Multiblock

Purpose

Create or apply a multiblock model for joining data.

Synopsis

model = multiblock({m1 d2 m3 d4}, options); %Make multiblock model.

[model, joinedData] = multiblock({m1 d2 m3 d4}, options); %Return model and joined data.

joinedData = multiblock({dd1 dd2 dd3 dd4}, model); %Get new joined data.

model = multiblock(model,postJoinModel); %Add a post join model to multiblock model.

pred = multiblock({x1' x2' x3' x3' x4'}, model); %Get joined data and prediction.

[pred, joinedData] = multiblock({x1' x2' x3' x3' x4'}, model); %Get joined data and prediction.

Description

Multiple block data joining allows for two or more datasets and or models to be joined and modeled. Model fields (e.g., Scores) are extracted into a dataset before joining.

This function joins data in the order it's input.

See multiblocktool for an interface to this function.

Inputs

• mx = Cell array of data and or models.

Outputs

• model = Standard model structure containing the multiblock model.

Options

options = a structure array with the following fields:

• display: [ 'off' | {'on'} ] Governs level of display to command window.

• plots: [ 'none' | {'final'} ] Governs level of plotting.

• waibar: [ 'off' | {'on'} ] Show waitbar.

• filter_defaults: [ 'off' | {'on'} ] Use default scores and Q model fields.

• filter_prompt: [ 'off' | {'on'} ] Prompt for selecting model filter fields. If off then defaults are selected.

• filter: [{}] n x 3 cell array of filter information (see GETMODELOUTPUTS).

• bin_options: structure of options to pass to bin2scale for data concatenation.

• preprocessing: [{}] Preproceessing for each block.

• label_threshold: [.5] Threshold for label matching.

• post_join_model : [] Model to apply after join.

• apply_postjoin_model: [ 'off' | {'on'} ] Apply post join model if available. Set this option to 'off' if only joined data is to desired.

See Also

bin2scale, coadd