ZunZunNG - Reciprocal Plant Disease Exponential Model Fitting Interface

Introduction

Please select an option from the pull-down menu above.

Text data editor

Cut-and-paste text or edit data here

Convert commas to spaces (1,2,0,3 yields 1 2 0 3)

Use comma as decimal separator (1,203 = 1.203)

Remove commas from text (1,2,0,3 yields 1203)

Standard Unweighted Fitting: no weights required

Weighted Fitting: each data point requires a weight

This data is provided as an example, cut and paste as needed to model your data. All lines of text that do not begin with a number are ignored. Weighted fitting requires an additional number to be used as a weight when fitting. The site does not calculate any weights, which are used as: error = weight * (predicted - actual) You must provide any weights you wish to use. 0.001 0.001 20 0.0025 40 0.003 60 0.0045 80 0.006 100 0.008

Fitting Target

Here you can select the desired fitting target.

Data labels for graphs

Here you can enter the X and Y data labels to be printed on the report graphs.

Label for first independent data (X)

Label for dependent data (Y)

Graph scaling and lin/log control

Here you can choose the scaling of the data plots. Log plots require data values greater than zero.

Graph size

Here you can choose the size of the graphs. Smaller size files load quickly, with less resolution while large files show more resolution but load more slowly.

Scientific Notation

Here you can control scientific notation on the data plots.

X Scientific Notation

On

Off

Auto

Y Scientific Notation

On

Off

Auto

Hold Coefficients Constant

Here you can enter values to hold coefficients constant during fitting.
Leave the entry field blank to let the coefficient fit normally.

Note that coefficient bounds are overridden by holding a coefficient constant.

Incidence = y0 * exp(r * time)
Incidence = 1.0 / Incidence

Coefficient	Value
y0
r

Estimated Initial Coefficients

Here you can enter values for initial parameter estimates.

Few users will need this section, as the site estimates initial parameters using the Differential Evolution genetic algorithm for both non-linear equations and non-Gaussian fitting targets.

Incidence = y0 * exp(r * time)
Incidence = 1.0 / Incidence

Coefficient	Value
y0
r

Coefficient Bounds

Here you can enter coefficient upper and lower bounds. Leave the entry fields blank to let the coefficients fit without bounds.

Note that holding a coefficient constant overrides these bounds.

Incidence = y0 * exp(r * time)
Incidence = 1.0 / Incidence

Coefficient	Upper Bound	Lower Bound
y0
r

3D Data Point Size Options

Here you can select the data point size for 3D surface plots and 3D scatter plots.

Introduction

Text data editor

Fitting Target

Data labels for graphs

Graph scaling and lin/log control

Graph size

Scientific Notation

Hold Coefficients Constant

Estimated Initial Coefficients

Coefficient Bounds

3D Data Point Size Options

3D Point Size