Density Plot

The view can be accessed either via the “Open view” action on the executed node or on KNIME Hub.
In the node configuration, you can enable certain controls, which are then available in the view. This includes the ability to choose a dimension column and a condition column or the possibility to set a title.
The configuration also offers a preview of the view, which should help to get the density plot in the desired shape quickly.

Missing as well as NaN (not a number) values in the dimension column are ignored. If a condition column is chosen, a category is ignored when all corresponding cells in the dimension column are ignored. The density is computed via kernel density estimation with a gaussian kernel. The bandwidth for the kernel can either be set to a specific value or computed with Scott's rule of thumb (see R's bw.nrd).

Options

Dimension column

Select the column with numerical values for which you wish to plot a density. Note: Missing and NaN (not a number) values will be ignored.

Condition column

Select the column with respect to which you want to plot conditional densities. The values of the selected column are treated as categories. Note: Also missing values are treated as a separate category.

Title

The title of the graph shown above the generated image. If left blank, no title will be shown.

Kernel bandwidth

The bandwidth of the gaussian kernel used for the density estimation can be configured here. If “Rule of thumb” is selected, the bandwidth is computed with bw = 1.06 * min(std, IQR/1.34) * n^(-1/5), where std is the sample standard deviation, IQR is the interquartile range and n is the number of observations (see R's bw.nrd). Alternatively, it is possible to set a fixed kernel bandwidth.

Adjustment factor

The kernel bandwidth computed by the rule of thumb is multiplied by this adjustment factor. Setting the adjustment factor is only possible if the kernel bandwidth is selected as “Rule of thumb”. The larger this value is the smoother the curve in the plot.

Custom bandwidth

Choose the bandwidth of the gaussian kernel. Setting the custom bandwidth is only possible if the kernel bandwidth is selected as “Fixed”. The larger this value is the smoother the curve in the plot.

Dimension axis limits

Choose if the dimension axis range should be calculated automatically, taken from the domain range or set manually. If “Automatic” is selected the axis ranges from (min - 3 * bw) to (max + 3 * bw) where min and max are the minimum and maximum values of the data and bw is the selected bandwidth. If you select “Domain bounds”, you might need to use a Domain calculator or Edit Numeric Domain node before this node.

Horizontal min

Value that sets the horizontal axis minimum.

Horizontal max

Value that sets the horizontal axis maximum.

Custom dimension axis label

The label used for the horizontal dimension axis. If the checkbox is deactivated, the label will correspond to the selected dimension. If activated and left blank, no label will be shown.

Density axis label

The label used for the vertical value axis. If left blank, no label will be shown.

Line thickness

Numeric value that defines the line size.

Fill area

When checked, the area under the curve is filled. This setting is only available when there is no condition column selected.

Display legend

When checked, the chart’s legend containing the categories of the condition column is displayed.

Enable image download

When checked, an image of the chart can be downloaded by clicking the image download button in the view.

Show tooltip

When checked, hovering over a box will show information about the data.

Enable animation

When checked, the animation of the graph is active.

Generate image

If checked, adds an image output port and, upon execution, generates a PNG image which is made available via this port.

Width

The width of the generated image (in pixels).

Height

The height of the generated image (in pixels).

Views

Density Plot

A density plot visualization of the input data.

Workflows

• 01. Descriptive StatisticsKNIME Hub
• 04_Data_Preprocessing_for_ML_ModelsKNIME Hub
• 04_Evaluating_Regression_Model_PerformanceKNIME Hub
• 07_Example_for_Density_PlotKNIME Hub
• 07_Four_Techniques_Outlier_DetectionKNIME Hub
• Show all 34 workflows

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