KernelDensityEstimation
Compute a kernel density estimation (KDE) from a table of values.
Syntax
model.result().dataset().create(<dtag>,"KernelDensityEstimation");
model.result().dataset(<dtag>).set(property,<value>);
model.result().dataset(<dtag>).setIndex(property,<value>,<index>);
Description
model.result().dataset().create(<dtag>,"KernelDensityEstimation"); creates a kernel density evaluation dataset.
Kernel density estimation datasets take a table and compute the kernel density estimation (KDE), which is a nonparametric way to estimate the probability density function of a random variable.
The following properties are available:
auto | manual
The bandwidth for the KDE kernel, if bandwidth is set to manual and dimension is 1.
The bandwidth for the KDE kernel, if bandwidth is set to manual and dimension is 2. Specify using setIndex syntax.
The bandwidth for the KDE kernel, if bandwidth is set to manual and dimension is 3. Specify using setIndex syntax
The evaluation group to use as input data, when source is set to evaluationgroup.
gaussian | parabolic
Resolution for the y variable, if autores is true and dimension is 2 or 3.
Resolution for the zvariable, if autores is true and dimension is 3.
table | evaluationgroup