Increment function plot visualizes the influence of a selected interaction. This helps to better understand the influence of the interaction effects and statistics/physics in the problem. Contrary, the total increment shows the behaviour of the function, where all the other variables are held at their nominal value (nominal design). However, in contrasts to standard visualization, the increments are additive and therefore, one can easily point the direction of future development. For example, considering total increment dTF1.2 and dTF3.4, one can define the best direction for variables 1, 2, 3 and 4.

From the mathematical perspective, the total increment function is defined as a sum of all lower order increment functions. Variables, which are not part of the total order increment are set to their nominal value (nominal design). In order to explain it more clearly, let us consider the nominal sample to be X = 1, 2, 3 and function of interest is F(x1, x2, x3). We want to visualize total increment function dTF1.2(x1, x2) than the equation reads:

dTF1.2(x1, x2) = dF1.2(x1, x2) + dF1(x1) + dF2(x2)

where dF1.2(x1, x2) is the increment function for the interaction of variables x1 and x2, dF1(x1) is the increment function for variable x1 and dF2(x2) is the increment function for variable x2. A deeper explanation of the increment function is provided in the increment function plot.

## How to use it: #

*Fig. 1: Total increment function plot*

Select the total increment function you want to plot and algorithm automatically plots the selected total increment function. In order to see the names of variables used to construct the total increment function, one needs to put the mouse pointer over the button and description will pop up. The samples used to create the total increment function are not showed and only the nominal sample is displayed. This helps to orientate around the domain. The scale of the graph is automatically adjusted to the size of the given increment.

### Tick box: #

**Scale:**Adjust the range of increment abscissa (Y for 1-D and Z for 2-D) accordingly to the range of given problem. This feature is useful if one wants to compare the influence of total increment function.

### Buttons: #

**View Details of Points:**Display table which shows the nominal sample. The table contains coordinates of the nominal sample and with a double click, the sample is highlighted. Also, with double click the full coordinates of the sample are shown next to the label ’Selected Sample:’. One can easily copy coordinates if it is required.**Apply:**Changes on the graph will take effect after pressing the*Apply*button.

The graph is fully adjustable with options, where:

**Amount of Points for Axis:**Setting of coarseness of the plot**Total Points Rendered:**Plot density information**Plot title:**Title of the graph**Title font:**Font type and size of the plot title**X Range:**Restriction of range for X axis, toggle on/off**Y Range:**Restriction of range for Y axis, toggle on/off**Function Color:**Basic color of interpolating curve, toggle on/off**Points Color:**Color and size of plotted function points (nominal solution only), toggle on/off**Legend Font:**Font type and size of the legend, toggle on/off**Axis Font:**Font type and size of the plot axis**X Axis Label:**Label of X axis**Y Axis Label:**Label of Y axis**Z Axis Label:**Label of Z axis

NOTE: Each change takes effect after the *Apply* button is pushed.

*Fig. 2: Total increment plot – options*

### Print: #

To store selected results in File select save. It will allow you to browse in folders starting in the project folder. The code automatically selects the format to store visualized results.