125e-05, 1.375e-05, 1.625e-05, 1.875e-05, 2.25e-05, 2.75e-05, 3.1e-05) y2<-c(-0.169718017273307, 7.28508517630734, 71.6802510299446, 164.637259265704, 322.02901173786, 522.719633360006, 631.977073772459, 792.321270345847, 971.810607095548, 1132.27551798986, 1321.01923840546, 1445.33152600664, 1568.14204073109, 1724.30089942149, 1866.79717333592, 1960.12465709003, 2028.46548012508, 2103.16027631327, 2184.10965255236, 2297.53360080873, 2406.98288043262, 2502.95194879366, 2565.31085776325, 2542.7485752473, 2499.42610084412, 2257.31567571328, 2150.92120390084, 1998.13356362596, 1990.25434682546, 2101.21333152526, 2211.08405955931, 1335.27559108724, 381.326449703455, 430.9020598199, 291.370887491989, 219.580548355043, 238.708972427248, 175.583544448326, 106.057481792519, 59.8876372379487, 26.965143266819, 10.2965349811467, 5.07812046132922, 3.19125838983254, 0.788251933518549, 1.67980552001939, 1.97695007279929, 0.770663673279958, 0.209216903989619, 0.0117903221723813, 0.000974437796492681, 0.000668823762763647, 0.000545308757270207, 0.000490042305650751, 0.000468780182460397, 0.000322977916070751, 0.000195423690538495, 0.000175847622407421, 0.000135771259866332, 9.15607623591363e-05)
which, when the plot looks like this: 
Then I tried to use the segmentation package to generate three linear regressions (solid black line) in three areas (10 ^ ⁻8--10 ^ ⁻7.10 ^ ⁻7--10 ^ ⁻6 and> 10 ^ -6), since I have a theoretical basis for finding different relationships in these different regions. However, it is obvious that my attempt to use the following code failed:
lin.mod <- lm(y2~x) segmented.mod <- segmented(lin.mod, seg.Z = ~x, psi=c(0.0000001,0.000001))
So my first question is, are there any other segmentation options that I can change other than breakpoints? As far as I understand, here the iterations are set to the maximum default values.
My second question is: can I try segmentation using the nls package? It seems that the first two areas on the graph (10 ^ --8--10 ^ ⁻7 and 10 ^ -7--10 ^ -6) are further from the linear than the last section, so perhaps the polynomial function will be better here ?
As an example of a result that I find acceptable, I described the original plot by hand: 
.
Edit: the reason for using linear fittings is the simplicity that they provide, in my inexperienced view, for regressing a dataset as a single unit, a rather complicated nonlinear function is required. One thought that came to my mind was to fit the lognormal model to the data, as this might work, given the skew axis X of the log. I do not have enough competence in R to do this, however, my knowledge extends only to fitdistr, which, as I understand it, will not work here.
Any help or guidance in the relevant direction will be most appreciated.