# Example: Nonlinear Regression
[
x y
0.5 1.255
0.387 1.25
0.24 1.189
0.136 1.124
0.04 0.783
0.011 0.402
]
# Nonlinear regression model
nlinfit y = a * x / (b + x)
# Initial guess of the
# regression model variables
m(a)= 2
m(b)= 1
y = a*x/(b+x)
| Variable |
Initial guess |
Value |
95% confidence |
| a |
2 |
1.3275313 |
0.02699022 |
| b |
1 |
0.02646154 |
0.00285336 |
| R^2 |
R^2adj |
Rmsd |
Variance |
| 0.9988019 |
0.9985024 |
0.004406 |
0.0001747 |
Nonlinear Regression Plot

Nonlinear Regression Residuals Plot

Source data points and calculated data points
| |
x |
y |
y calc |
Delta y |
| 1 |
0.5 |
1.255 |
1.2608056 |
-0.00580558 |
| 2 |
0.387 |
1.25 |
1.2425693 |
0.00743072 |
| 3 |
0.24 |
1.189 |
1.1956979 |
-0.00669793 |
| 4 |
0.136 |
1.124 |
1.1113046 |
0.0126954 |
| 5 |
0.04 |
0.783 |
0.79897717 |
-0.01597717 |
| 6 |
0.011 |
0.402 |
0.38980899 |
0.01219101 |
General
| Algorithm |
LM |
| Sample size |
6 |
| Model vars |
2 |
| Indep vars |
1 |
| Iterations |
9 |
| Max iterations |
64 |