Forms predictions from a radial basis function model fitted by `RBFIT`

.

### Option

`PRINT` = strings |
Controls fitted output (`description` , `predictions` ); default `desc` , `pred` |
---|

### Parameters

`X` = pointers |
X-values at which to predict |
---|---|

`PREDICTIONS` = variates |
Predictions |

`SAVE` = pointers |
Details of the fitted model |

### Description

`RBPREDICT`

forms predictions using radial basis function model fitted by `RBFIT`

. Details of the the model and the estimated parameters are supplied using the `SAVE`

parameter. This must have been saved using the `SAVE`

parameter of `RBFIT`

. If this is not set, the output is from the most recent model fitted by `RBFIT`

. The values of the x-variates at which to predict are supplied, in a pointer, using the `X`

parameter. The variates in the pointer must be in exactly the same order as the equivalent variates in the pointer defined for the `X`

parameter in the original `RBFIT`

command.

The output is controlled by the `PRINT`

option, with settings:

`description` |
a description of the model, |
---|---|

`predictions` |
predicted values. |

Option: `PRINT`

.

Parameters: `X`

, `PREDICTIONS`

, `SAVE`

.

### Method

`RBPREDICT`

uses the function `nagdmc_predict_RBF`

from the Numerical Algorithms Group’s library of Data Mining Components (DMCs).

### Action with `RESTRICT`

You can restrict the set of units used for the prediction by applying a restriction to any of the x-variates. If several of these are restricted, they must all be restricted to the same set of units.

### See also

Commands for: Data mining.

### Example

CAPTION 'RBPREDICT example',\ 'Predicting the grape cultivar from 13 wine attributes'; STYLE=meta,plain SPLOAD '%Data%/WinesTrain.gsh'; ISAVE=pData POINTER [VALUES=pData[2...14]] Attributes GROUPS Wine; FACTOR=Cultivar TABULATE [CLASS=Cultivar] Attributes[]; MEANS=Mn[1...13] RBFIT [PRINT=*; RBTYPE=linear; LAMBDA=10] Y=Wine; X=Attributes; \ CENTRES=Mn; SAVE=RBSave "Predictions for wines from unknown cultivars from fitted model" SPLOAD '%Data%/WinesPred.gsh'; ISAVE=TestAttr RBPREDICT X=TestAttr; PREDICTIONS=Fit; SAVE=RBSave "Predicted class is closest integer 1...3" VARIATE Prediction; VALUES = 1 + (Fit > 1.5) + (Fit > 2.5) GROUPS Prediction; Pred_Cultivar TABULATE [CLASS=Pred_Cultivar; PRINT=counts]