WOLFRAM NOTEBOOK

WOLFRAM|DEMONSTRATIONS PROJECT

Choosing Initial Parameter Values for Nonlinear Regression

​
dataset
1
2
3
4
5
6
7
8
method
Automatic
accuracy
4
fit and plot
reset to default parameters
set initial to fitted parameters
precision
4
initial parameter values
y
0
150.
treat
y
0
as a
constant
fitted parameter
t
c1
42.
m
1
2.4
t
c2
44.
m
2
3.35
Automatic methodfitted parameters:
y
0
=
t
t
1
=
m
1
=
t
t
2
=
m
2
=
Fitting scattered experimental data with a multi-parameter model by nonlinear regression is frequently hampered by the difficulty in making sufficiently good guesses of the parameters' initial values. This Demonstration lets you plot the points from one of eight datasets and then use the model to generate a curve that approximately matches the data by manually adjusting the parameters’ values with sliders. The values that produce the visually matched curve are then used as the initial guesses to the regression procedure. The method is demonstrated with different scattered peaked datasets to be fitted with a double-stretched exponential model having four or five adjustable parameters. Hints: Start by matching
y
0
first. Try the Gradient method if Automatic frequently fails.​
Wolfram Cloud

You are using a browser not supported by the Wolfram Cloud

Supported browsers include recent versions of Chrome, Edge, Firefox and Safari.


I understand and wish to continue anyway »

You are using a browser not supported by the Wolfram Cloud. Supported browsers include recent versions of Chrome, Edge, Firefox and Safari.