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===Purpose=== | ===Purpose=== | ||
Multiple Linear Regression for multivariate Y. | Multiple Linear Regression for multivariate Y. | ||
===Synopsis=== | ===Synopsis=== | ||
:model = mlr(x,y,options) | :model = mlr(x,y,options) | ||
:pred = mlr(x,model,options) | :pred = mlr(x,model,options) | ||
:valid = mlr(x,y,model,options) | :valid = mlr(x,y,model,options) | ||
:mlr % Launches analysis window with MLR as the selected method. | |||
===Description=== | ===Description=== | ||
MLR identifies models of the form Xb = y + e. | MLR identifies models of the form Xb = y + e. | ||
* y = X-block: predictor block (2-way array or DataSet Object) | ====Inputs==== | ||
* y = Y-block: predictor block (2-way array or DataSet Object) | |||
* '''y''' = X-block: predictor block (2-way array or DataSet Object) | |||
* model = scalar, estimate of filtered data. | |||
* pred = structure array with predictions | * '''y''' = Y-block: predictor block (2-way array or DataSet Object) | ||
* valid = structure array with predictions | |||
====Outputs==== | |||
* '''model''' = scalar, estimate of filtered data. | |||
* '''pred''' = structure array with predictions | |||
* '''valid''' = structure array with predictions | |||
===Options === | ===Options === | ||
* display: [ {'off'} | 'on'] Governs screen display to command line. | '''options''' = a structure array with the following fields. | ||
* plots: [ 'none' | {'final'} ] governs level of plotting. | |||
* preprocessing: { [] [] } preprocessing structure (see PREPROCESS). | * '''display''': [ {'off'} | 'on'] Governs screen display to command line. | ||
* blockdetails: [ 'compact' | {'standard'} | 'all' ] | |||
* '''plots''': [ 'none' | {'final'} ] governs level of plotting. | |||
* '''ridge''': [ 0 ] ridge parameter to use in regularizing the inverse. | |||
* '''preprocessing''': { [] [] } preprocessing structure (see PREPROCESS). | |||
* '''blockdetails''': [ 'compact' | {'standard'} | 'all' ] level of detail (predictions, raw residuals, and calibration data) included in the model. | |||
:* ‘Standard’ = the predictions and raw residuals for the X-block as well as the X-block itself are not stored in the model to reduce its size in memory. Specifically, these fields in the model object are left empty: 'model.pred{1}', 'model.detail.res{1}', 'model.detail.data{1}'. | |||
:* ‘Compact’ = for this function, 'compact' is identical to 'standard'. | |||
:* 'All' = keep predictions, raw residuals for both X- & Y-blocks as well as the X- & Y-blocks themselves. | |||
===See Also=== | ===See Also=== | ||
[[analysis]], [[crossval]], [[modelstruct]], [[pcr]], [[pls]], [[preprocess]], [[ridge]] | |||
[[analysis]], [[crossval]], [[ils_esterror]], [[modelstruct]], [[pcr]], [[pls]], [[preprocess]], [[ridge]], [[testrobustness]] | |||
Latest revision as of 10:53, 28 July 2017
Purpose
Multiple Linear Regression for multivariate Y.
Synopsis
- model = mlr(x,y,options)
- pred = mlr(x,model,options)
- valid = mlr(x,y,model,options)
- mlr % Launches analysis window with MLR as the selected method.
Description
MLR identifies models of the form Xb = y + e.
Inputs
- y = X-block: predictor block (2-way array or DataSet Object)
- y = Y-block: predictor block (2-way array or DataSet Object)
Outputs
- model = scalar, estimate of filtered data.
- pred = structure array with predictions
- valid = structure array with predictions
Options
options = a structure array with the following fields.
- display: [ {'off'} | 'on'] Governs screen display to command line.
- plots: [ 'none' | {'final'} ] governs level of plotting.
- ridge: [ 0 ] ridge parameter to use in regularizing the inverse.
- preprocessing: { [] [] } preprocessing structure (see PREPROCESS).
- blockdetails: [ 'compact' | {'standard'} | 'all' ] level of detail (predictions, raw residuals, and calibration data) included in the model.
- ‘Standard’ = the predictions and raw residuals for the X-block as well as the X-block itself are not stored in the model to reduce its size in memory. Specifically, these fields in the model object are left empty: 'model.pred{1}', 'model.detail.res{1}', 'model.detail.data{1}'.
- ‘Compact’ = for this function, 'compact' is identical to 'standard'.
- 'All' = keep predictions, raw residuals for both X- & Y-blocks as well as the X- & Y-blocks themselves.
See Also
analysis, crossval, ils_esterror, modelstruct, pcr, pls, preprocess, ridge, testrobustness