Non-linear Models

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This webpage performs non-linear model regression.
It extends John Pezzullo's http://statpages.info/nonlin.html to handle more than 8 parameters. In addition you can create plots for 1-Dimensional models with prediction confidence bands.
For more information take a thorough look at John's excelent page.
June 2020.

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DATA

• N-row × N-col matrix
1. First row must contain column names (unquoted).
2. Column values separated by comma or tab
3. Cases separated by new line
4. Use NA for missing value
• Entry
  1. Manual entry
  2. Copy/paste (eg. from spreadsheet)
  3. Load CSV file

Data re-formating

  Specifications:  

Dependent variable ~ model List parameter(s): param=value, ... The regression equation must contain the '~' (tilde symbol) separating the dependent variable from the model, eg.         y ~ (a - c) * exp(-b * x) + c where x is the independent variable and a, b and c is parameters. All parameters must have initial values. To transform a variable or a parameter just wrap it in a function call, eg. log(x).
ℹ
Model
Param
Iterations				min: 50, max: 250
Confidence level		%
In some cases the iteration process may fail to converge. Then try: change the initial value(s) of the parameter(s) increase the number of iterations plot your data and reconsider the model
Include plot		Depicts the original data and predicted values with conf. bands
Plotting requires a 1-Dimensional model, ie. one independent variable only. Change 'None' below to the name of the independent variable
Independent variable name
The confidence interval may be calculated using either the Delta method (default) or the Bootstrapped method.
Method				Delta Bootstrap
Number of boot samples
Increasing the number of bootstrap samples results in more precise CI estimation but increase the risk of non-converging samples and the calculation may take considerable more time.

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