| | Name | Description |
|---|
 | Alias | The alias for this tool's toolbox. |
| bandwidth_method | Bandwidth method (in) |
| cell_size | The cell size (a number) or reference to the cell size (a pathname to a raster dataset) to use when creating the coefficient rasters. (In, Optional) |
| coefficient_raster_workspace | Coefficient raster workspace (in, Optional) |
| dependent_field | The numeric field containing values for what you are trying to model. (In, Required) |
| distance | Distance (in, Optional) |
| explanatory_field | A list of fields representing independent explanatory variables in your regression model. (In, Required) |
| in_features | The feature class containing the dependent and independent variables. (In, Required) |
| in_prediction_locations | A feature class containing features representing locations where estimates should be computed. Each feature in this dataset should contain values for all of the explanatory variables specified; the dependent variable for these features will be estimated using the model calibrated for the input feature class data. (In, Optional) |
| kernel_type | Specifies if the kernel is constructed as a fixed distance, or if it is allowed to vary in extent as a function of feature density. (In, Required) |
| number_of_neighbors | An integer reflecting the exact number of neighbors to include in the local bandwidth of the Gaussian kernel whenever the kernel type is ADAPTIVE and the bandwidth method is BANDWIDTH PARAMETER. (In, Optional) |
| out_featureclass | The output feature class to receive dependent variable estimates and residuals. (Out, Required) |
| out_prediction_featureclass | Output prediction feature class (out, Optional) |
| out_regression_rasters | Output regression rasters (out, Optional) |
| out_table | Output table (out, Optional) |
| ParameterInfo | The parameters used by this tool. For internal use only. |
| prediction_explanatory_field | A list of fields representing explanatory variables in the Prediction Locations feature class. These field names should be provided in the same order (a one-to-one correspondance) as those listed for the input feature class Explanatory variables parameter. If no prediction explanatory variables are given, the output prediction feature class will only contain computed coefficient values for each prediction location. (In, Optional) |
| ToolboxDirectory | The directory of this tool's toolbox. |
| ToolboxName | The name of this tool's toolbox. |
| ToolName | The name of this tool. |
| weight_field | The numeric field containing a spatial weighting for individual features. This weight field allows some features to be more important in the model calibration process than others. Primarily useful when the number of samples taken at different locations varies, values for the dependent and independent variables are averaged, and places with more samples are more reliable (should be weighted higher). If you have an average of 25 different samples for one location, but an average of only 2 samples for another location, you can use the number of samples as your weight field so that locations with more samples have a larger influence on model calibration that locations with few samples. (In, Optional) |
